Bhabatosh Chanda

Multiscale morphological segmentation of gray-scale images

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.

A multiscale morphological approach to local contrast enhancement

Abstract A scheme for enhancing local contrast of raw images based on multiscale morphology is presented in this paper. The conventional theoretical concept of local contrast enhancement has been extended in the regime of mathematical morphology. The intensity values of the scale-specific features of the image extracted using multiscale tophat transformation are modified for achieving local contrast enhancement. Locally enhanced features are combined to reconstruct the final image. The proposed algorithm has been executed on a set of raw images for testing its efficacy and the result has been compared with that of other standard methods for getting idea about its relative performance.

A simple and efficient algorithm for multifocus image fusion using morphological wavelets

This paper presents a simple yet efficient algorithm for multifocus image fusion, using a multiresolution signal decomposition scheme. The decomposition scheme is based on a nonlinear wavelet constructed with morphological operations. The analysis operators are constructed by morphological dilation combined with quadratic downsampling and the synthesis operators are constructed by morphological erosion combined with quadratic upsampling. A performance measure based on image gradients is used to evaluate the results. The proposed scheme has some interesting computational advantages as well.

Enhancing effective depth-of-field by image fusion using mathematical morphology

Abstract Reduced depth-of-field (DOF) poses a problem in the light optical imaging system, since the objects present outside this zone appear blurry in the recorded image. The effective DOF of the sensor may be enhanced considerably without compromising the quality of the image by fusing images captured with different focused regions. This paper presents an image fusion technique suitable for combining multifocus images of a scene. The method employs morphological filters to select sharply focused regions from various images and then combines them together to reconstruct the image in which all the regions are properly focused. A performance measure based on image gradients is used to compare the results obtained by the proposed method with those obtained by other image fusion techniques.

Writer-independent off-line signature verification using surroundedness feature

The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database.

Topology preservation in 3D digital space

Abstract In a 3D two-tone digital space, the conditions under which sequential transformation of an object point to a non-object point, or its inverse, does not affect the image topology are investigated. The concept of border-connectivity is introduced and an efficient algorithm for checking a simple point is proposed. In this connection, some other interesting properties of 3D digital geometry are also discussed. This investigation may be used in thinning and topology preserving morphological transformations in 3D digital space.

Fusion of 2D grayscale images using multiscale morphology

Abstract A scheme for fusion of multi-sensor 2D images based on multiscale morphology is presented in this paper. A point-based registration, using affine transformation is performed prior to fusion. The scale-specific features are extracted from both the images using the morphological towers constructed in course of various types of multiscale filtering. Extracted features are combined to get the fused image. A quantitative measure of the degree of fusion is estimated by cross-correlation coefficient and the error measure obtained by eigenvector fitting between the fused image and each of the constituting images.

Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures.

An edge preserving noise smoothing technique using multiscale morphology

This paper presents a method for improving the quality of gray-level images by reducing the effect of noise using multiscale morphology. The underlying concept of the work is to assign progressively less importance to features of smaller scales as their possibilities of being noise particles are more. Features at various scales are extracted by means of morphological filtering. The proposed scheme is first illustrated in one dimension. Morphological towers are built to implement the method in two dimensions. The proposed algorithm has been tested on a set of real images corrupted with different types of noise. The results are compared with those of other standard noise removal algorithms based on some standard performance measures. A modification of the method considering noise statistics along with its results are also presented in this paper.

A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters

We have presented a unified model for detecting different types of video shot transitions. Based on the proposed model, we formulate frame estimation scheme using the previous and the next frames. Unlike other shot boundary detection algorithms, instead of properties of frames, frame transition parameters and frame estimation errors based on global and local features are used for boundary detection and classification. Local features include scatter matrix of edge strength and motion matrix. Finally, the frames are classified as no change (within shot frame), abrupt change, or gradual change frames using a multilayer perceptron network. The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.