Results 201 to 210 of about 8,610,250 (238)

A parallel architecture for co-occurrence matrix computation

Proceedings of 36th Midwest Symposium on Circuits and Systems, 2002
Using 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

Combining singular point and co-occurrence matrix for fingerprint classification

Proceedings of the Third Annual ACM Bangalore Conference, 2010
Fingerprint classification is an important part of fingerprint identification system that works on large databases to increase its matching speed. In this paper, an automatic fingerprint classification method is proposed to classify the fingerprint images by combining singular points and Gray Level Co-occurrence Matrix (GLCM) features.
C. Zacharias Geevar, P. Sojan Lal
openaire   +2 more sources

Robust skin-roughness estimation based on co-occurrence matrix

Journal of Visual Communication and Image Representation, 2017
Abstract As the interest in one’s appearance has recently increased, the demand for diagnosing skin conditions has also increased. However, conventional specialized skin diagnostic devices are generally expensive, and people have to visit a skin-care shop to diagnose their skin condition. This is time consuming and troublesome.
Ji-Sang Bae, Sang-Ho Lee, Kang-Sun Choi, Jong-Ok Kim   +3 more
openaire   +2 more sources

Classification of Crop Lodging with Gray Level Co-occurrence Matrix

2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
Lodging in agricultural crops is the permanent displacement of a plant from its upright position [2]. It may be caused by several weather and environmental conditions. Harvesting severely lodged crops may take twice as much time and results in reduced yield. Plant breeders seek to identify and select for lodging-resistant varieties.
Sajith Rajapaksa, Mark G. Eramian, Hema Sudhakar Duddu, Menglu Wang 0001, Steve Shirtliffe, Seungbum Ryu, Anique Josuttes, Ti Zhang, Sally Vail, Curtis Pozniak, Isobel Parkin   +10 more
openaire   +2 more sources

Dynamic textures indexing using the co-occurrence matrix features

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2012
The area of applications of dynamic texture is very wide: video surveillance, transaction systems, medical application and video synthesis. The paper presents an indexing model in large databases of dynamics texture using the co-occurrence matrix features.
Muguras Mocofan, Radu Vasiu
openaire   +1 more source

Image Retrieval Based on Block Motif Co-Occurrence Matrix

2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019
Motif co-occurrence matrix (MCM) is one of the commonly used image features descriptions. However, MCM has two shortcomings. One is that it doesn’t meets translation invariance, and another is that different sub-blocks can be represented by the same motif.
Yuan-Ting Yan, Meili Yang, Shi-bo Zhang, Yanping Zhang 0001   +3 more
openaire   +1 more source

Image retrieval using improved texton co-occurrence matrix

International Journal of Computational Vision and Robotics, 2011
This 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, R. P. Maheshwari 0001, Raman Balasubramanian   +2 more
openaire   +1 more source

Edge detection in noisy images based on the co-occurrence matrix

Pattern Recognition, 1994
Abstract An edge detection method for noisy images is proposed based on the co-occurrence matrix. In the proposed technique based on the step-edge model, the gray level information is simply converted into a bit-map, i.e. the uniform and boundary regions of an image are transformed into a binary pattern by normalization using the local mean.
Deok J. Park, Kwon Moon Nam, Rae-Hong Park   +2 more
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A Novel Fuzzy Co-occurrence Matrix for Texture Feature Extraction

2013
Texture analysis is one of the important steps in many computer vision applications. One of the important parts in texture analysis is texture classification. This classification is not an easy problem since texture can be non-uniform due to many reasons, e.g., rotation, scale, and etc.
Yutthana Munklang, Sansanee Auephanwiriyakul, Nipon Theera-Umpon   +2 more
openaire   +2 more sources

The semivariogram in comparison to the co-occurrence matrix for classification of image texture

IEEE Transactions on Geoscience and Remote Sensing, 1998
Semivariogram functions are compared to co-occurrence matrices for classification of digital image texture, and accuracy is assessed using test sites. Images acquired over the following six different spectral bands are used: 1) SPOT HRV, near infrared; 2) Landsat thematic mapper (TM), visible red; 3) India Remote Sensing (IRS) LISS-II, visible green; 4)
James R. Carr, Fernando Pellon de Miranda   +1 more
openaire   +2 more sources