Synopsis
Deep learning has fundamentally transformed image processing from hand-crafted algorithmic pipelines into end-to-end learned systems capable of human-surpassing performance across classification, detection, segmentation, generation, and restoration tasks. Convolutional neural networks replace decades-old filtering, thresholding, and feature engineering with hierarchical feature extractors learning directly from raw pixels through millions of parameterized filters trained via gradient descent. This paradigm shift eliminates brittle cascade architectures where edge detection failure propagates through Hough voting to tracking collapse, replacing sequential failure modes with robust holistic understanding emerging from statistical training. Modern vision transformers extend convolutional foundations through global self-attention mechanisms modeling long-range spatial dependencies absent from purely local receptive fields.
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Deep Learning Methods for Image Processing Workflows
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Paperback. Condition: new. Paperback. Deep learning has fundamentally transformed image processing from hand-crafted algorithmic pipelines into end-to-end learned systems capable of human-surpassing performance across classification, detection, segmentation, generation, and restoration tasks. Convolutional neural networks replace decades-old filtering, thresholding, and feature engineering with hierarchical feature extractors learning directly from raw pixels through millions of parameterized filters trained via gradient descent. This paradigm shift eliminates brittle cascade architectures where edge detection failure propagates through Hough voting to tracking collapse, replacing sequential failure modes with robust holistic understanding emerging from statistical training. Modern vision transformers extend convolutional foundations through global self-attention mechanisms modeling long-range spatial dependencies absent from purely local receptive fields. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209438950
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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS
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Paperback. Condition: new. Paperback. Deep learning has fundamentally transformed image processing from hand-crafted algorithmic pipelines into end-to-end learned systems capable of human-surpassing performance across classification, detection, segmentation, generation, and restoration tasks. Convolutional neural networks replace decades-old filtering, thresholding, and feature engineering with hierarchical feature extractors learning directly from raw pixels through millions of parameterized filters trained via gradient descent. This paradigm shift eliminates brittle cascade architectures where edge detection failure propagates through Hough voting to tracking collapse, replacing sequential failure modes with robust holistic understanding emerging from statistical training. Modern vision transformers extend convolutional foundations through global self-attention mechanisms modeling long-range spatial dependencies absent from purely local receptive fields. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9786209438950
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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS
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Taschenbuch. Condition: Neu. DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS | Rajeswari J (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209438950 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 134552808
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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deep learning has fundamentally transformed image processing from hand-crafted algorithmic pipelines into end-to-end learned systems capable of human-surpassing performance across classification, detection, segmentation, generation, and restoration tasks. Convolutional neural networks replace decades-old filtering, thresholding, and feature engineering with hierarchical feature extractors learning directly from raw pixels through millions of parameterized filters trained via gradient descent. This paradigm shift eliminates brittle cascade architectures where edge detection failure propagates through Hough voting to tracking collapse, replacing sequential failure modes with robust holistic understanding emerging from statistical training. Modern vision transformers extend convolutional foundations through global self-attention mechanisms modeling long-range spatial dependencies absent from purely local receptive fields.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. Seller Inventory # 9786209438950
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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS
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