The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

American Society for Testing and Materials

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Magnetic resonance imaging apparatus

This paper addresses the problem of rain streak removal from a single image. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision algorithms. Rain streak removal can be formulated as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either dictionary learning methods or impose a low rank structure on the appearance of the rain streaks. While these methods can improve the overall visibility, they tend to leave too many rain streaks in the background image or over-smooth the background image. In this paper, we propose an effective method that uses simple patch-based priors for both the background and rain layers. These priors are based on Gaussian mixture models and can accommodate multiple orientations and scales of the rain streaks. This simple approach removes rain streaks better than the existing methods qualitatively and quantitatively. We overview our method and demonstrate its effectiveness over prior work on a number of examples.

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Rain Streak Removal Using Layer Priors

A nonlinear energy operator (NEO) gives an estimate of the energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. Here, the authors give a new interpretation of NEO and extend its use in stochastic signals. They show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEGs).

A new interpretation of nonlinear energy operator and its efficacy in spike detection

Tool condition monitoring is gaining a parallel development with the advancement of automatic manufacturing processes in the last thirty years due to the increasing need for improvement of product quality. The advances of digital image processing techniques used in tool condition monitoring are an important research interest due to the improvement of machine vision system, computing hardware and non-tactile application. In this paper, a review of development of digital image processing techniques in tool condition monitoring is discussed and finally a conclusion is drawn about required systematic research in this field.

Application of digital image processing in tool condition monitoring: A review

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

/pdf/a-combination-of-shape-and-texture-features-for-3wcrq6xm0y.pdf

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

In this paper, the algorithms for the detection and removal of rain from videos have been reviewed. Rain reduces the visibility of scene and thus performance of computer vision algorithms which use feature information. Detection and removal of rain requires the discrimination of rain and nonrain pixels. Accuracy of the algorithm depends upon this discrimination. Here merits and demerits of the algorithms are discussed, which motivate the further research. A rain removal algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

Removal of rain from videos: a review

In this study, a novel and efficient fog removal algorithm is proposed. Fog formation is because of attenuation and airlight. Attenuation reduces the contrast and airlight increases the whiteness in the scene. Proposed algorithm uses anisotropic diffusion to recover scene contrast. Simulation results demonstrate that proposed algorithm outperforms prior state-of-the-art algorithms in terms of contrast gain, percentage of number of saturated pixels and computation time. Proposed algorithm is independent of the density of fog and does not require user intervention. It can handle colour as well as grey images. Along with the RGB (red, blue and green) colour model, proposed algorithm can work for HSI (hue, saturation and intensity) model which further reduces the computation. Proposed algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

Sudipta Mukhopadhyay

Papers

A new interpretation of nonlinear energy operator and its efficacy in spike detection

Application of digital image processing in tool condition monitoring: A review

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

Removal of rain from videos: a review

Single image fog removal using anisotropic diffusion