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Unsupervised texture image segmentation by improved neural network art2
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Labini Sr., G. Sylos Mugnuolo, R. Desario, Marco Wang, Zhiling |
| Copyright Year | 1994 |
| Description | We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera. |
| File Size | 386352 |
| Page Count | 6 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19940026044 |
| Archival Resource Key | ark:/13960/t7bs3qd1f |
| Language | English |
| Publisher Date | 1994-03-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Computer Vision Algorithms Neural Nets Robot Sensors Pattern Recognition Image Enhancement Image Classification Textures Fuzzy Systems Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
