The 5 Most Amazing Computer Vision Techniques to Learn

At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence.

Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let’s look at what are the five primary computer vision techniques.

Image Classification

Image clarification comprises of a variety of challenges, including viewpoint variation, scale variation, intra-class variation, image deformation, image occlusion, illumination conditions, and background clutter.

Computer vision researchers have come up with a data-driven approach to classify images into distinct categories. They provide the computer with a few examples of each image class and expand learning algorithms. It looks at the bars and learns about the visual appearance of each type. In short, they first accumulate a training dataset of labelled images and then feed it to the computer to process the data.

Convolutional Neural Networks (CNNs) is the most famous architecture used for image classification. An average use case for CNNs is where one feeds the network images, and the network categorises the data. CNNs tend to start with an input “scanner” that isn’t intended to parse all the training data at once. For instance, to input an image of 100×100 pixels, one wouldn’t want a layer with 10,000 nodes.

#computer vision #latest news #deep-learning

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The 5 Most Amazing Computer Vision Techniques to Learn

Why you should learn Computer Vision and how you can get started

I. Motivation

In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?

II. What is Computer Vision?

Image for post

Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.

#computer-science #machine-learning #deep-learning #computer-vision #learning #deep learning

Alfredo  Sipes

Alfredo Sipes

1617715380

Why You Should Learn Computer Vision and How You Can Get Started

In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?

#computer-science #machine-learning #deep-learning #computer-vision #learning

Self-Supervised Learning Methods for Computer Vision

Self-supervised Learning is an unsupervised learning method where the supervised learning task is created out of the unlabelled input data.
This task could be as simple as given the upper-half of the image, predict the lower-half of the same image, or given the grayscale version of the colored image, predict the RGB channels of the same image, etc.

#self-supervised-learning #representation-learning #deep-learning #computer-vision #unsupervised-learning

Few Shot Learning — A Case Study (2)

In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.

Please watch the GitHub repository to check out the implementations and keep updated with further experiments.

Introduction to Few-Shot Classification

In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.

In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.

And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.

About Relation Network

The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.

That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.

#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning

The 5 Most Amazing Computer Vision Techniques to Learn

At this point, computer vision is the hottest research field within deep learning. It fits in many academic subjects such as Computer science, Mathematics, Engineering, Biology, and psychology. Computer vision represents a relative understanding of visual environments. Therefore, due to its cross-domain mastery, many scientists believe the field paves the way towards Artificial General Intelligence.

Recent developments in neural networks and deep learning approaches have immensely advanced the performance of state-of-the-art visual recognition systems. Let’s look at what are the five primary computer vision techniques.

Image Classification

Image clarification comprises of a variety of challenges, including viewpoint variation, scale variation, intra-class variation, image deformation, image occlusion, illumination conditions, and background clutter.

Computer vision researchers have come up with a data-driven approach to classify images into distinct categories. They provide the computer with a few examples of each image class and expand learning algorithms. It looks at the bars and learns about the visual appearance of each type. In short, they first accumulate a training dataset of labelled images and then feed it to the computer to process the data.

Convolutional Neural Networks (CNNs) is the most famous architecture used for image classification. An average use case for CNNs is where one feeds the network images, and the network categorises the data. CNNs tend to start with an input “scanner” that isn’t intended to parse all the training data at once. For instance, to input an image of 100×100 pixels, one wouldn’t want a layer with 10,000 nodes.

#computer vision #latest news #deep-learning