Review of: Lewis\u27 Dictionary of Toxicology (Robert A. Lewis, ed.; Lewis Publishers 1998)

Review of the book: Lewis\u27 Dictionary of Toxicology (Robert A. Lewis, ed.; Lewis Publishers 1998). About the author, acknowledgments, alphabetical listing of terms defined. ISBN 1-56670-223-2; [1127 pp. $84.95. Hardbound. 2000 Corporate Blvd. N.W., Boca Raton, FL 33431.

Color image segmentation using a spatial k-means clustering algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm

Georgia Welfare Leavers Study - Initial Results

Funded by the Department of Human Resources, the Georgia State welfare leavers study is tracking families as they leave Temporary Assistance to Needy Families (TANF). Using administrative data combined with the results of a telephone interview, the project monitors the impact of leaving welfare on the individuals, their families and their communities.2 The study includes both single-parent and child-only leavers and, unlike studies in some other states, does include individuals who have returned to the rolls. The response rate for this study approaches 35% and continues to rise as the project makes intensive efforts to locate respondents. Preliminary analyses of administrative data indicate that interview respondents closely resemble individuals whom the project has been unable to interview

Adaptive pre-filtering techniques for colour image analysis

One important step in the process of colour image segmentation is to reduce the errors caused by image noise and local colour inhomogeneities. This can be achieved by filtering the data with a smoothing operator that eliminates the noise and the weak textures. In this regard, the aim of this paper is to evaluate the performance of two image smoothing techniques designed for colour images, namely bilateral filtering for edge preserving smoothing and coupled forward and backward anisotropic diffusion scheme (FAB). Both techniques are non-linear and have the purpose of eliminating the image noise, reduce weak textures and artefacts and improve the coherence of colour information. A quantitative comparison between them will be evaluated and also the ability of such techniques to preserve the edge information will be investigated

Automatic segmentation of skin cancer images using adaptive color clustering

This paper presents the development of an adaptive image segmentation algorithm designed for the identification of the skin cancer and pigmented lesions in dermoscopy images. The key component of the developed algorithm is the Adaptive Spatial K-Means (A-SKM) clustering technique that is applied to extract the color features from skin cancer images. Adaptive-SKM is a novel technique that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The A-SKM has been included in the development of a flexible color-texture image segmentation scheme and the experimental data indicates that the developed algorithm is able to produce accurate segmentation when applied to a large number of skin cancer (melanoma) images

Georgia Welfare Leavers Study - Technical Appendices

The following provides an overview of the Georgia State study and compares it to that of leavers studies in other states. While there are similarities, this study differs in several crucial ways from these other studies

Color image segmentation using a self-initializing EM algorithm

This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM algorithm is highly dependent on the starting condition (initialization stage). Usually the initialization procedure selects the color seeds randomly and often this procedure forces the EM algorithm to converge to numerous local minima and produce inappropriate results. In this paper we propose a simple and yet effective solution to initialize the EM algorithm with relevant color seeds. The resulting self initialised EM algorithm has been included in the development of an adaptive image segmentation scheme that has been applied to a large number of color images. The experimental data indicates that the refined initialization procedure leads to improved color segmentation

Nearby main sequence stars with cool circumstellar material

The discovery of the so-called Vega phenomenon was one of the most important and unexpected results of the IRAS mission. Several nearby main sequence stars were found to possess clouds of solid grains emitting strongly in the far-IR. Three of these objects were marginally resolved by IRAS. This phenomenon appears to be widespread and not limited to proto-planetary epochs. Possible connection of this phenomenon to the existing of planets is discussed

Evaluation of local orientation for texture classification

The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation in the classification process. In this paper the orientation for each pixel in the image is extracted using the partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of the local dominant orientation (which is calculated by combining the magnitude and local orientation) on the classification results. While the dominant orientation of the texture depends strongly on the observation scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant orientations for all pixels in the image that sample the texture at micro-level. The experimental results were conducted on standard texture databases and the results indicate that the dominant orientation calculated at micro-level is an appropriate measure for texture description