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- W2476886948 abstract "This chapter covers aspects of the approach to object detection proposed by Viola and Jones. Object detection in still images and video is among the most-demanded techniques that originate from computer vision. Paul Viola and Michael Jones came up with their framework for object detection in early 2001 and since that time, the framework has not changed significantly. The core algorithm of object detection, as described in consists of creation of the object classifier and application of this classifier to an image. Full GPU residency of an algorithm helps to offload the CPU and eliminates excessive memory copies via the PCI-e bus. The implementation and analysis require basic knowledge of C/CCC programming languages and CUDA architecture. When a user has an object classifier trained on images of size M × N and an input image of much higher dimensions, it is possible to detect all instances of the object of interest in the image. This is achieved by applying the classifier to every region of pixels of size M × N on a large set of scales of the input image. The algorithm is discussed along with its well-known implementation from the OpenCV library. The creation process of the GPU-resident object detection pipeline is described step by step and analysis of intermediate results and the pseudo code is provided." @default.
- W2476886948 created "2016-08-23" @default.
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- W2476886948 date "2011-01-01" @default.
- W2476886948 modified "2023-09-23" @default.
- W2476886948 title "Haar Classifiers for Object Detection with CUDA" @default.
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- W2476886948 doi "https://doi.org/10.1016/b978-0-12-384988-5.00033-4" @default.
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