{"product_id":"image-processing-and-computer-vision-jamie-flux-9798300982058","title":"Image Processing and Computer Vision Algorithms With CUDA","description":"\u003cp\u003eUnlock the true potential of GPU acceleration in image processing and computer vision with this comprehensive guide. Designed for researchers, practitioners, and advanced students, this book delves deep into cutting-edge algorithms optimized using \u003cb\u003epyCUDA\u003c\/b\u003e, offering unparalleled performance improvements for real-world applications.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey Features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eIn-Depth Exploration of Advanced Algorithms: \u003c\/b\u003e Each chapter provides a meticulous analysis of specific, state-of-the-art algorithms, pushing the boundaries of current knowledge and exploring uncharted territories in the field.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eOptimization with pyCUDA: \u003c\/b\u003e Learn how to harness the massive parallelism of CUDA-enabled GPUs using pyCUDA, transforming computational workflows for real-time processing.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eInnovative Methodologies: \u003c\/b\u003e Discover original theoretical frameworks, novel methodologies, and interdisciplinary perspectives that challenge the status quo and inspire new horizons.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePractical Implementation Details: \u003c\/b\u003e Gain insights into optimizing memory management, thread synchronization, and kernel configurations to maximize computational efficiency.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eSample Topics Covered: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eOptimized Convolutional Filtering Techniques: \u003c\/b\u003e Implement convolutional filters like Gaussian and Laplacian kernels using pyCUDA, achieving real-time performance even on high-resolution images through optimized memory access and data transfer strategies.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eAdaptive Edge Detection with Dynamic Thresholding: \u003c\/b\u003e Explore novel adaptive edge detection algorithms employing dynamic thresholding mechanisms that adjust in real-time based on local image statistics, enhancing accuracy in varying illumination and noise conditions.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eAdvanced Image Segmentation with Graph-Based Methods: \u003c\/b\u003e Model images as weighted graphs and implement parallel algorithms for graph construction and label propagation, utilizing spectral clustering and community detection techniques optimized for GPU architectures.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eAccelerated Histogram Equalization and Contrast Enhancement: \u003c\/b\u003e Learn to compute histograms and cumulative distribution functions in parallel, implementing adaptive methods like Contrast Limited Adaptive Histogram Equalization (CLAHE) for efficient image enhancement.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eFeature Detection and Description with SURF and SIFT Algorithms: \u003c\/b\u003e Master the implementation of Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) on GPUs, optimizing integral image computations and descriptor matching for real-time applications.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eAdvanced Optical Flow Estimation: \u003c\/b\u003e Dive into optical flow computation using Lucas-Kanade and Horn-Schunck methods, optimized for GPUs to handle large displacements and occlusions with real-time performance.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eStereo Vision and Depth Map Estimation: \u003c\/b\u003e Implement depth estimation techniques using block matching and semi-global matching methods, optimizing cost aggregation and handling of occlusions for high-resolution stereo images.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eWavelet Transformations for Multi-Resolution Processing: \u003c\/b\u003e Utilize discrete wavelet transforms for tasks like denoising and compression, implementing both 1D and 2D transformations efficiently on GPUs.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eReal-Time Object Recognition with HOG Features: \u003c\/b\u003e Accelerate object recognition using Histogram of Oriented Gradients (HOG) descriptors, optimizing gradient histograms and detection strategies for applications like pedestrian and vehicle recognition.\u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eImage Registration Techniques Using Mutual Information: \u003c\/b\u003e Apply multi-modal image registration using mutual information metrics, optimizing joint histogram estimation and transformation handling for applications in medical imaging and panorama stitching.\u003c\/li\u003e\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Jamie Flux\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798300982058\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 11\/23\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 394\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.16lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.81d","brand":"Jamie Flux","offers":[{"title":"Paperback","offer_id":47201040859391,"sku":"9798300982058","price":39.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_a42f5a90-ab1e-469b-898e-d346fb0c5bf8.jpg?v=1756788693","url":"https:\/\/www.whiterainbookhouse.com\/products\/image-processing-and-computer-vision-jamie-flux-9798300982058","provider":"WR Book House","version":"1.0","type":"link"}