International Journal of Pattern Recognition and Artificial Intelligence

A problem of automatic detection of tracks in aerial photos is considered. We adopt a Bayesian approach and base our inference on an a priori knowledge of the structure of tracks. The probability of a pixel to belong to a track depends on how the pixel gray level differs from the gray levels of pixels in the neighborhood and on additional prior information. Several suggestions on how to formalize the prior knowledge about the shape of the tracks are made. The Gibbs sampler is used to construct the most probable configuration of tracks in the area. The method is applied to aerial photos with cell size of 1 sq. m. Even for detection of trails of width comparable with or smaller than the cell size, positive results can be achieved.

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification "worth" is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

This paper introduces a novel method for the recognition of human faces in two-dimensional digital images using a new feature extraction method and Radial Basis Function (RBF) neural network with a Hybrid Learning Algorithm (HLA) as classifier. The proposed feature extraction method includes human face localization derived from the shape information using a proposed distance measure as Facial Candidate Threshold (FCT) as well as Pseudo Zernike Moment Invariant (PZMI) with a newly defined parameter named Correct Information Ratio (CIR) of images for disregarding irrelevant information of face images. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that high order PZMI together with the derived face localization technique for extraction of feature data yielded a recognition rate of 99.3%.

Based on evolutionary computation, a new 3D route planner for unmanned air vehicles is presented. In our evolutionary route planner, the individual candidates are evaluated with respect to the workspace. Therefore a computation of the configuration space is avoided. With Digital Terrain Elevation Data, our approach can find a near-optimal route that can increase the surviving probability efficiently. By using a problem-specific representation of candidate solutions and genetic operators, the routes are generated in real-time and are able to take into account different kinds of mission constraints such as minimum route leg length and flying altitude, maximum turning angle, and fixed approach vector to goal position, etc.

Symmetry of an object on a plane and in a space is an important geometric feature for biology, chemistry, and the understanding of human perception of figures. We propose a randomized method for the detection of symmetry in planar polygons and polyhedrons without assuming the predetermination of the centroids of the objects. Using a voting process, which is the main concept of the Hough transform in image processing, we transform the geometric computation for symmetry detection which is usually based on graph theory and combinatorial optimization, to the peak detection problem in a voting space in the context of the Hough transform. Our algorithm detects the centroid after detecting symmetry of an object for both planar and spatial objects.

Extraction of effective features plays a key role in pattern recognition. A large number of patterns, such as speech, radar signals, earthquake signals, handwriting, etc. are of non-stationary signals or exhibit time-varying behavior. The features of these patterns are often located in both the time and frequency domains. The traditional methods fail to extract such kind of features. Fortunately, wavelet packet transform (WPT) can provide an arbitrary time-frequency decomposition for the signals, because a wavelet packet (WP) library contains many WP bases, which can handle the different components of a signal. Therefore, by selecting a suitable basis, which is called "best basis", the effective features can be extracted. In this paper, three criteria are used to select the best WPT basis, namely: (1) distance criterion, (2) divergence criterion and (3) entropy criterion. Three algorithms to implement the above criteria are also provided. Experiments are conducted and the positive results are obtained.

Wavelet-based techniques are suitable for recovering a signal corrupted by noise. The time- and frequency-localization capabilities of wavelets provide better noise reduction and less signal distortion than conventional filtering methods. The noise reduction technique used in this paper is based on the hidden Markov model (HMM) structure, which can efficiently shape the statistical characteristics of practical data. As confirmed by numerical results, the HMM based approach provides a significant performance improvement over competing methods.