International Journal of Pattern Recognition and Artificial Intelligence

A knowledge-guided approach to automatic classification of Coastal Zone Color images off the West Florida Shelf is described. The approach is used to identify red tides on the West Florida Shelf, as well as areas with high concentration of dissolved organic matter such as a river plume found seasonally along the West Florida coast over the middle of the shelf. The Coastal Zone Color images are initially segmented by the unsupervised Multistage Random Sampling Fuzzy c-Means algorithm. Then, a knowledge-guided system is applied to the centroid values of resultant clusters to label case I, case II waters, a dilute river plume ("green river"), and red tide. The domain knowledge base contains information on cluster distribution in feature space, as well as spatial information such as bathymetry data. Our knowledge base consists of a rule-guided system and an embedded neural network. From 60 images, after training the system, this procedure recognizes all 15 images which contained a river plume and 45 images without. The system can correctly classify 74% of the pixels that belong to the river plume, which provides a substantial advantage to users looking for offshore extensions of riverine influence. Red tides are also successfully identified in a time series of images for which ground truth confirmed the presence of a harmful bloom.

Since networks of main roads are basic information for the classification of use of the earth surface, the automatic detection of roads from satellite images is a very important issue. In this paper, a new detection theory is proposed which can overcome drawbacks of current theories and detect plural roads in an image with high speed and high precision. Firstly, binary images representing edges are used to evaluate the possibility for a road to pass on a given pixel. An 8-directions-filter, a clearing filter and a parallel-edge-detection filter are proposed which can bring insufficient local information to each other to obtain global information enough to detect a road and by which the possibility of a road-passing on the pixel can be effectively evaluated. Secondly, by using the Hough transform and the optimal search method it is possible to detect a complete road. This detection theory does not depend on the size of image and can detect all the roads in an image including intersecting and T-type roads.

A kriging method is presented as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. The concepts are based on the analysis of semivariances, the linear combination scheme of kriging, and fuzzy sets. Application of fuzzy sets allows a gradual transition between two boundaries of semivariance levels as a criterion for smoothing the pixel values. This fuzzy thresholding also allows some degree of flexibility to suit various desired results for particular problems. Experimental results obtained by the fuzzy kriging filter are smoother and still preserve edges compared with those by the adaptive Wiener filter.

Linear Quadtree (LQ), Interpolation-Based Binary tree (IBB) and Minimized Boolean Function (MBF) are some of the set-of-codes type of encoding scheme for region representation of binary images. Chain Code (CC) is a well-known technique for boundary representation of such images. It is desirable to develop efficient methods of conversion between the two types of representation schemes. In this paper, we present a generalized algorithm for conversion from set-of-codes type of representation to chain codes. The computational complexity of the algorithm is also discussed.

We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multiwindow scheme using left–right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both accuracy and efficiency.

In this paper, a new Hopfield-model net called Compensated Fuzzy Hopfield Neural Network (CFHNN) is proposed for vector quantization in image compression. In CFHNN, the compensated fuzzy c-means algorithm, modified from penalized fuzzy c-means, is embedded into Hopfield neural network so that the parallel implementation for codebook design is feasible. The vector quantization can be cast as an optimal problem that may also be regarded as a minimization of a criterion defined as a function of the average distortion between training vector and codevector. The CFHNN is trained to classify the divided vectors on a real image into feasible class to generate an available codebook when the defined energy function converges to near global minimum. The training vectors on a divided image are mapped to a two-dimensional Hopfield neural network. Also the compensated fuzzy c-means technique is used to update the quantization performance and to eliminate searching for the weighting factors. In the context of vector quantization, each training vector on the divided image is represented by a neuron which is fully connected by the other neurons. After a number of iterations, neuron states are refined to reach near optimal result when the defined energy function is converged.

The capability of Kasuba's Simplified Fuzzy ARTMAP (SFAM) to behave as a Pattern Recognizer/Classifier of images both noisy and noise free has been investigated in this paper. This calls for augmenting the original Neuro–Fuzzy model with a modified moment-based RST invariant feature extractor.

The potential of the SFAM based Pattern Recognizer to recognize patterns — monochrome and color, noisy and noise free — has been studied on two experimental problems. The first experiment which concerns monochrome images, pertains to recognition of satellite images, a problem discussed by Wang et al. The second experiment, which concerns color images, deals with the recognition of some sample test colored patterns. The results of the computer simulation have also been presented.

This paper describes probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored. The first model maintains no inter-pixel dependencies, the second model is capable of modeling a set of arbitrary pair-wise dependencies, and the last model allows dependencies only between neighboring pixels. We show that for all three of these models, the accuracy of the learned models can be greatly improved by augmenting a small number of labeled training images with a large set of unlabeled images using Expectation–Maximization. This is important because it is often difficult to obtain image labels, while many unlabeled images are readily available. Through a large set of empirical tests, we examine the benefits of unlabeled data for each of the models. By using only two randomly selected labeled examples per class, we can discriminate between the five facial orientations with an accuracy of 94%; with six labeled examples, we achieve an accuracy of 98%.