Results 231 to 240 of about 766,173 (279)
Some of the next articles are maybe not open access.
Co-occurrence matrix features for fingerprint verification
2011 IEEE International Conference on Anti-Counterfeiting, Security and Identification, 2011In this paper, an enhanced image-based fingerprint verification algorithm is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to poor image quality. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point and then extracts a 129 X 129 block ...
Mohammed S. Khalil +2 more
openaire +1 more source
Rotation Invariant Co-occurrence Matrix Features
2017Grey level co-occurrence matrix (GLCM) has been one of the most used texture descriptor. GLCMs continue to be very common and extended in various directions, in order to find the best displacement for co-occurrence extraction and a way to describe this co-occurrence that takes into account variation in orientation.
PUTZU, LORENZO, DI RUBERTO, CECILIA
openaire +2 more sources
Crowd Detection Based on Co-occurrence Matrix
2013This paper describes a new approach for crowd detection based on the analysis of the gray level dependency matrix (GLDM), a technique already exploited for measuring image texture. New features for characterizing the GLDM have been proposed, and both Adaboost and Bayesian classifiers have been applied to the new feature introduced, and the system has ...
GHIDONI, STEFANO +2 more
openaire +2 more sources
Generalized co-occurrence matrix for multispectral texture analysis
Proceedings of 13th International Conference on Pattern Recognition, 1996We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM).
M. Hauta-Kasari +3 more
openaire +1 more source
Detecting Ocular Tb With Modified Co-Occurrence Matrix
Tuijin Jishu/Journal of Propulsion Technology, 2023Image mapping is a method both for identifying and retrieving similar images from a large database of digital images. This system utilizes the low level image features such as color, texture and shape for retrieving similar images. Among the low level image features, color and texture are perceptually important low level features.
openaire +1 more source
Scene Classification by Feature Co-occurrence Matrix
2015Classifying scenes (such as mountains, forests) is not an easy task owing to their variability, ambiguity, and the wide range of illumination and scale conditions that may apply. Bag of features (BoF) model have achieved impressive performances in many famous databases (such as the 15 scene dataset).
Haitao Lang +4 more
openaire +1 more source
Temporal co-occurrence matrix approaches to motion analysis
SPIE Proceedings, 2004We propose a novel method for motion analysis in video sequences. It extends the co-occurrence matrix concept for texture analysis to the temporal domain. The approach proved to be versatile in the sense of targeting different motion analysis tasks.
A. Ukovich +2 more
openaire +2 more sources
A parallel architecture for co-occurrence matrix computation
Proceedings of 36th Midwest Symposium on Circuits and Systems, 2002Using the odd-even network topology, a parallel hardware architecture for gray level image co-occurrence matrix computation is designed. The architecture consists of simple, regularly structured processing elements (PE's) operating in parallel. As a result, the proposed design is suitable for VLSI implementation.
S. Khalaf, M. El-Gabali, N. Abdelguerfi
openaire +1 more source
Image retrieval using improved texton co-occurrence matrix
International Journal of Computational Vision and Robotics, 2011This paper proposed a new technique for content-based image retrieval (CBIR) using a combination of a` trous wavelet transform (AWT) and Julesz|s texton elements. AWT is used to decomposse the image into different scales and different texton elements are used to detect the spatial co-relation among the transform coefficients in horizontal, vertical ...
Anil Balaji Gonde +2 more
openaire +1 more source
Dynamic background subtraction using texton co-occurrence matrix
2014 Annual IEEE India Conference (INDICON), 2014Moving object detection in the presence of changing illumination and non-stationary background such as swaying of trees, fountains, ripples in water, flag fluttering in the wind, camera jitters, noise, etc., is known to be very difficult and challenging task.
Deepak Kumar Panda, Sukadev Meher
openaire +1 more source