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An Adaptive Color Image Segmentation
Deshmukh K.S. (M.G.M’s CS & IT. Computer Science Department (Nanded, Índia))
Shinde G. N. (Y.M. Research center. Electronics Department (Nanded, Índia))

Data: 2006
Resum: A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i. e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Red neural ; Color Image Segmentation ; Adaptive Thresholding ; Neural Network ; Segmentació de la imatge en color ; Llindar adaptatiu ; Xarxa neural ; Segmentación de la imagen en color ; Umbral adaptativo
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 5 n. 4 (2006) p. 12-23, ISSN 1577-5097

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