Department of Computer Science and Information Systems

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Optimal use of polarimetric signature on PALSAR-2 data for land cover classification
(IEEE, 217) Phartiyal, Gopal Singh
SAR data is playing key role in monitoring, the current status or change in, the land cover. For unsupervised SAR image classification, polarization signatures can play a significant role. Since it is difficult to obtain specific polarization signature of real land cover, it is customary to represent them with standard canonical structures polarization signatures. A critical analysis of the complex signatures of real targets is essential thereafter it is also a challenge to decide the thresholds or class boundary value on the correlation images. Therefore, in this paper an attempt has been made to critically analyze the polarimetric signature of complex targets and based on the correlation image analysis an OTSU multi-thresholding based approach is proposed to decide the individual class boundary values which will finally help in building a decision tree (DT) based classification technique. For this purpose L band fully polarimetric SAR data (PALSAR-2) has been used. DT class thresholds are computed using OTSU multi-thresholding method, scatter plot method, and a priori information. Obtained results reveal that complementary features like polarization signatures can help in identification as well as classification of land surface objects significantly by the proposed method.
Segmentation of range images: an orthogonal moment-based integrated approach
(IEEE, 1993) Ghosal, Sugata
A new approach to range image segmentation is presented. The proposed approach involves two phases in which the region and edge information detected using a set of orthogonal Zernike moment-based operators are combined to provide robust segmentation of range images. In the first phase, each range image point is characterized by the surface normal vector and the depth value at that point. A surface feature-based clustering of range image points yields its initial region-based segmentation. This initial segmentation phase often produces oversegmented images. In the second phase of the proposed technique, the oversegmented image is resegmented by appropriately merging adjacent regions using the edge information to produce final segmentation. One attractive characteristic of the proposed technique is that the same set of three moment-based operators is used to extract both surface and edge features. Thus only three convolution operations are needed at an image point to compute all the desired surface and edge features associated with that point. The performances of the proposed Zernike moment-based operators in surface and edge feature detection are theoretically analyzed
Orthogonal moment operators for subpixel edge detection
(Elsevier, 1993-02) Ghosal, Sugata
A new approach to detect step edges with subpixel accuracy is presented. The proposed approach is based on a set of orthogonal complex moments of the image known as Zernike moments. An ideal two-dimensional (2D) step edge is modeled in terms of four parameters: the background gray level, the step size, the distance of the edge from the center of the mask, and the orientation of the edge. Discrete Zernike moments are used to obtain a total of three complex masks to compute all the edge parameters for subpixel detection. For pixel-level edge detection only two masks (one real and one complex) are required. The theoretical analysis of the influence of noise on the location and the orientation of an edge is presented. This analysis reveals that the accuracy of the proposed approach is virtually unaffected by the additive noise. The technique is effective in detecting both the pixel-level and subpixel-level edges. Experimental results are presented to demonstrate the efficacy of the proposed technique.
Detection of composite edges
(IEEE, 1994-01) Ghosal, Sugata
The paper presents a new parametric model-based approach to high-precision composite edge detection using orthogonal Zernike moment-based operators. It deals with two types of composite edges: (a) generalized step and (b) pulse/staircase edges. A 2-D generalized step edge is modeled in terms of five parameters: two gradients on two sides of the edge, the distance from the center of the candidate pixel, the orientation of the edge and the step size at the location of the edge. A 2-D pulse/staircase edge is modeled in terms of two steps located at two positions within the mask, and the edge orientation. A pulse edge is formed if the steps are of opposite polarities whereas a staircase edge results from two steps having the same polarity. Two complex and two real Zernike moment-based masks are designed to determine parameters of both the 2-D edge models. For a given edge model, estimated parameter values at a point are used to detect the presence or absence of that type of edge. Extensive noise analysis is performed to demonstrate the robustness of the proposed operators. Experimental results with intensity and range images are included to demonstrate the efficacy of the proposed edge detection technique as well as to compare its performance with the geometric moment-based step edge detection technique and Canny's (1986) edge detector
Forced axisymmetric response of linearly tapered circular plates
(Elsevier, 1994-05) Goyal, Navneet
Forced axisymmetric response of a circular plate of linearly varying thickness, based on the classical theory, is analyzed by the eigen-function method. An exact solution for the free vibration mode shapes is obtained by the Frobenius method. Clamped and simply-supported plates subjected to symmetric uniformly distributed and concentrated impulsive ring and point loads are solved as example problems. Numerical results computed for transverse deflection and radial stress are plotted in the figures.
Forced Motion of a Semi-Infinite Plate of Linearly Varying Thickness
(Elsevier, 1994-06) Goyal, Navneet
Forced motion of a semi-infinite plate of linearly varying thickness based on classical theory is analyzed by an eigenfunction method. Uniformly distributed and concentrated impulsive loads applied to plates clamped at both the edges and cantilever plates are taken as example problems. Numerical results are computed for the transverse deflection and the bending moment of the plate.
Effect Of Transverse Shear And Rotatory Inertia On The Forced Motion Of A Plate-Strip Of Linearly Varying Thickness
(Elsevier, 1994-07) Goyal, Navneet
Shear theory and the eigenfunction method are used to analyze the forced motion of a plate-strip of linearly varying thickness. A plate clamped at both edges and a cantilever plate subjected to uniformly distributed and concentrated impulsive loads are analyzed as example problems. Numerical results computed for transverse deflection and bending moment of the plate are compared with those of classical theory.
Range surface characterization and segmentation using neural networks
(Elsevier, 1995) Ghosal, Sugata
This paper presents an integrated neural net-based approach to the segmentation of range images into distinct surfaces, which is an essential step in range image analysis and interpretation. A two-stage connectionist neural net model is proposed which extracts local surface features at each image point and groups pixels via local interactions among different features. The first stage computes surface parameters, e.g., surface normals, curvature and discontinuities (crease and jump) by optimally projecting the local range profile onto a set of non-orthogonal basis functions. In the second stage, adjacent pixels compete with each other based on the surface features associated with them to group themselves into different surface patches. Daugman's projection neural net (DPNN) and Kohonen's self-organizing neural net (KSNN) are used for the feature extraction and region-growing, respectively. Empirical performance analysis shows that the feature extraction using neural net is quite robust with respect to the additive noise. Experimental results are included to demonstrate the performance of the proposed technique.
Target detection in foveal ATR systems
(IEEE, 1996) Ghosal, Sugata
Automatic target recognition (ATR) applications require simultaneously a wide field of view (FOV) for better detection and situation awareness, high resolution for target recognition and threat assessment, and high frame rate for detecting brief events and disambiguating frame-to-frame correlation. Uniformly sampling the entire FOV at recognition resolution is simply wasteful in ATR scenarios with localized regions of interest (ROIs). Foveal data acquisition with space-variant sampling and context-sensitive sensor articulation is highly optimized for active ATR applications. We propose a multiscale local Zernike filter-based front end target detection technique for a commercially feasible foveal sensor topology with piecewise constant resolution profile. Anisotropic heat diffusion is employed for preprocessing of the foveal data. Expansion template matching is used to derive a detection filter that optimizes the discriminant signal-to-noise ratio (SNR). Results are presented with simulated foveal imagery, derived from real uniform acuity FLIR data.
A fast scalable algorithm for discontinuous optical flow estimation
(IEEE, 1996-02) Ghosal, Sugata
Multiple moving objects, partially occluded objects, or even a single object moving against the background gives rise to discontinuities in the optical flow field in corresponding image sequences. While uniform global regularization based moderately fast techniques cannot provide accurate estimates of the discontinuous flow field, statistical optimization based accurate techniques suffer from excessive solution time. A 'weighted anisotropic' smoothness based numerically robust algorithm is proposed that can generate discontinuous optical flow field with high speed and linear computational complexity. Weighted sum of the first-order spatial derivatives of the flow field is used for regularization. Less regularization is performed where strong gradient information is available. The flow field at any point is interpolated more from those at neighboring points along the weaker intensity gradient component. Such intensity gradient weighted regularization leads to Euler-Lagrange equations with strong anisotropies coupled with discontinuities in their coefficients. A robust multilevel iterative technique, that recursively generates coarse-level problems based on intensity gradient weighted smoothing weights, is employed to estimate discontinuous optical flow field. Experimental results are presented to demonstrate the efficacy of the proposed technique.
A moment-based unified approach to image feature detection
(IEEE, 1997-06) Ghosal, Sugata
In this paper, a novel model-based approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a piecewise smooth parametric model is developed to characterize the local intensity function in an image. Projections of the intensity profile onto a set of orthogonal Zernike-moment-generating polynomials are used to estimate model-parameters and, in turn, generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of proposed feature detectors.
Efficient algorithms for delay-bounded minimum cost path problem in communication networks
(IEEE, 1998) Narang, Nishit
As the amount of data transmitted over a network increases and high bandwidth applications requiring point to multipoint communications like videoconferencing, distributed database management or cooperative work become widespread, it becomes very important to optimize network resources. One such optimization is multicast tree generation. The problem of generating a minimum cost multicast tree given the network topology and costs associated with the connecting links can be modelled as a Steiner tree problem which is NP-complete. Much work has been done in the direction of obtaining near-optimal multicast trees when the objective is only to minimize the cost. However, many real time applications such as videoconferencing require that data be sent within prespecified delay limits in order to avoid problems such as anachronism and lack of synchronization. We deal with the delay-bounded cost-optimal multicast tree (DBCPAT) generation problem. Specifically, we discuss a closely related problem which is to find a delay-bounded cost-optimal path (DBCP) between a specified source and destination node. Such a path can be used as a starting point to solve the DBCMT. We present an exact solution to the DBCP which is based on the branch-and-bound paradigm. We also propose a heuristic technique to solve the DBCP using the principle of evolutionary computing. The results obtained using the two techniques are compared for several large networks.
Efficient Algorithms for Delay-bounded Multicast Tree Generation for Multimedia Applications
(Springer, 1999) Narang, Nishit
Given a network topology and costs associated with the links, the problem of generating a minimum cost multicast tree can be modelled as a Steiner tree problem. However, many real time applications such as video-conferencing require that data be sent within prespecified delay limits in order to avoid problems such as anachronism and lack of synchronization. This paper deals with the delay-bounded cost-optimal multicast tree (DBCMT) generation problem. A closely related problem is to find a delay-bounded cost-optimal path (DBCP) between a specified source and destination node. Such a path can be used be used as a starting point to solve the DBCMT. We present here two heuristics for building delay constrained multicast trees which have near optimal cost. A comparison of our heuristics with other proposed heuristics is also presented.
Forced asymmetric response of linearly tapered circular plates.
(Elsevier, 1999-03) Goyal, Navneet
The eigenfunction method is used to analyze the asymmetric response of linearly tapered circular plates subjected to transverse loads, uniformly distributed over an annular sectorial area of the plate. The analysis is based on the classical plate theory. Numerical results are presented graphically for the transverse deflection and stresses of the plate for various combinations of plate and loading parameters. Results obtained, as a particular case, for a plate of constant thickness subjected to an off-center half-sine pulse point load are compared with previously published results and found to match exactly
An image retrieval system with automatic query modification
(IEEE, 2002-06) Ghosal, Sugata
Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.
Automatic substructuring for domain decomposition using neural networks
(IEEE, 2002-08) Ghosal, Sugata
Application of neural networks for guiding solutions of large numerical problems is an emerging area of research. Automatic generation of subdomains from large 3D finite element meshes is a key preprocessing step in domain decomposition techniques and extremely important for proper load balancing, reducing communication bandwidth and latency, and efficient processor coordination and synchronization in a parallel computing environment. It is desired that the subdomains are approximately of same size, and the total number of interface nodes between adjacent subdomains is minimal. We propose two neural network algorithms employing the philosophy of competitive learning and Hopfield network, that can automatically generate substructures from large 3D meshes with reasonable speed. Both these techniques are implemented in such as a way that they have almost linear complexity w.r.t. the problem size for serial execution. Experimental results show more than 25% improvement over an existing greedy algorithm
Adaptable Similarity Search using Non-Relevant Information
(Elsevier, 2002-08) Ghosal, Sugata
This chapter presents a novel technique for improving the accuracy of adaptable similarity based retrieval by incorporating negative relevance judgment, and demonstrates excellent performance and robustness of the proposed scheme with a large number of experiments. Many modern database applications require content-based similarity search capability in numeric attribute space. Therefore, online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the similarity metric, without taking into account the information about non-relevant (or unsatisfactory) items. Consequently, items in database close to non-relevant ones continue to be retrieved in further iterations. A decision surface is determined to split the attribute space into relevant and non-relevant regions. The decision surface is composed of hyperplanes, each of which is normal to the minimum distance vector from a non-relevant point to the convex hull of the relevant points.
Leveraging non-relevant images to enhance image retrieval performance
(ACM Digital Library, 2002-12) Ghosal, Sugata
Inherent subjectivity in user's perception of an image has motivated the use of relevance feedback (RF) in the image desigined output's retrieval process. RF techniques interactively determine the user's query concept, given the user's relevance judgments on a set of images. In this paper we propose a robust technique that utilizes non-relevant images to efficiently discover the relevant search region. A similarity metric, estimated using the relevant images is then used to rank and retrieve database images in the relevant region. The partitioning of the feature space is achieved by using a piecewise linear decision surface that separates the relevant and non-relevant images. Each of the hyperplanes constituting the decision surface is normal to the minimum distance vector from a non-relevant point to the convex hull of relevant points. Experimental results demonstrate significant improvement in retrieval performance for the small feedback size scenario over two well established RF algorithms.
3G Mobile Networks: Architecture, Protocols and Procedures
(McGraw Hill, 2004) Narang, Nishit
In India, the mobile subscriber baser is increasing at a phenomenal rate. After the successful adoption of Second Generation (2G) Technology GSM and 2.5G Technology GPRS, the industry is now rapidly moving towards Third Generation (3G) Networks. The book, written by two young engineers, touches almost every imaginable aspect of a 3G Network, spanning across topics such as: • UMTS Network Architecture (including Access Network and Core Network), • Protocols (including RRC, NBAP, RANAP, MM/GMM, MAP and GTP), • Procedures (including UTRAN Procedures, Mobility Management, Call/Session handling and Security Management), and • Services (including Supplementary Services and Value-added Services). Also the book covers topics like IP Multimedia Sub-system (IMS) and SIGTRAN. Besides these, the book includes the status of deployment of 3G UMTS Networks across the world and provides a brief introduction to 4G Networks setting the tone for future advancements. With this coverage, the book would serve the needs of telecom engineers and students.
Communication Networks: Principles and Practice
(2005) Narang, Nishit
Communication Networks: Principles and Practice is a simple and jargon-free presentation on the core concepts of networking. The book adopts a novel approach, wherein each chapter first details a particular concept of networking and then explains it using examples from contemporary technologies like TCP/IP, ATM, 3G Networks, etc.