Titlebook: Innovations in Intelligent Image Analysis; Halina Kwaśnicka,Lakhmi C. Jain Book 2011 Springer-Verlag Berlin Heidelberg 2011 Biometric Syst

MOTTO

Innovations in Intelligent Image Analysis978-3-642-17934-1Series ISSN 1860-949X Series E-ISSN 1860-9503

Stable-Angina

Asymptomatic

antenna

1860-949X s in image interpretation, image retrieval, knowledge acquis. This book presents an introduction to new and important research in the images processing and analysis area. It is hoped that this book will be useful for scientists and students involved in many aspects of image analysis. The book does n

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Fast and Efficient Local Features Detection for Building Recognition,this analysis, we present a new approach for detecting and filtering local features. The proposed approach is tested in a real-life application which supports navigation in urban environments based on visual information. The study shows that our approach performs as well as existing methods but with a significantly lower number of features.

Self-Help-Group

Local Keypoints and Global Affine Geometry: Triangles and Ellipses for Image Fragment Matching,is. In the former stage, we analyze low-level image characteristics, such as local intensity gradients or local shape approximations. In the latter stage, we formulate global geometrical hypotheses about the image contents and verify them using a probabilistic framework.

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Feature Analysis for Object and Scene Categorization,nd selection in two case studies. Our aim is to demonstrate that feature selection is indispensable in providing clues for finding good feature combination schemes and building compact and effective classifiers that produce much improved performance.

机密

Book 2011ul for scientists and students involved in many aspects of image analysis. The book does not attempt to cover all of the aspects of Computer Vision, but the chapters do present some state of the art examples..

朋党派系

大约冬季

Multi-class Classification in Image Analysis via Error-Correcting Output Codes,fication decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.