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Titlebook: Building Computer Vision Applications Using Artificial Neural Networks; With Step-by-Step Ex Shamshad Ansari Book 20201st edition Shamshad

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發(fā)表于 2025-3-21 18:18:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Building Computer Vision Applications Using Artificial Neural Networks
期刊簡(jiǎn)稱(chēng)With Step-by-Step Ex
影響因子2023Shamshad Ansari
視頻videohttp://file.papertrans.cn/192/191610/191610.mp4
發(fā)行地址Contains real examples that you can implement and modify to build useful computer vision systems.Gives line-by-line explanations of computer vision working code examples.Explains training neural netwo
圖書(shū)封面Titlebook: Building Computer Vision Applications Using Artificial Neural Networks; With Step-by-Step Ex Shamshad Ansari Book 20201st edition Shamshad
影響因子.Apply computer vision and machine learning concepts in developing business and industrial applications ?using a practical, step-by-step approach.?..The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section.?..Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.?..The final section is about training neural networks involving a large number of images on clou
Pindex Book 20201st edition
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Building Computer Vision Applications Using Artificial Neural Networks978-1-4842-5887-3
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ial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.?..The final section is about training neural networks involving a large number of images on clou978-1-4842-5887-3
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發(fā)表于 2025-3-22 09:33:46 | 只看該作者
Techniques of Image Processing,other application. In most cases, these input images are converted from one form into another. For example, we may need to resize, rotate, or change their colors. In some cases, we may need to remove the background pixels or merge two images. In other cases, we may need to find the boundaries around
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Deep Learning in Object Detection,During classification tasks, we predict the class of the entire image and do not care what kind of objects are in the image. In this chapter, we will detect objects and their locations within the image.
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發(fā)表于 2025-3-22 17:54:48 | 只看該作者
Practical Example: Object Tracking in Videos, set of images, object detection provides the ability to identify one or more objects in an image, and object tracking provides the ability to track a detected object across a set of images. In previous chapters, we explored the technical aspects of training deep learning models to detect objects. I
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Practical Example: Face Recognition,ct and locate the position of the face in the input image. This is a typical object detection task like we learned about in the previous chapters. After the face is detected, a feature set, also called a . or ., is created from various key points on the face. A human face has 80 nodal points or dist
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Computer Vision Modeling on the Cloud,rk depending on the number of training samples, network configuration, and available hardware resources. A single GPU may not be feasible to train a complex network involving large numbers of training images. The models need to be trained on multiple GPUs. Only a limited number of GPUs can be instal
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