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Handbook of Pattern Recognition and Computer Vision
TLDR
This book provides the latest advances on pattern recognition and computer vision along with their many applications and features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers.Abstract:
Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.read more
Citations
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Journal ArticleDOI
Filtering for texture classification: a comparative study
Trygve Randen,John Hakon Husoy +1 more
TL;DR: Most major filtering approaches to texture feature extraction are reviewed and a ranking of the tested approaches based on extensive experiments is presented, showing the effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches.
Journal ArticleDOI
Ultrasound image segmentation: a survey
J.A. Noble,Djamal Boukerroui +1 more
TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.
Book ChapterDOI
Blur Insensitive Texture Classification Using Local Phase Quantization
Ville Ojansivu,Janne Heikkilä +1 more
TL;DR: The classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods, and it is also slightly better for textures that are not blurred.
Journal ArticleDOI
Comparison of algorithms that select features for pattern classifiers
Mineichi Kudo,Jack Sklansky +1 more
TL;DR: It is shown that sequential floating search methods are suitable for small- and medium-scale problems and genetic algorithms are suitableFor large-scale feature selection algorithms, the goodness of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier.
Journal ArticleDOI
Towards better exploiting convolutional neural networks for remote sensing scene classification
TL;DR: An analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors points that fine tuning tends to be the best performing strategy.