The 5 Most Amazing Computer Vision Techniques to Learn

by September 24, 2020 0 comments

Computer Vision

5 Major computer vision techniques to help a computer extract

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.


Object Detection

The task to identify objects within images usually involves outputting bounding boxes and labels for individual items. It differs from the classification task by using classification and localization to many objects instead of a single dominant object. There are only two classes of object classification. One is object bounding boxes, and other is non-object bounding boxes. For instance, in vehicle detection, one has to identify all vehicles, including two-wheelers and four-wheelers, in a given image with their bounding boxes.

If the Sliding Window technique is taken up such a way we classify localize images, we need to apply a CNN to different crops of the picture. It is because of CNN classifies each crop as object or background. We then need to use CNN to vast numbers of locations and scales that are very computationally expensive.


Object Tracking

Object Tracking indicates the process of following a particular object of interest or multiple items. Traditionally it has applications in video and real-world interactions where observations are made following initial object detection. It can be divided into two categories as per the observation model. One is the generative method, uses a generative model to describe the apparent characteristics. And the discriminative method can be used to separate between the object and the background. Its performance is more robust, and it slowly becomes the principal method in tracking.


Semantic Segmentation

Computer vision is the process of Segmentation that distinguishes whole images into pixel grouping, which can be labelled and classified. Semantic Segmentation tries to understand the role of each pixel in a snap. For instance, if we pick a landscape where we can see people, roads, cars, and tresses, we have to delineate the boundaries of each object. Thus, unlike classification, we need dense pixel-wise predictions from the models.


Instance Segmentation

Instance, Segmentation involves different models of classes like labelling five cars with five different colours. In classification, there is usually an image with a single object as the focus, and the task is to identify what that image is. We see complicated sights with several overlapping objects with different backgrounds. We not only classify these other objects but also detect their boundaries, differences, and relations to one another.

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