Neural network based object recognition using color block matching
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
A New Type of ART2 Architecture and Application to Color Image Segmentation
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Region-based Deformable Net for automatic color image segmentation
Image and Vision Computing
Neural network based object recognition using color block matching
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
CCIW'11 Proceedings of the Third international conference on Computational color imaging
CCIW'11 Proceedings of the Third international conference on Computational color imaging
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Heart cavity detection in ultrasound images with SOM
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Approach to image segmentation based on interval type-2 fuzzy subtractive clustering
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
Identifying Regions of Interest in Medical Images Using Self-Organizing Maps
Journal of Medical Systems
Color image segmentation using centroid neural network
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Centroid neural network with simulated annealing and its application to color image segmentation
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Facial image medical analysis system using quantitative chromatic feature
Expert Systems with Applications: An International Journal
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
Automatic fish segmentation on vertical slot fishways using SOM neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images
Computers in Biology and Medicine
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
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An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L*u*v* color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks.