Results 61 to 70 of about 1,193 (111)

A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix

Multimedia Tools and Applications, 2021
Image processing is a very rich and important research area, which provides efficient solutions to many real and industrial problems. Texture analysis is one of the most interesting fields in image processing and pattern recognition. It became a very attractive research area these last years, especially after the growth and the advancement of ...
Saliha Aouat, Idir Ait-hammi, Izem Hamouchene   +2 more
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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
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Gray Level Co-Occurrence Matrix Computation Based On Haar Wavelet

Computer Graphics, Imaging and Visualisation (CGIV 2007), 2007
In this paper, a new computation for gray level co-occurrence matrix (GLCM) is proposed. The aim is to reduce the computation burden of the original GLCM computation. The proposed computation will be based on Haar wavelet transform. Haar wavelet transform is chosen because the resulting wavelet bands are strongly correlated with the orientation ...
M. M. Mokji, S. A. R. Abu-Bakar
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Research on Characteristic Properties of Gray Level Co-Occurrence Matrix

Applied Mechanics and Materials, 2012
Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal ...
Ying Chen, Feng Yu Yang
openaire   +1 more source

Directional Analysis of Texture Images Using Gray Level Co-Occurrence Matrix

2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008
Direction parameter thetas is one of the important parameters of GLCM (gray level co-occurrence matrix). A fixed angle (such as thetas=45deg) or the average of the measurements in four direction (thetas=0deg,45deg,90deg,135deg) were usually used in calculating GLCM. However, these methods are just empiristic idea, lacking of theoretical support.
Yong Hu 0004, Chunxia Zhao, Hong-nan Wang   +2 more
openaire   +1 more source

Skin Disease Identification System using Gray Level Co-occurrence Matrix

Proceedings of the 9th International Conference on Computer and Automation Engineering, 2017
Diagnosis of the skin disease has always been in terms of a doctor's knowledgeable opinion, or by number of laboratory screenings. Diagnosis is made by looking for additional signs that make the doctor's statement accurate, however in some cases signs are indistinguishable that results to miss potential diagnosis.
Joseph Mark G. Aglibut, Luigi L. Alonzo, Monica Francesca B. Coching, Jumelyn L. Torres, Noel B. Linsangan   +4 more
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An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications, 2017
Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis
Maktabdar Oghaz, M., Maarof, M. A., Rohani, M. F., Zainal, A., Shaid, S. Z. M.   +4 more
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Weld Classification Using Gray Level Co-Occurrence Matrix and Local Binary Patterns

2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018
This paper presents an algorithm that can classify weld seams from images, exploiting machine learning techniques. Manual visual inspection is the primary way of evaluating weld seams, in cases where the primary goal is to keep inspection costs low. Such, visual inspections entail manual interpretation and evaluation, which are both time consuming and ...
Philip Valentin, Tsampikos Kounalakis, Lazaros Nalpantidis   +2 more
openaire   +2 more sources

A Supervised Method for Determining Displacement of Gray Level Co-Occurrence Matrix

2011 7th Iranian Conference on Machine Vision and Image Processing, 2011
Gray Level Co-occurrence Matrix (GLCM) is one of the most powerful methods for extracting texture information. GLCM extracts occurrence probability of intensities with a specific displacement between pair pixels. Most applications of GLCM use a single displacement or a distinct number of displacements.
Hassan Nikoo, Hossein Talebi, Abdolreza Mirzaei   +2 more
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Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation

2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2018
A novel interpolation-based model for the computation of the Gray Level Co-occurrence Matrix (GLCM) is presented. The model enables GLCM computation for any real-valued angles and offsets, as opposed to the traditional, lattice-based model. A texture directionality estimation algorithm is defined using the GLCM-derived correlation feature.
Marcin Kociolek, Peter Bajcsy, Mary Brady, Antonio Cardone   +3 more
openaire   +1 more source