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

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书目名称Building Computer Vision Applications Using Artificial Neural Networks影响因子(影响力)




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书目名称Building Computer Vision Applications Using Artificial Neural Networks网络公开度学科排名




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书目名称Building Computer Vision Applications Using Artificial Neural Networks被引频次学科排名




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书目名称Building Computer Vision Applications Using Artificial Neural Networks年度引用学科排名




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书目名称Building Computer Vision Applications Using Artificial Neural Networks读者反馈学科排名

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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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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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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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