Shantaram Vasikarla

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

Fuzzy edge detector using entropy optimization

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f/sub h/ and the crossover point x/sub c/, is used to enhance the image. The entropy function is optimized to obtain the parameters f/sub h/, and x/sub c/ using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, /spl alpha/ and /spl beta/. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

Performance evaluation of an incorporated DCT block-based watermarking algorithm with human visual system model

In this paper we propose a new approach to predict a fixed fidelity level of a watermarked image while increasing the detection rate. The new approach incorporates a DCT block-based algorithm with human visual system (HVS). The performance of the new approach has been defined in terms of its effectiveness (detection rate) and fidelity. We show experimentally how our novel approach can be used to control distribution of a watermark to get a higher detection rate as well as better fidelity.

Cluster-Weighted Modeling as a Basis for Non-Additive GFM

The cluster-weighted modeling (CWM) is a mixture density estimator around local models. To be specific, the input regions together with output regions are treated to be Gaussian serving as local models. These models are linked by a linear function involving the mixture of densities of local models. A connection between the CWM and generalized fuzzy model (GFM) is established in this work for utilizing the concepts of probability theory in deriving additive and non-additive fuzzy system versions of GFM

An Authentication System Based on Palmprint

In this paper, palmprint based authentication is presented. The palmprint image is acquired using an acquisition system developed at IIT Delhi. The Region of interest (ROI) is extracted from the palmprint image by finding a tangent of curves between fingers. The perpendicular bisector of this tangent divides the rectangular area enclosing the palmprint into two equal parts. The features extracted from the ROIs are used for matching. Two approaches are suggested for the feature extraction. In the first approach the ROI is divided into a suitable number of non-overlapping windows from which fuzzy features are extracted. In the second approach multi-scale wavelet decomposition is applied on the ROI and the detail images are combined to yield a superimposed image which is partitioned into non-overlapping windows. From these windows energy feature is extracted. The two sets features are used to determine the genuine and impostor scores. The results on 125 users show 99.2% with fuzzy feature and 94.4% with wavelet based feature.

Fuzzy-based parameterized Gaussian edge detector using global and local properties

Many edge detection schemes suffer from the lack of image quality at the global level. Global properties are more vital in grayscale images due to loss of hue and texture. This paper proposes a novel fuzzy-based Gaussian edge detector that uses both global and local image properties for grayscale images. In the global contrast intensification phase, each pixel in an image is represented in the fuzzy domain using a modified Gaussian membership function. A nonlinear contrast intensification function containing three parameters is used to further enhance the image. In the local phase, we present a novel fuzzy parameterized Gaussian-type edge detector mask containing two fuzzifier parameters, which are chosen based on experimental selection rules. Optionally, the fuzzy image entropy function can be used to optimize all the parameters through simple gradient descent technique. In experiments conducted on various classic images, this algorithm showed notable visual improvement on both strong and weak edges in comparison with common edge detectors.

A comparison of some clustering techniques via color segmentation

This paper proposes a new improved modified mountain clustering technique. The proposed technique is being compared with some existing techniques such as FCM, Gath-Geva, probabilistic clustering and modified mountain clustering. The performance of all these clustering techniques is compared by applying them to color segmentation in terms of cluster validity and computational complexity.

Fuzzy Edge and Corner Detector for Color Images

Localization of edges and corner points by fuzzy detectors in images is the main concern of this paper. This paper presents an approach to edge and corner detection based on fuzzy logic. In this approach SUSAN mask is employed to compute USAN area. The histogram of USAN area permits us construct type 1 and type 2 fuzzy membership functions by fuzzifying USAN area computed about every pixel in an image. The edge map of the image is obtained using adaptive thresholding. An attempt is made to extend the proposed approach to the detection of colour edges. Experiments show that this approach is less sensitive to noise without causing edge displacement.

Fuzzy Edge Detection in Biometric Systems

This paper proposes a fuzzy logic based edge detector for feature extraction in biometric systems such as face and palm print recognition. Edge detection is carried out by means of global (histogram of gray levels) and local (pixels within in a window) information. The local information is fuzzified by employing a modified Gaussian membership function. Using the contrast intensification operator, the image is enhanced to the required level of visual quality by entropy optimization of fuzzification parameters. Furthermore, the local edge detection operator is applied on the enhanced image using parameters obtained from entropy optimization. Finally, a simple threshold is applied to produce the skeleton image. Results demonstrate that this edge detector is well suited for feature extraction in biometric image systems.

Medical Image Segmentation Using Improved Mountain Clustering Technique Version-2

This paper proposes Improved Mountain Clustering version-2 (IMC-2) based medical image segmentation. The proposed technique is a more powerful approach for medical image based diagnosing diseases like brain tumor, tooth decay, lung cancer, tuberculosis etc. The IMC-2 based medical image segmentation approach has been applied on various categories of images including MRI images, dental X-rays, chest X-rays and compared with some widely used segmentation techniques such as K-means, FCM and EM as well as with IMC-1. The performance of all these segmentation approaches is compared on widely accepted validation measure, Global Silhouette Index. Also, the segments obtained from the above mentioned segmentation approaches have been visually evaluated.