Department of Computer Science & Engineering

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IIT Jodhpur

Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable. © 2011 Elsevier B.V.

World University root node

Applied Soft Computing Journal

Tissue classification in magnetic resonance images through the hybrid approach of Michigan and Pittsburg genetic algorithm

Optimal placement of visual sensors along with good lighting conditions is indispensable for the successful execution of surveillance applications. Limited field-of-view, depth-of-field, occlusion due to presence of different objects in the scene form the major constraints for visual sensor placement. While over/under exposed objects, shadowing and light rays directly incident on the camera lens are some of the constraints for light source placement. Because of the nature of the constraints and complexity of the problem, the placement problem is considered to be a multi-objective global optimization problem. The paper outlines the camera and light source location optimization problem with multiple objective functions. Multi-Objective Genetic Algorithm is used to maximize the camera coverage with optimum illumination of the sensing space. © 2011 IEEE.

Proceedings - 2011 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2011

Camera and light source placement: A multi-objective approach

Inspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy. © 2011 IEEE.

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

An MRF model for binarization of natural scene text

Page segmentation is a basic step in any character recognition system. Its failure is one of the major causes for deteriorating overall accuracy of the current Indian language OCR engines. Many segmentation algorithms are proposed in literature. Often these algorithms fail to adapt dynamically to a given page and thus tend to yield poor segmentation for some specific regions or some specific pages. Given the ground truth, locating page segmentation errors is a straight foreword problem and merely useful for comparing segmentation algorithms. In this work, we locate segmentation errors without directly using the ground truth. Such automatic localization of page segmentation errors can be considered a major step towards improving page segmentation errors. In this work, we focus on localizing line level segmentation errors. We perform experiments on more than 18000 scanned pages of 109 books belonging to four prominent south Indian languages. Copyright © 2011 ACM.

ACM International Conference Proceeding Series

Automatic localization of page segmentation errors

A novel technique for binarization of degraded documents is proposed. It works in a multi-scale framework with an adaptive-cum-interpolative thresholding as a modification of Otsu's method. Instead of computing a global threshold value for an input document image, it computes the local threshold values for a small set of grid points by observing the intensity pattern of the pixels lying in the concerned grid cells. Thresholds estimated for these grid points are used, in turn, to compute the threshold values of all the remaining pixels using a fast-yet-efficient interpolation procedure. To handle noises in degraded images, this grid-based adaptive thresholding is applied in successively reducing scales to obtain the nearoptimal binarization as a set of connected components. After a post-processing with these connected components, we get the final output. Exhaustive experimentation has been carried out with benchmark datasets including George Washington corpus of handwritten documents, and also with our own datasets. When compared to other methods, the proposed method is found to be robust and appreciably better, as tested by conventional evaluation schemes. © 2011 IEEE.

ICIIP 2011 - Proceedings: 2011 International Conference on Image Information Processing

Robust binarization of degraded documents using adaptive-cum-interpolative thresholding in a multi-scale framework

Digital skeleton of character images, generated by thinning method, has a wide range of applications for shape analysis and classification. But thinning of character images is a big challenge. Removal of spurious strokes or deformities in thinning is a difficult problem. In this paper, we propose a contour-based thinning method used for performing skeletonization of printed noisy isolated character images. In this method, we use shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shapes of the character images. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English, Bengali, Hindi, and Tamil characters and we obtain much better results comparing with other thinning methods without any post-processing. © 2011 Elsevier B.V. All rights reserved.

Pattern Recognition Letters

An improved contour-based thinning method for character images

A convergecast is a process in which all the sensor nodes sense data and fuse them to forward to a base station. A correct data gathering requires that there is no data loss or delivery of redundant data. Nodes can form a spanning tree rooted at the sink to perform the convergecast. Leaves of the tree can sense and forward data independently. But for any internal node data can be forwarded to its parent only after receiving data from all its children. It has been observed that a Breadth-First-Search (BFS) tree is a better choice for convergecast under low system load because the depth of any node from the root is always minimum; whereas under higher system load condition a Depth-First-Search (DFS) tree may be a better option as the degree of any node in a DFS tree is lower than that in a BFS tree. Hence per node load is lower in case of a DFS tree than that of a BFS tree. So to meet the requirement of load based adaptation, a dynamic tree switching algorithm has been proposed in this paper. The convergecast application remains transparent of the switching assuring the availability of the system at any instance of time. Also each convergecast message is assured to be delivered correctly to the base station without any loss or redundancy. © 2011 Springer-Verlag.

Communications in Computer and Information Science

A novel approach for adaptive data gathering in sensor networks by dynamic spanning tree switching

Query driven Broadcast through wireless sensor nodes also leads to the domain of event driven converge cast. A query-response based application in Wireless Sensor Networks(WSN) demands the correct delivery of data message at each sensor node. A Breadth-First Search(BFS) tree rooted at the base station offers shortest path traversal for each data message which utilizes the sensor resources efficiently. Resource constrained sensor nodes are highly prone to sudden crash. So the application demands a quick and smart approach to repair the tree when a node dies. In this paper a novel scheme has been proposed to locally repair the tree with constant round of message transmissions. Each node piggybacks a few bytes of extra information along with each query and response messages. Based on these piggybacked values each node calculates its alternate parent. When a parent node fails, its children can contact their respective alternate parents immediately to establish an alternate path to the root. Reduced communication cost in terms of extra message transmissions saves battery power at each node. Efficient query-response message handler ensures the correct delivery of messages. Fast repairing offers good Quality of Service(QoS). Simulation result shows that no message is lost except the one holding by the crashed node. © 2011 IEEE.

Proc. - 14th IEEE Int. Conf. on Computational Science and Engineering, CSE 2011 and 11th Int. Symp. on Pervasive Systems, Algorithms, and Networks, I-SPA 2011 and 10th IEEE Int. Conf. on IUCC 2011

A tree-based local repairing approach for increasing lifetime of query driven WSN

In this paper, we propose a real-time distributed framework for composite event recognition in a calibrated pan-tilt camera network. A composite event comprises of events that occur simultaneously or sequentially at different locations across time. Distributed composite event recognition requires distributed multi-camera multi-object tracking and distributed multi-camera event recognition. We apply belief propagation to reach a consensus on the global identities of the objects in the pan-tilt camera network and to arrive at a consensus on the event recognized by multiple cameras simultaneously observing it. We propose a hidden Markov model based approach for composite event recognition. We also propose a novel probabilistic Latent Semantic Analysis based algorithm for pair-wise interaction recognition and present an application of our distributed composite event recognition framework, where the events are interactions between pairs of objects. © 2010 ACM.

Distributed framework for composite event recognition in a calibrated pan-tilt camera network

Traffic monitoring/prediction using a distributed camera network is presented in this paper. The activities on each road link are monitored and features are derived to identify the pattern. Then it is learnt, classified, predicted and communicated to neighboring road links. We used GMM-EM based classification and HMM based prediction. Optimum path is determined by assigning proportional weights to the predicted states of the connected road links. The proposed method is neither based on tracking nor on vehicle detection. Apart from this the method is flexible, adaptive, robust and computationally light. Unlike the existing methods it does not assume or draws analogies of traffic moving as particles, neither does it impose restriction on road conditions or road tributaries and distributaries. The model is validated using traffic simulator and tested on real road network. © 2010 ACM.