Deep Learning Methods for Image Processing Workflows - Couverture souple

J, Rajeswari ; S, Piriyadharshini ; S, Senthil Kumar

9786209438950: Deep Learning Methods for Image Processing Workflows

Couverture souple

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

Editeur : LAP Lambert Academic Publishing, 2026

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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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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2026
Langue: anglais
ISBN 10: 6209438954
ISBN 13: 9786209438950
Reliure: Broch�
Nombre de pages: 156
Coordonn�es du fabricant: non disponible
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Deep Learning Methods for Image Processing Workflows

J, Rajeswari; S, Piriyadharshini; S, Senthil Kumar

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

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Deep Learning Methods for Image Processing Workflows

J, Rajeswari; S, Piriyadharshini; S, Senthil Kumar

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

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Deep Learning Methods for Image Processing Workflows

J, Rajeswari; S, Piriyadharshini; S, Senthil Kumar

Edit� par LAP Lambert Academic Publishing, 2026

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

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Deep Learning Methods for Image Processing Workflows (Paperback)

Rajeswari J.

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Paperback. Etat : 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. N� de r�f. du vendeur 9786209438950

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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS

Rajeswari J

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Deep Learning Methods for Image Processing Workflows (Paperback)

Rajeswari J.

Edit� par LAP Lambert Academic Publishing, 2026

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Paperback. Etat : 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. N� de r�f. du vendeur 9786209438950

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Deep Learning Methods for Image Processing Workflows (Paperback)

Rajeswari J.

Edit� par LAP Lambert Academic Publishing, 2026

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Paperback. Etat : 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 UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786209438950

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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS

Rajeswari J (u. a.)

Edit� par LAP LAMBERT Academic Publishing, 2026

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

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DEEP LEARNING METHODS FOR IMAGE PROCESSING WORKFLOWS

Rajeswari J

Edit� par LAP LAMBERT Academic Publishing Jan 2026, 2026

ISBN 10 : 6209438954 ISBN 13 : 9786209438950

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Taschenbuch. Etat : 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 Landstra�e 99, 66123 Saarbr�cken 156 pp. Englisch. N� de r�f. du vendeur 9786209438950

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