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Deep Learning for Computer Vision

Deep Learning for Computer Vision

By : Shanmugamani
3.2 (22)
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Deep Learning for Computer Vision

Deep Learning for Computer Vision

3.2 (22)
By: Shanmugamani

Overview of this book

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
Table of Contents (12 chapters)
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Predicting pixels

Image classification is the task of predicting labels or categories. Object detection is the task of predicting a list of several deep learning-based algorithms with its corresponding bounding box. The bounding box may have objects other than the detected object inside it. In some applications, labeling every pixel to a label is important rather than bounding box which may have multiple objects. Semantic segmentation is the task of predicting pixel-wise labels.

Here is an example of an image and its corresponding semantic segmentation:

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As shown in the image, an input image is predicted with labels for every pixel. The labels could be the sky, tree, person, mountain, and bridge. Rather than assigning a label to the whole image, labels are assigned to each pixel. Semantic segmentation labels pixels independently. You will notice that every...

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