Supervised Textured Classigication Using A Probabilistic Neural Network and Constraint Satisfaction Model  

Oleh:

Yegnanarayana, B. ; Raghu, P. P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 3 (1998), page 516-522.
Topik: texture mapping; texture classification; probabilistic neural network; satisfaction model

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature - label interaction constraint. This distribution of features for each class is assumed as a Gaussian mixture model. The feature - label interactions and a set of label - label interactions are represented on a constraint satisfaction neural network. A stochastic relaxation strategy is used to obtain an optimal classification of textures in an image. The advantage of this approach is that all classes in an image are determined simultaneously, similar to human perception of textures in an image.

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