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5 Ways Artificial Intelligence Will Impact Our Future

5 Ways Artificial Intelligence Will Impact Our Future

Artificial Intelligence, or, its more popular acronym AI is no longer a term to be read about in a sci-fi book, it is a reality that is reshaping the world by introducing us to virtual assistants, helping us be more secure by enabling us with futuristic measures. The evolution of AI has been pretty consistent and as we are busy navigating through a pandemic-ridden path towards the future, adapting to the “new normal”, and becoming increasingly reliant on technology, AI assumes a greater significance.

The AI applications which are already being implemented has resulted in a big shift, causing an apprehension that the adoption of AI technology on a larger scale would eventually lead to job cuts, whereas in reality, it would lead to the creation of new jobs across industries. Adoption of AI technology would push the demand for a workforce that is highly skilled, enrolling in an artificial intelligence course in delhi could be a timely decision.

Now that we are about to reach the end of 2020, let us take a look at the possible impacts of AI in the future.

AI will create more jobs

Yes, contrary to the popular apprehension AI would end up creating jobs in the future. However, the adoption of AI to automate tasks means yes, there would be a shift, and a job that does not need special skills will be handled by AI powered tools. Jobs that could be done without error, completed faster, with a higher level of efficiency, in short better than humans could be performed by robots. However, with that being said there would be more specialized job roles, remember AI technology is about the simulation of human intelligence, it is not the intelligence, so there would be humans in charge of carrying out the AI operated areas to monitor the work. Not just that but for developing smarter AI application and implementation there should be a skilled workforce ready, a report by World Economic Forum is indicative of that. From design to maintenance, AI specialists would be in high demand especially the developers. The fourth industrial revolution is here, industries are gearing up to build AI infrastructure, it is time to smell the coffee as by the end of 2022 there will be millions of AI jobs waiting for the right candidates.

Dangerous jobs will be handled by robots

In the future, hazardous works will be handled by robots. Now the robots are already being employed to handle heavy lifting tasks, along with handling the mundane ones that require only repetition and manual labor. Along with automating these tasks, the robot workforce can also handle the situation where human workers might sustain grave injuries. If you have been aware and interested then you already heard about the “SmokeBot”. In the future, it might be the robots who will enter the flaming buildings for assessment before their human counterparts can start their task. Manufacturing plants that deal with toxic elements need robot workers, as humans run a bigger risk when they are exposed to such chemicals. Furthermore, the nuclear plants might have a robot crew that could efficiently handle such tasks. Other areas like pipeline exploration, bomb defusing, conducting rescue operations in hostile terrain should be handled by AI robots.

Smarter healthcare facilities

 AI  implementation which has already begun would continue to transform the healthcare services. With AI being in place CT scan and MRI images could be more precise pointing out even minuscule changes that earlier went undetected. Drug development could also be another area that would see vast improvement and in a post-pandemic world, people would need to be better prepared to fight against such viruses. Real-time detection could prevent many health issues going severe and keeping a track of the health records preventive measures could be taken. One of the most crucial changes that could be revolutionary, is the personalized medication which could only be driven by AI technology. This would completely change the way healthcare functions. Now that we are seeing chat bots for handling sales queries, the future healthcare landscape might be ruled by virtual assistants specifically developed for offering assistance to the patients. There are going to be revolutionary changes in this field in the future, thereby pushing the demand for professionals skilled in deep learning for computer vision with python.

Smarter finance

We are already living in an age where we have robo advisors, this is just the beginning and the growing AI implementation would enable an even smarter analytics system that would minimize the credit risk and would allow banks and other financial institutes to minimize the risk of fraud. Smarter asset management, enhanced customer support are going to be the core features. Smarter ML algorithms would detect any and every oddity in behavior or in transactions and would help prevent any kind of fraud from happening. With analytics being in place it would be easier to predict the future trends and thereby being more efficient in servicing the customers. The introduction of personalized services is going to be another key feature to look out for.

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Retail space gets a boost

The retailers are now aiming to implement AI applications to  offer smart shopping solutions to the future buyers. Along with coming up with personalized shopping suggestions for the customers and showing them suggestions based on their shopping pattern, the retailers would also be using the AI to predict the future trends and work accordingly. Not just that but they can easily maintain the supply and demand balance with the help of AI solutions and stock up items that are going to be in demand instead of items that would not be trendy. The smarter assistants would ensure that the customer queries are being handled and they could also be helping them with shopping by providing suggestions and information. From smart marketing to smarter delivery, the future of retail would be dominated by AI as the investment in this space is gradually going up.

The future is definitely going to be impacted by the AI technology in more ways than one. So, be future ready and get yourself upskilled as it is the need of the hour, stay updated and develop the skill to move towards the AI future with confidence.


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How Computer Vision Technology Is Empowering Different Industries?

How Computer Vision Technology Is Empowering Different Industries?

Computer vision is an advanced branch of AI that revolves around the concept of object recognition and smart classification of objects in images or, videos. This is indeed a revolutionary innovation that aims to simulate the way human vision is trained to identify and classify objects. Studying deep learning for computer vision course can help gain specialized knowledge in this field. The growing application of computer vision across industries is now opening up multiple career avenues.

The application of computer vision is changing different industries:

Healthcare

In healthcare computer vision technology is adding efficiency to medical imaging procedures such as MRI. Detecting even the smallest of oddity is now possible which ensures accurate diagnosis. In departments like radiology, cardiology,  computer vision techniques are gradually being adopted. Not just that, during surgical procedures too computer vision can offer cutting edge solutions. A case in point here would be Gauss Surgical’s blood monitoring system that analyzes the amount of blood loss during surgery.

Automotive

The self-driving cars are no longer a sci-fi theme, but, a hardcore reality, computer vision technology analyzes the road conditions, detects humans crossing the road, objects as well as road signs and lane changes. There are advanced systems that aim to prevent accidents that run on the same technology and could also signal if the driver behind the wheel is not awake, thus saving lives in real-time.

Manufacturing

The manufacturing industry is reaping benefits of computer vision technology in so many ways. Using computer vision the equipment condition can be monitored and measures could be taken accordingly to prevent untimely breakdown. Maintaining production quality also gets easier with computer vision application as even the smallest defect in a product or, on the packaging could be detected which might get missed by human eyes. Not just that but, even the labels could be efficiently screened to detect printing errors.

Agriculture

In the field of agriculture, computer vision technology is helping maintain quality and adding efficiency. Using drones to monitor the crops is getting easier, not just that but computer vision technology is helping farmers separate crops as per quality and decide which crop could be stored for a long time. Livestock monitoring is another job that could be efficiently handled using computer vision technology.  However, one significant application is perhaps using computer vision to detect crops that are infected and need pesticide.

Military applications

Computer vision can add an edge to modern warfare, its adoption in the military surely indicate that. Autonomous vehicles powered with computer vision techniques can save so many lives, especially when deployed during battles. Not just that, but detecting landmines, or, enemy, both high-risk yet extremely important operations can be handled successfully by adopting computer vision techniques. Image sensors could deliver the intelligence the military think-tank needs to take timely decisions.

Surveillance

Surveillance is a highly crucial area that could immensely benefit from computer vision applications. In shops preventing crimes like shoplifting could become easier, as the cameras could easily detect any kind of suspicious behavior and activity going on in the shop premises. Another factor to consider here would be the application of facial recognition to identify miscreants from videos.

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Computer vision technology is changing the way we look at our world, and with further research, there would be smarter products on the market that can truly transform our lives by allowing us to be more efficient. For someone aspiring to make a career in this promising domain should undergo computer vision course python training.


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Funnel Activation for Visual Recognition: A New Research Breakthrough

Funnel Activation for Visual Recognition: A New Research Breakthrough

The latest research work in the field of image recognition led to the development of a new activation function for visual recognition tasks, namely Funnel activation(FReLU). In this research ReLU and PReLU are extended to a 2D activation by adding a negligible overhead of spatial condition. Experiments on ImageNet, COCO detection, and semantic segmentation tasks are conducted to measure the performance of FReLU.

CNNs have shown advanced performances in many visual recognition tasks, such as image classification, object detection, and semantic segmentation.  In a CNN framework, basically two major kind of layers play crucial roles, the convolution layer and the non-linear activation layer. Both the convolution layers and activation layers perform distinct functions, however, in both layers there are challenges regarding capturing the spatial dependency. However, despite advancements achieved by complex convolutions, improving the performance of visual tasks is still challenging which results in Rectified Linear Unit (ReLU) being the most widely used function till date.

The research focused on two distinct queries

  1. Could regular convolutions achieve similar accuracy, to grasp the challenging complex images?
    2. Could we design an activation specifically for visual tasks?

1. Effectiveness and generalization performance

In a bid to find answers to these questions, researchers identified spatially insensitiveness in activations to be the main impending factor that prevent visual tasks from improving further.

To address this issue they proposed to find a new visual activation task that could be effective in removing this obstacle and be a better alternative to previous activation approaches.

How other activations work

Taking a look at other activations such as Scalar activations, Contextual conditional activations helps in understanding the context better.

Scalar activations basically are concerned with single input and output which could be represented in form of y = f(x). ReLU or, the Rectified Linear Unit is a widely used activation that is used for various tasks and could be represented as y = max(x, 0).

Contextual conditional activations work on the basis of many-to-one function. In this process neurons that are conditioned on contextual information are activated.

Spatial dependency modeling

In order to accumulate the various ranges of spatial dependences, some approaches utilize various shapes of convolution kernels which leads to lesser efficiency. In other methods like STN, spatial transformations are adaptively used for refining short-range dependencies for the dense vision tasks.

FReLU differs from all other methods in the sense that it performs better without involving complex convolutions. FReLU addresses the issues and solves with a higher level of efficiency.

Receptive field: How FReLU differs from other methods regarding the Receptive field

The size as well as the region of the receptive field play a crucial role in vision recognition tasks. The pixel contribution can be unequal. In order to implement the adaptive receptive field and for a better performance, many methods resort to complex convolutions. FReLU differs from such methods in the way that it achieves the same goal with regular convolutions in a more simple yet highly efficient manner.

Funnel Activation: how funnel activation works

FReLU being conceptually simple is designed for visual tasks. The research further delves into reviewing the ReLU activation and PReLU which is an advanced variant of ReLU, moving on to the key elements of FReLU the funnel condition and the pixel-wise modeling capacity, both of which are not found in ReLU or, in any of its variants.

2. Funnel activation

Funnel condition

Here the same max(·) is adopted as the simple non-linear function, when it comes to the condition part it gets extended to be a 2D condition which is dependent on the spatial context for individual pixel.  For the implementation of the spatial condition, Parametric Pooling Window is used for creating dependency.

Pixel-wise modeling capacity

 Due to the funnel condition the network is capable of generating spatial conditions in the non-linear activations for each pixel. This differs from usual methods where spatial dependency is created in the convolution layer and non-linear transformations are conducted separately. This model achieves a pixel-wise modeling capacity thereby extraction of spatial structure of objects could be addressed naturally.

Experiments

Evaluation of the activation is tested via experiments on ImageNet 2012 classification dataset[9,37].The evaluation is done in stages starting with  different sizes of ResNet. Comparisons with scalar activations is done on ResNets with varying depths, followed by Comparison on light-weight CNNs. An object detection experiment is done to evaluate the generalization performance on various tasks on COCO dataset containing 80 object categories. Further comparison is also done on semantic segmentation task in CityScape dataset. Difference of the images could be perceived through the CityScape images.

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4. Visualization of semantic segmentation

Funnel activation: ablation studies

The scope of the visual activation is tested further via ablation studies where each component of the activation namely 1) funnel condition, and 2)max(·) non-linearity are individually examined. The three parts of the investigation are as follows Ablation on the spatial condition, Ablation on the non-linearity, Ablation on the window size

Compatibility with Existing Methods

Before the new activation could be adopted into the convolutional networks, layers and stages need to be decided, the compatibility with other existing approaches such as SENet also was tested. The process took place in stages as follows

Compatibility with different convolution layers

Compatibility with different stages

Compatibility with SENet

Conclusion: Post all the investigations done to test out the compatibility of FReLU on different levels, it could be stated that this funnel activation is simple yet highly effective and specifically developed for visual tasks.  Its pixel-wise modeling capacity is able to grasp even complex layouts easily. But further research work could be done to expand its scope as it definitely has huge potential.

To get in-depth knowledge regarding the various stages of the research work on Funnel Activation for Visual Recognition, check https://arxiv.org/abs/2007.11824.

 


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5 Most Powerful Computer Vision Techniques in use

5 Most Powerful Computer Vision Techniques in use

Computer Vision is one of the most revolutionary and advanced technologies that deep learning has birthed. It is the computer’s ability to classify and recognize objects in pictures and even videos like the human eye does. There are five main techniques of computer vision that we ought to know about for their amazing technological prowess and ability to ‘see’ and perceive surroundings like we do. Let us see what they are.

Image Classification

The main concern around image classification is categorization of images based on viewpoint variation, image deformation and occlusion, illumination and background clutter. Measuring the accuracy of the description of an image becomes a difficult task because of these factors. Researchers have come up with a novel way to solve the problem.

They use a data driven approach to classify the image. Instead of classifying what each image looks like in code, they feed the computer system with many image classes and then develop algorithms that look at these classes and “learn” about the visual appearance of each class. The most popular system used for image classification is Convolutional Neural Networks (CNNs).

Object Detection

Object detection is, simply put, defining objects within images by outputting bounding boxes and labels or tags for individual objects. This differs from image classification in that it is applied to several objects all at once rather than identifying just one dominant object in an image. Now applying CNNs to this technique will be computationally expensive.

So the technique used for object detection is region-based CNNs of R-CNNs. In this technique, first an image is scanned for objects using an algorithm that generates hundreds of region proposals. Then a CNN is run on each region proposal and only then is each object in each region proposal classified. It is like surveying and labelling the items in a warehouse of a store.

Object Tracking

Object tracking refers to the process of tracking or following a specific object like a car or a person in a given scene in videos. This technique is important for autonomous driving systems in self-driving cars. Object detection can be divided into two main categories – generative method and discriminative method.

The first method uses the generative model to describe the evident characteristics of objects. The second method is used to distinguish between object and background and foreground.

Semantic Segmentation

Crucial to computer vision is the process of segmentation wherein whole images are divided or segmented into pixelgroups that are subsequently labeled and classified.

The science tries to understand the role of each pixel in the image. So, for instance, besides recognizing and detecting a tree in an image, its boundaries are depicted as well. CNNs are best used for this technique.

Instance Segmentation

This method builds on semantic segmentation in that instead of classifying just one single dominant object in an image, it labels multiple images with different colours.

When we see complicated images with multiple overlapping objects and different backgrounds, we apply instance segmentation to it. This is done to generate pixel studies of each object, their boundaries and backdrops.

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Conclusion

Besides these techniques to study and analyse and interpret images or a series of images, there are many more complex techniques that we have not delved into in this blog. However, for more on computer vision, you can peruse the DexLab Analytics website. DexLab Analytics is a premiere Deep Learning training institute In Delhi.

 


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Deep Learning — Applications and Techniques

Deep Learning — Applications and Techniques

Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. While classic machine-learning algorithms solved many problems, they are poor at dealing with soft data such as images, video, sound files, and unstructured text.

Deep-learning algorithms solve the same problem using deep neural networks, a type of software architecture inspired by the human brain (though neural networks are different from biological neurons). Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

The data is inputted into the first layer of the neural network. In the first layer individual neurons pass the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced. Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings.

Deep Learning Use Case Examples

Robotics

Many of the recent developments in robotics have been driven by advances in AI and deep learning. Developments in AI mean we can expect the robots of the future to increasingly be used as human assistants. They will not only be used to understand and answer questions, as some are used today. They will also be able to act on voice commands and gestures, even anticipate a worker’s next move. Today, collaborative robots already work alongside humans, with humans and robots each performing separate tasks that are best suited to their strengths.

Agriculture

AI has the potential to revolutionize farming. Today, deep learning enables farmers to deploy equipment that can see and differentiate between crop plants and weeds. This capability allows weeding machines to selectively spray herbicides on weeds and leave other plants untouched. Farming machines that use deep learning–enabled computer vision can even optimize individual plants in a field by selectively spraying herbicides, fertilizers, fungicides and insecticides.

Medical Imaging and Healthcare

Deep learning has been particularly effective in medical imaging, due to the availability of high-quality data and the ability of convolutional neural networks to classify images. Several vendors have already received FDA approval for deep learning algorithms for diagnostic purposes, including image analysis for oncology and retina diseases. Deep learning is also making significant inroads into improving healthcare quality by predicting medical events from electronic health record data.  Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer.

Here are some basic techniques that allow deep learning to solve a variety of problems.

Fully Connected Neural Networks

Fully Connected Feed forward Neural Networks are the standard network architecture used in most basic neural network applications.

Deep Learning — Applications and Techniques

In a fully connected layer each neuron is connected to every neuron in the previous layer, and each connection has its own weight. This is a totally general purpose connection pattern and makes no assumptions about the features in the data. It’s also very expensive in terms of memory (weights) and computation (connections).

Deep Learning — Applications and Techniques

Each neuron in a neural network contains an activation function that changes the output of a neuron given its input. These activation functions are:

  • Linear function: – it is a straight line that essentially multiplies the input by a constant value.
  •  Sigmoid function: – it is an S-shaped curve ranging from 0 to 1.
  • Hyperbolic tangent (tanH) function: – it is an S-shaped curve ranging from -1 to +1
  • Rectified linear unit (ReLU) function: – it is a piecewise function that outputs a 0 if the input is less than a certain value or linear multiple if the input is greater than a certain value.

Each type of activation function has pros and cons, so we use them in various layers in a deep neural network based on the problem. Non-linearity is what allows deep neural networks to model complex functions.

Convolutional Neural Networks

Convolutional Neural Networks (CNN) is a type of deep neural network architecture designed for specific tasks like image classification. CNNs were inspired by the organization of neurons in the visual cortex of the animal brain. As a result, they provide some very interesting features that are useful for processing certain types of data like images, audio and video.

Deep Learning — Applications and Techniques

Mainly three main types of layers are used to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). We will stack these layers to form a full ConvNet architecture.  A simple ConvNet for CIFAR-10 classification could have the above architecture [INPUT – CONV – RELU – POOL – FC].

  • INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
  • CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
  • RELU layer will apply an elementwise activation function, such as the max(0,x)max(0,x)thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
  • POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
  • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.

In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and others don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.

Convolution is a technique that allows us to extract visual features from an image in small chunks. Each neuron in a convolution layer is responsible for a small cluster of neurons in the receding layer. CNNs work well for a variety of tasks including image recognition, image processing, image segmentation, video analysis, and natural language processing.

Recurrent Neural Network

The recurrent neural network (RNN), unlike feed forward neural networks, can operate effectively on sequences of data with variable input length.

The idea behind RNNs is to make use of sequential information. In a traditional neural network we assume that all inputs (and outputs) are independent of each other. But for many tasks that is a very bad idea. If you want to predict the next word in a sentence you better know which words came before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. Another way to think about RNNs is that they have a “memory” which captures information about what has been calculated so far. This is essentially like giving a neural network a short-term memory. This feature makes RNNs very effective for working with sequences of data that occur over time, For example, the time-series data, like changes in stock prices, a sequence of characters, like a stream of characters being typed into a mobile phone.

The two variants on the basic RNN architecture that help solve a common problem with training RNNs are Gated RNNs, and Long Short-Term Memory RNNs (LSTMs). Both of these variants use a form of memory to help make predictions in sequences over time. The main difference between a Gated RNN and an LSTM is that the Gated RNN has two gates to control its memory: an Update gate and a Reset gate, while an LSTM has three gates: an Input gate, an Output gate, and a Forget gate.

RNNs work well for applications that involve a sequence of data that change over time. These applications include natural language processing, speech recognition, language translation, image captioning and conversation modeling.

Conclusion

So this article was about various Deep Learning techniques. Each technique is useful in its own way and is put to practical use in various applications daily. Although deep learning is currently the most advanced artificial intelligence technique, it is not the AI industry’s final destination. The evolution of deep learning and neural networks might give us totally new architectures. Which is why more and more institutes are offering courses on AI and Deep Learning across the world and in India as well. One of the best and most competent artificial intelligence certification in Delhi NCR is DexLab Analytics. It offers an array of courses worth exploring.


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Deep Learning and Computer Vision – A study – Part II

Deep Learning and Computer Vision – A study – Part II

In the first series of this article we have seen what is computer vision and a brief review of its applications. You can read the first part of this article here. We have also seen the contribution of deep learning in computer vision. Especially we focused on Image Classification and deep learning architecture which is used in Image Classification. In this series we will focus on other applications including Image Localization, Object Detection and Image Segmentation. We will also walk through the required deep learning architecture used for above applications.

Image classification with Localization

Similar to classification, localization finds the location of a single object inside the image. Localization can be used for lots of useful real-life problems. For example, smart cropping (knowing where to crop images based on where the object is located), or even regular object extraction for further processing using different techniques. It can be combined with classification for not only locating the object but categorizing it into one of many possible categories.

A classical dataset for image classification with localization is the PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g. VOC 2012). These are datasets used in computer vision challenges over many years.

Object detection

Iterating over the problem of localization plus classification we end up with the need for detecting and classifying multiple objects at the same time. Object detection is the problem of finding and classifying a variable number of objects on an image. The important difference is the “variable” part. In contrast with problems like classification, the output of object detection is variable in length, since the number of objects detected may change from image to image.

The PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g. VOC 2012), is a common dataset for object detection.

Deep learning for Image Localization and Object Detection

There is nothing hardcore about the architectures which we are going to discuss. What we are going to discuss are some clever ideas to make the system intolerant to the number of outputs and to reduce its computation cost. So, we do not know the exact number of objects in our image and we want to classify all of them and draw a bounding box around them. That means that the number of coordinates that the model should output is not constant. If the image has 2 objects, we need 8 coordinates. If it has 4 objects, we want 16. So how we build such a model?

One key idea to traditional computer vision is regions proposal. We generate a set of windows that are likely to contain an object using classic CV algorithms, like edge and shape detection and we apply only these windows (or regions of interests) to the CNN. To learn more about how regions are proposed, we introduce a new architecture called RCNN.

R-CNN

Given an image with multiple objects, we generate some regions of interests using a proposal method (in RCNN’s case this method is called selective search) and wrap the regions into a fixed size. We forward each region to Convolutional Neural Network (such as AlexNet), which will use an SVM to make a classification decision for each one and predicts a regression for each bounding box. This prediction comes as a correction of the region proposed, which may be in the right position but not at the exact size and orientation.

Although the model produces good results, it suffers from a major issue. It is quite slow and computationally expensive. Imagine that in an average case, we produce 2000 regions, which we need to store in disk, and we forward each one of them into the CNN for multiple passes until it is trained. To fix some of these problems, an improvement of the model comes in play called ‘Fast-RCNN’

Fast RCNN

The idea is straightforward. Instead of passing all regions into the convolutional layer one by one, we pass the entire image once and produce a feature map. Then we take the region proposals as before (using some external method) and sort of project them onto the feature map. Now we have the regions in the feature map instead of the original image and we can forward them in some fully connected layers to output the classification decision and the bounding box correction.

Note that the projection of regions proposal is implemented using a special layer (ROI layer), which is essentially a type of max-pooling with a pool size dependent on the input, so that the output always has the same size.

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Faster RCNN

And we can take this a step further. Using the produced feature maps from the convolutional layer, we infer regions proposal using a Region Proposal network rather than relying on an external system. Once we have those proposals, the remaining procedure is the same as Fast-RCNN (forward to ROI layer, classify using SVM and predict the bounding box). The tricky part is how to train the whole model as we have multiple tasks that need to be addressed:

  • The region proposal network should decide for each region if it contains an object or not.
  • It needs to produce the bounding box coordinates.
  • The entire model should classify the objects to categories.
  • And again predict the bounding box offsets.

As the name suggests, Faster RCNN turns out to be much faster than the previous models and is the one preferred in most real-world applications.

Localization and object detection is a super active and interesting area of research due to the high emergency of real world applications that require excellent performance in computer vision tasks (self-driving cars, robotics). Companies and universities come up with new ideas on how to improve the accuracy on regular basis.

There is another class of models for localization and object detection, called single shot detectors, which have become very popular in the last few years because they are even faster and require less computational cost in general. Sure, they are less accurate, but they are ideal for embedded systems and similar power-hungry applications.

Object segmentation

Going one step further from object detection we would want to not only find objects inside an image, but find a pixel by pixel mask of each of the detected objects. We refer to this problem as instance or object segmentation.

Semantic Segmentation is the process of assigning a label to every pixel in the image. This is in stark contrast to classification, where a single label is assigned to the entire picture. Semantic segmentation treats multiple objects of the same class as a single entity. On the other hand, instance segmentation treats multiple objects of the same class as distinct individual objects (or instances). Typically, instance segmentation is harder than semantic segmentation.

In order to perform semantic segmentation, a higher level understanding of the image is required. The algorithm should figure out the objects present and also the pixels which correspond to the object. Semantic segmentation is one of the essential tasks for complete scene understanding. This can be used in analysis of medical images and satellite images. Again, the VOC 2012 and MS COCO datasets can be used for object segmentation.

Deep Learning for Image Segmentation

Modern image segmentation techniques are powered by deep learning technology. Here are several deep learning architectures used for segmentation.

Convolutional Neural Networks (CNNs) 

Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels. The CNN cannot process the whole image at once. It scans the image, looking at a small “filter” of several pixels each time until it has mapped the entire image. To learn more see our in-depth guide about Convolutional Neural Networks.

Fully Convolutional Networks (FCNs)

Traditional CNNs have fully-connected layers, which can’t manage different input sizes. FCNs use convolutional layers to process varying input sizes and can work faster. The final output layer has a large receptive field and corresponds to the height and width of the image, while the number of channels corresponds to the number of classes. The convolutional layers classify every pixel to determine the context of the image, including the location of objects.

DeepLab

One main motivation for DeepLab is to perform image segmentation while helping control signal decimation—reducing the number of samples and the amount of data that the network must process. Another motivation is to enable multi-scale contextual feature learning—aggregating features from images at different scales. DeepLab uses an ImageNet pre-trained residual neural network (ResNet) for feature extraction.   DeepLab uses atrous (dilated) convolutions instead of regular convolutions. The varying dilation rates of each convolution enable the ResNet block to capture multi-scale contextual information. DeepLab comprises three components:

  • Atrous convolutions—with a factor that expands or contracts the convolutional filter’s field of view.
  • ResNet—a deep convolutional network (DCNN) from Microsoft. It provides a framework that enables training thousands of layers while maintaining performance. The powerful representational ability of ResNet boosts computer vision applications like object detection and face recognition.
  • Atrous spatial pyramid pooling (ASPP)—provides multi-scale information. It uses a set of atrous convolutions with varying dilation rates to capture long-range context. ASPP also uses global average pooling (GAP) to incorporate image-level features and add global context information.

SegNet neural network

An architecture based on deep encoders and decoders is also known as semantic pixel-wise segmentation. It involves encoding the input image into low dimensions and then recovering it with orientation invariance capabilities in the decoder. This generates a segmented image at the decoder end.

Conclusion

In this post we have discussed some applications of computer vision including Image Localization, Object Detection and Image Segmentation. We then discussed required deep learning architectures which are used for the above applications.


.

Commercial Uses of Deep Learning

Commercial Uses of Deep Learning

Deep Learning has its limitations, scientists argue.

“We have machines that learn in a very narrow way,” Yoshua Bengio, deep learning pioneer, said in his keynote address at NeurIPS in December, 2019. “They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.”

Unarguably, deep learning is an imperfect framework of intelligence. It does not think abstractedly, does not comprehend causation and struggles with out-of-distribution generalization. For a deeper understanding of its limitations, this brilliant paper on the science and its shortcomings is available on the internet.

However, despite numerous shortcomings, the commercial uses of deep learning are only just being mined and its capabilities to automate and transform industries still abound. AI and deep learning capabilities, as developed as they are today, are sufficiently mature to spearhead transformation, innovation, and value creation across industries like agriculture, healthcare and construction. “For the most part, these transformative opportunities have not yet been operationalized at scale.”

Radiology

For instance, in the radiology industry, something as extreme and point blank as this was declared in 2016 by AI luminary Geoff Hinton – “It’s quite obvious that we should stop training radiologists now.” Hinton’s comments drew worked up reactions in the medical community but his statement was based on strong data which showed neural networks can identify medical conditions from X-rays with better accuracy than human radiologists can.

Yet, years after Hinton foresaw the removal of the need of human radiologists from the medical science field, no clinic in the world has deployed AI-driven radiology tools at scale. Only a few health organizations have begun using it in limited settings. But more and more organizations are slowly adopting deep learning in radiology.

Off Road Autonomous Vehicles

In another instance, the off-road autonomous vehicle industry is seeing a slow move towards tapping the massive unrealized commercial potential of AI. Construction, agriculture and mining are some of the largest industries in the world. If these industries start deploying deep learning powered automated machines to do work that human hands are trained to do, a massive pool of cost, productivity and safety benefits could be tapped.

Energy

In the field of energy, leading players like BP are using deep learning to innovate and transform work conditions on site. “It uses technology to drive new levels of performance, improve the use of resources and safety and reliability of oil and gas production and refining. From sensors that relay the conditions at each site to using AI technology to improve operations, BP puts data at the fingertips of engineers, scientists and decision-makers to help drive high performance.”

Retail

Burberry, a luxury fashion brand, uses big data and AI to fight counterfeit products. It is also trying to enhance sales and customer relationships by initiating a loyalty program that creates data to help personalize the shopping experience for each customer.

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Social Media

Both Twitter and Facebook are tapping into structured and unstructured sets of big data for understanding user behavior and using deep learning to check for communal or racist comments and user preferences.

Deep Learning and Artificial Intelligence is the future and it is here to stay. No wonder then, that more and more professionals are opting to train themselves through deep learning courses. DexLab Analytics is one of the best Deep Learning training institutes in Delhi. Do go through its website for more details.

 

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Computer Vision and Image Classification -A study

Computer Vision and Image Classification -A study

Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. With computer vision, our computer can extract, analyze and understand useful information from an individual image or a sequence of images. Computer vision is a field of artificial intelligence that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide the appropriate output.

Initially computer vision only worked in limited capacity but due to advance innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.

The Contribution of Deep Learning in Computer Vision

While there are still significant obstacles in the path of human-quality computer vision, Deep Learning systems have made significant progress in dealing with some of the relevant sub-tasks. The reason for this success is partly based on the additional responsibility assigned to deep learning systems.

It is reasonable to say that the biggest difference with deep learning systems is that they no longer need to be programmed to specifically look for features. Rather than searching for specific features by way of a carefully programmed algorithm, the neural networks inside deep learning systems are trained. For example, if cars in an image keep being misclassified as motorcycles then you don’t fine-tune parameters or re-write the algorithm. Instead, you continue training until the system gets it right.

With the increased computational power offered by modern-day deep learning systems, there is steady and noticeable progress towards the point where a computer will be able to recognize and react to everything that it sees.

Application of Computer Vision

The field of Computer Vision is too expansive to cover in depth.  The techniques of computer vision can help a computer to extract, analyze, and understand useful information from a single or a sequence of images. There are many advanced techniques like style transfer, colorization, action recognition, 3D objects, human pose estimation, and much more but in this article we will only focus on the commonly used techniques of computer vision. These techniques are: –

  • Image Classification
  • Image Classification with Localization
  • Object Segmentation
  • Object Detection

So in this article we will go through all the above techniques of computer vision and we will also see how deep learning is used for the various techniques of computer vision in detail. To avoid confusion we will distribute this article in a series of multiple blogs. In first blog we will see the first technique of computer vision which is Image Classification and we will also explore that how deep learning is used in Image Classification.

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Image Classification

Image classification is the process of predicting a specific class, or label, for something that is defined by a set of data points. Image classification is a subset of the classification problem, where an entire image is assigned a label. Perhaps a picture will be classified as a daytime or nighttime shot. Or, in a similar way, images of cars and motorcycles will be automatically placed into their own groups.

There are countless categories, or classes, in which a specific image can be classified. Consider a manual process where images are compared and similar ones are grouped according to like-characteristics, but without necessarily knowing in advance what you are looking for. Obviously, this is an onerous task. To make it even more so, assume that the set of images numbers in the hundreds of thousands. It becomes readily apparent that an automatic system is needed in order to do this quickly and efficiently.

There are many image classification tasks that involve photographs of objects. Two popular examples include the CIFAR-10 and CIFAR-100 datasets that have photographs to be classified into 10 and 100 classes respectively.

Deep learning for Image Classification

The deep learning architecture for image classification generally includes convolutional layers, making it a convolutional neural network (CNN). A typical use case for CNNs is where you feed the network images and the network classifies the data. CNNs tend to start with an input “scanner” which isn’t intended to parse all the training data at once. For example, to input an image of 100 x 100 pixels, you wouldn’t want a layer with 10,000 nodes.

Rather, you create a scanning input layer of say 10 x 10 which you feed the first 10 x 10 pixels of the image. Once you passed that input, you feed it the next 10 x 10 pixels by moving the scanner one pixel to the right. This technique is known as sliding windows.

Following Layers are used to build Convolutional Neural Networks:

  • INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
  • CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
  • RELU layer will apply an element wise activation function, such as the max(0,x)max(0,x)thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
  • POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
  • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.

Output of the Model History

In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and other don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.

Conclusion

The above content focuses on image classification only and the architecture of deep learning used for it. But there is more to computer vision than just classification task. The detection, segmentation and localization of classified objects are equally important. We will see these in next blog.

 

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A Handbook of the Basic Data Types in Python 3: Strings

A Handbook of the Basic Data Types in Python 3: Strings

In general, a data type defines the format, sets the upper & lower bounds of the data so that a program could use it appropriately. Data types are the classification or categorization of data items which describes the character of a variable. The most used data types are numeric, non-numeric and Boolean (true/false).

Python has the following standard Data Types:

  • Booleans
  • Numbers
  • String
  • List
  • Tuple
  • Set
  • Dictionary

Mutable and Immutable Objects

Data objects of the above types are stored in a computer’s memory for processing. Some of these values can be modified during processing, but the contents of the others can’t be altered once they are created in the memory.

Number values, strings, and tuple are immutable, which means their contents can’t be altered after creation.

On the other hand, the collection of items in a List or Dictionary object can be modified. It is possible to add, delete, insert, and rearrange items in a list or dictionary. Hence, they are mutable objects.

Booleans

A Boolean is such a data type that almost every programming language has, and so does Python. Boolean in Python can have two values – True or False. These values can be used for assigning and comparison.

Numbers

Numbers are one of the most prominent Python data types. In Numbers, there are mainly 3 types which include Integer, Float, and Complex.

String

A sequence of one or more characters enclosed within either single quotes ‘or double quotes” is considered as String in Python. Any letter, a number or a symbol could be a part of the string. Multi-line strings can be represented using triple quotes,”’ or “””.

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List

Python list is an array-like construct which stores a heterogeneous collection of items of varied data typed objects in an ordered sequence. It is very flexible and does not have a fixed size. The Index in a list begins with a zero in Python.

Tuple

A tuple is a sequence of Python objects separated by commas. Tuples are immutable, which means tuples once created cannot be modified. Tuples are defined using parentheses ().

Set

A set is an unordered collection of items. Set is defined by values separated by a comma inside braces { }. Amongst all the Python data types, the set is one which supports mathematical operations like union, intersection, symmetric difference etc. Since the set derives its implementation from the “Set” in mathematics, so it can’t have multiple occurrences of the same element.

Dictionary

A dictionary in Python is an unordered collection of key-value pairs. It’s a built-in mapping type in Python where keys map to values. These key-value pairs provide an intuitive way to store data. To retrieve the value we must know the key. In Python, dictionaries are defined within braces {}.

This article is about one specific data type, which is a string. The String is a sequence of characters enclosed in single (”) or double quotation (“”) marks.

Here are examples of creating strings in Python.

Counting Number of Characters Using LEN () Function

The LEN () built-in function counts the number of characters in the string.

Creating Empty Strings

Although variables S3 and S4 do not contain any characters they are still valid strings. S3 and S4 both represent empty strings here.

We can verify this fact by using the type () function.

String Concatenation

String concatenation means joining one or more strings together. To concatenate strings in Python we use + operator.

String Repetition Operator (*)

Just like in numbers, * operator can also be used with strings. When used with strings * operator repeats the string n number of times. Its general format is: 1 string * n,

where n is a number of type int.

Membership Operators – in and not in

The in or not in operators are used to check the existence of a string inside another string. For example:

Indexing in a String

In Python, characters in a string are stored in a sequence. We can access individual characters inside a string by using an index.

An index refers to the position of a character inside a string. In Python, strings are 0 indexed. This means that the first character is at index 0; the second character is at index 1 and so on. The index position of the last character is one less than the length of the string.

To access the individual characters inside a string we type the name of the variable, followed by the index number of the character inside the square brackets [].

Instead of manually counting the index position of the last character in the string, we can use the LEN () function to calculate the string and then subtract 1 from it to get the index position of the last character.

We can also use negative indexes. A negative index allows us to access characters from the end of the string. Negative index starts from -1, so the index position of the last character is -1, for the second last character it is -2 and so on.

Slicing Strings

String slicing allows us to get a slice of characters from the string. To get a slice of string we use the slicing operator. Its syntax is:

str_name[start_index:end_index]

str_name[start_index:end_index] returns a slice of string starting from index start_index to the end_index. The character at the end_index will not be included in the slice. If end_index is greater than the length of the string then the slice operator returns a slice of string starting from start_index to the end of the string. The start_index and end_index are optional. If start_index is not specified then slicing begins at the beginning of the string and if end_index is not specified then it goes on to the end of the string. For example:

Apart from these functionalities, there are so many built-in methods for strings which make the string as the useful data type of Python. Some of the common built-in methods are as follows: –

capitalize ()

Capitalizes the first letter of the string

join (seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

lower ()

Converts all the letters in a string that are in uppercase to lowercase.

max (str)

Returns the max alphabetical character from the string str.

min (str)

Returns the min alphabetical character from the string str.

replace (old, new [, max])

Replaces all the occurrences of old in a string with new or at most max occurrences if max gave.

 split (str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.

upper()

Converts lowercase letters in a string to uppercase.

Conclusion

So in this article, firstly, we have seen a brief introduction of all the data types of python. Later in this article, we focused on the strings. We have seen several Python operations on strings as well as the most common useful built-in methods of strings.

Python is the language of the present age, wherein almost every field there is a need for Python. For example, Python for data analysisMachine Learning Using Python has been easy and comprehensible than they were ever before. Thus, if you are also interested in Python and looking for promising courses Computer Vision Course PythonRetail Analytics using PythonNeural Network Machine Learning Python, then get in touch with Dexlab Analytics now and step into the world of opportunities!

 

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