DSpace Collection:http://dspace.wunu.edu.ua/handle/316497/149192024-03-29T07:34:26Z2024-03-29T07:34:26ZFuzzy clustering methods in multispectral satellite image segmentationSadykhov, Rauf Kh.Ganchenko, Valentin V.Podenok, Leonid P.http://dspace.wunu.edu.ua/handle/316497/320122018-12-05T10:15:56Z2009-01-01T00:00:00ZTitle: Fuzzy clustering methods in multispectral satellite image segmentation
Authors: Sadykhov, Rauf Kh.; Ganchenko, Valentin V.; Podenok, Leonid P.
Abstract: Segmentation method for subject processing the multi-spectral satellite images based on fuzzy clustering and preliminary non-linear filtering is represented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson- Kessel, and Gath-Geva have been utilized. The experimental results obtained using these algorithms with and without preliminary nonlinear filtering to segment multi-spectral Landsat images have approved that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy Cmeans, Gustafson-Kessel, and Gath-Geva algorithms have got linear computational complexity depending on initial cluster amount and image size for single iteration step. They assume internal parallel implementation. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence more clear segment outlining…2009-01-01T00:00:00ZA survey on wavelet network, multi library wavelet network training, 1D-2D function approximation and a new image compression methodBellil, WajdiAmar, Chokri BenAlimi, Adel M.http://dspace.wunu.edu.ua/handle/316497/320112018-12-05T10:10:19Z2009-01-01T00:00:00ZTitle: A survey on wavelet network, multi library wavelet network training, 1D-2D function approximation and a new image compression method
Authors: Bellil, Wajdi; Amar, Chokri Ben; Alimi, Adel M.
Abstract: This paper presents an original architecture of Wavelet Neural Network (WNN) based on multi Wavelets activation function and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.2009-01-01T00:00:00ZAutomatic detection of spinal deformity based on statistical features from the Moire topographic imagesKim, HyoungseopTan, Joo KooiIshikawa, SeijiShinomiya, Takashihttp://dspace.wunu.edu.ua/handle/316497/320102018-12-05T10:03:46Z2009-01-01T00:00:00ZTitle: Automatic detection of spinal deformity based on statistical features from the Moire topographic images
Authors: Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji; Shinomiya, Takashi
Abstract: Spinal deformity is one of a disease mainly suffered by teenagers during their growth stage particularly from element school to middle school. There are many different causes of abnormal spinal curves, but all of them are unknown. To find the spinal deformity in early stage, orthopedists have traditionally performed on children a painless examination called a forward bending test in mass screening of school. But this test is neither objective nor reproductive, and the inspection takes much time when applied to medical examination in schools. To solve this problem, a moire method has been proposed which takes moire topographic images of human backs and checks symmetry/asymmetry of their moire patterns. In this paper, we propose a method for automatic judgment of spinal deformity which is obtained moire topographic images based on statistical features on the moire image. Statistical feature of asymmetry degrees are applied to train employing the classifier such as Artificial Neural Network, Support Vector Machine, Self-Organization Map and AdaBoost.2009-01-01T00:00:00ZFace detection on grayscale and color images using combined cascade of classifiersKurylyak, YuriyPaliy, IhorSachenko, AnatolyChohra, AmineMadani, Kuroshhttp://dspace.wunu.edu.ua/handle/316497/320092018-12-05T09:53:31Z2009-01-01T00:00:00ZTitle: Face detection on grayscale and color images using combined cascade of classifiers
Authors: Kurylyak, Yuriy; Paliy, Ihor; Sachenko, Anatoly; Chohra, Amine; Madani, Kurosh
Abstract: The paper describes improved face detection methods for grayscale and color images using the combined cascade of classifiers and skin color segmentation. The combined cascade with proposed face candidates’ verification method allows achieving one of the best detection rates on CMU test set and a high processing speed suitable for a video flow processing. It’s also shown that the mixture of color spaces is more efficient during the skin color segmentation than the application of one color space. A lot of experiments are made to choose rational parameters for the developed face detection system in order to improve the detection rate, false positives’ number and system’s speed.2009-01-01T00:00:00Z