S. Subramanian

Bio: S. Subramanian is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Pattern recognition (psychology) & Matching (graph theory). The author has an hindex of 2, co-authored 7 publications receiving 20 citations.

Recognition of partial planar shapes in limited memory environments

Abstract: Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.

An abductive reasoning based image interpretation system

Abstract: This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.

A Framework for Image Interpretation in a Distributed Problem Solving Environment

Abstract: This paper presents a framework for constructing image interpretation in a distributed problem solving environment. The interpretation of an image is constructed using abductive inferencing scheme. The inferencing mechanism ensures generation of interpretations of the image which can account for the features detected in the image in the most consistent manner. Two different decomposition strategies have been discussed for identifying the eubproblems. Strategies have been formulated for solving the subproblems and integrating the partial results in a parallel and distributed fashion. Correctness and validity of the strategies have been proved. A black board model based distributed architecture have been shown to satisfy the requirements of the problem solving technique.

View-invariant regions and mobile robot self-localization

Abstract: This paper addresses the problem of mobile robot self-localization given a polygonal map and a set of observed edge segments. The standard approach uses interpretation tree search with pruning heuristics to match observed edges to map edges. Our approach introduces a preprocessing step in which the map is decomposed into view-invariant regions (VIRs). The VIR decomposition captures information about map edge visibility, and can be used for a variety of robot navigation tasks.