NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Computer vision.")'

Template matching techniques in computer vision : theory and practice theory and practice

By Brunelli, Roberto. Chichester, West Sussex, U.K. ; | Hoboken, NJ : Wiley, 2009 . 338 p : 26 cm.. 9780470517062

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Fuzzy Logic and the Semantic Web

By Sanchez, Elie. San Diego : Elsevier Science [Imprint] | Elsevier Science & Technology Books 2006 9780444519481 | 0444519483 (Trade Cloth)

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Computer vision : algorithms and applications /

By Szeliski, Richard,. London ; | New York : Springer, 2011 9781848829343 (hbk.)

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Neuroprosthetics Theory and Practice /

River Edge, N.J. : World Scientific, 2004 . xxv, 1261 p. : 24 cm..

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Changeable and Reconfigurable Manufacturing Systems

By ElMaraghy, Hoda A.. 9781848820661

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Advances in Embedded Computer Vision \

By Kisačanin, Branislav.. 9781849967761

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Handbook of ambient intelligence and smart environments \

By Nakashima, Hideyuki.. New York : Springer, 2010 9780387938073 (hbk.)

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Swarm Stability and Optimization /

By Gazi, Veysel.. 2011 9783642180408

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Bayesian Network (E-Book)

By Ahmed Rebai. India Sciyo 2010 . x,432p;

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High-performance interactive marketing /

By Ryan, Christopher.. [S.l.] : Racom Communications, 2001 . 312 p. ; , Hardcover. 24 cm.. 0970451504 | 9780970451507

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E-business to go : insider secrets /

By Thompson, G. Liam.. [S.l.] : Appallaso Publishing, 2001 . 256 p. ; , Paperback. 24 cm.. 0970151705 | 9780970151704

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Contemporary marketing : plus /

By Boone, Louis E.. [S.l.] : Harcourt Brace College Publishers, 1994 . 700 p. ; , Hardcover. 29 cm.. 0030976014 | 9780030976018

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E-business to go : insider secrets /

By Thompson, G. Liam.. [S.l.] : Appallaso Publishing, 2001 . 256 p. ; 24 cm.. 0970151705 (paperback) | 9780970151704 (paperback)

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Robotics, Vision and Control : Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition /

By Corke, Peter.. . 1 online resource (XXIX, 693 pages 402 illustrations, 335 illustrations in color.) 9783319544137

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Enhancing Tumor Segmentation in 3D MRI Scans with YOLOv8 And Volumetric segmentation: A Computational Efficient Approach /

By Abdullah. . 71p. ; 30cm..

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Automatic Detection and Recognition of Citrus Fruits Diseases Using Deep Learning Model /

By Faisal, Shah . . 59p. , In a country's economy, agriculture plays a very vital role. Agriculture's yield and production are reduced by plant diseases, resulting in significant economic losses and instability in the food market. In plants, the citrus fruit crop is one of the most important agricultural products in the world, produced and grown in around 140 countries. It has a lot of nutrients, such as vitamin C. However, due to pests and diseases, citrus cultivation is widely affected and suffers significant losses in both yield and quality. The majority of plant diseases exhibit visible symptoms, and the accepted method used today is for a skilled plant pathologist to detect the diseases by examining affected plant leaves under a microscope, which is a costly and time-consuming method. During the last decade, computer vision and machine learning have been widely adopted to detect and classify plant diseases, providing opportunities for early disease detection and bringing improvements to agricultural production. The early detection and accurate diagnosis of plant diseases are essential for reducing their spread and damage to crops. In this work, we presented an automatic system for early detection and recognition of citrus plant diseases based on a deep learning (DL) model to improve accuracy and reduce computational complexity. The most recent transfer learning-based models were applied to our dataset in order to increase classification accuracy. In this work, we successfully proposed a CNN-based pre-trained model (EfficientNetB3, ResNet50, MobiNetV2, (InceptionV3) for the identification and classification of citrus plant diseases using transfer learning. In order to assess the performance of the model, we found that the transfer of an EfficientNetb3 model led to the highest training, validating, and testing accuracies, which were 99.43%, 99.48%, and 99.58%, respectively. The proposed CNN model exceeds other cutting-edge CNN network architectures developed in earlier literature in the identification and categorization of citrus plant diseases. 30cm.

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Learning deep learning : theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow /

By Ekman, Magnus,. Boston : Addison-Wesley; 2022 . 688p 9780137470358

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Automatic Antenna Alignment System (AAAS) /