Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should reflect species richness on a given zone. However, in a high spectral mixing scenario, an additional unmixing step, just before entropy computation, is required; cluster centroids are enough for the unmixing process. Entropies computed using the proposed method correlate well with the ones calculated directly from synthetic and field data.

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain–computer interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes steady-state visually evoked potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital Electroencephalographic (EEG) responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40, and band power declined throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20–40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age-related cognitive decline.

A Gabor filtering method for texture-based classification of color images is presented. The algorithm is robust and can be used with different color representations. It involves a filter selection process based on texture smoothness. Unichannel and interchannel correlation features are computed. Two types of color representations have been considered: computing chromaticity values from xyY, HIS, and RGB spaces; and using the three channels of the perceptually uniform color spaces L^*a^*b^* and HSV. The RGB space universally used in image processing can be used for color-texture-based classification by computing the rgb chromaticity values, which yield higher classification accuracies than the direct use of R, G, and B values. The wavelet transform methods have been extended to perform color image classifications with additional features. The two approaches, Gabor filtering and wavelet transform methods, are compared in terms of classification accuracy and efficiency. The pyramid wavelet transform (PWT) performs well with all color spaces. The tree-structured wavelet transform (TWT) is more suitable for smaller classification problems. The best color spaces for higher class problems with wavelet methods are L^*a^*b^* and HSV spaces. The HSV space is found to be the best for application of both of these texture-based approaches. Computationally the Gabor method followed by PWT is fast and efficient. © 2002 Society of Photo-Optical Instrumentation Engineers.

Spatial smoothing over the original hyperspectral data based on wavelet and partial differential equations (PDEs) are incorporated in the classifiers using composite kernel with kNN graphs. The kernels combine spectral-spatial relationships using the smoothed and original images. Experiments with real hyperspectral scenarios are presented. Comparison with recent graph based methods show that the proposed scheme improves existing methods.

This paper presents a performance analysis of the transputer T805 processor networks for implementing low-level image processing algorithms. The influence of communication on the performance of transputer networks is analyzed. The network implementation constraints for using transputer networks for image processing are discussed. The paper also presents the results of implementing a texture feature extraction algorithm, called the Spatial Gray Level Dependence Method (SGLDM), on transputer networks and the results are studied with respect to communication. This algorithm is used for image texture analysis. The algorithm estimates the second-order joint conditional probabilities, of transition from one gray level to another, between two pixels that are at a specific distance and at a specific angle to the horizontal axis. Many statistical texture features can be derived from the estimated co- occurrence matrices. The transputer networks are configured as hypercubes which provides embedded tree, ring, and mesh topologies. The algorithm is implemented on different transputer hypercube configurations with tree topologies mapped onto them. The communication overheads in parallel transputer networks have a major influence on the optimal number of processors that can be used in an application, and on the maximum speedup that can be achieved. By resolving the communication issues transputer based real-time image processing applications can be developed.

Classification of images requires extraction of optimal set of features. In this paper, a method that uses genetic algorithm creating texture descriptors on features computed from a feature extraction method is presented. A feature extraction algorithm is applied to a database of images and a training feature matrix is created. This matrix is updated by a dynamic algorithm, which finds the vectors most close to the real solution in the Euclidean norm. This set forms the texture descriptor which can be further used for classification of unknown samples. A weighted fitness function that selects best parents in each generation has been implemented. Examples of classification are presented with the features computed from a classification algorithm. Results show that the classification performance of the features improved after applying the genetic algorithm. The algorithm is cost efficient. This algorithm is also compared with that of the Learning Vector Quantization method which quantizes the training vectors to an optimal set of codebook vectors.