Advances In Deep Learning And Computer Vision
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ADVANCES IN DEEP LEARNING AND COMPUTER VISION
Author: Dr. Jagadeesh Kumar
language: en
Publisher: Xoffencerpublication
Release Date: 2024-12-18
Computer Vision (CV) can be defined as “the hypothesis and innovation for building artificial frameworks that acquire data from pictures or multi-dimensional information.” A more straightforward clarification is that computer vision endeavors to take care of similar issues you can unravel with your own one of kind eyes. For instance, in case you're driving and you see a kid run into the street, your mind will rapidly translate the kid in the street in front of you, that it's perilous, and that you ought to quickly brake to abstain from hitting the kid. That is one of the issues self-driving vehicle engineers are presently striving to comprehend by the methods of computer vision. The method requires being competent of realizing object recognition, which can be subdivided into three varieties: object classification, identification, and detection. Object Classification is everywhere you have a little recently learned objects that you need to have the option to perceive in a picture. Characterizing a representation photograph as having individual's face in it is a model object classification, arranged that this photograph contains a face in it. Object Identification is the recognition of a specific instance of an object. For example, being able to identify that there are two faces in an image and that one is John and the other is Sarah is an example of object identification. Object Detection is the ability to identify that there’s an object in an image. This is typically used for things like automatic toll roads where you want to know when a new object has entered the frame so you can take a scan the license plate. Connecting this to the self-driving car problem, if you think to how the human brain would solve this problem, it would have to answer the same questions: In order for the situation to be dangerous, we would have to both identify that there is a child (object) in or approaching the road. Identify that the child in the road is something that we should avoid. You would also want to identify other objects, like trash, soccer ball, bike, etc., where you don’t necessarily need evasive action. In other words, Computer Vision is the field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely remains an unsolved problem based both on the limited understanding of biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world.
Advances in Deep Learning
This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
Advances in Computer Vision
This book presents a remarkable collection of chapters covering a wide range of topics in the areas of Computer Vision, both from theoretical and application perspectives. It gathers the proceedings of the Computer Vision Conference (CVC 2019), held in Las Vegas, USA from May 2 to 3, 2019. The conference attracted a total of 371 submissions from pioneering researchers, scientists, industrial engineers, and students all around the world. These submissions underwent a double-blind peer review process, after which 120 (including 7 poster papers) were selected for inclusion in these proceedings. The book’s goal is to reflect the intellectual breadth and depth of current research on computer vision, from classical to intelligent scope. Accordingly, its respective chapters address state-of-the-art intelligent methods and techniques for solving real-world problems, while also outlining future research directions. Topic areas covered include Machine Vision and Learning, Data Science,Image Processing, Deep Learning, and Computer Vision Applications.