Showing papers by "Svetha Venkatesh published in 1998"

Robot navigation inspired by principles of insect vision

Abstract: Recent studies of insect visual behaviour and navigation reveal a number of elegant strategies that can be profitably applied to the design of autonomous robots. The “peering” behaviour of grasshoppers, for example, has inspired the design of new rangefinding systems. The “centring” response of bees flying through a tunnel has led to simple methods for navigating through corridors. These and other visually-mediated insect behaviours are described along with a number of applications to robot navigation.

Noetica: a tool for semantic data modelling

Abstract: Noetica is a tool for structuring knowledge about concepts and the relationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly-typed semantic network. By providing a rich type system it is possible to represent conceptual information using formalised structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in “free” semantic networks and gives the ability to easily extend a knowledge model while retaining its semantics. We also provide visualisation and query tools for this data model. Visualisation can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualise the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and path-finding queries (where the system find relationships between objects in the database by following links).

Efficient Graph Matching for Video Indexing

Abstract: Traditionally graph algorithms have been of restricted use due to their exponential computational complexity in the general case. Recently a new class of graph algorithms for subgraph isomorphism detection has been proposed, one of these algorithms having quadratic time complexity. These new algorithms use a preprocessing step to allow rapid matching of an input graph against a database of model graphs. We present a new algorithm for largest common subgraph detection that provides a significant performance improvement over previous algorithms. This new algorithm is based on the work on preprocessed subgraph isomorphism detection by Messmer and Bunke [3].

Investigation of a prefetch model for low bandwidth networks

Abstract: We investigate speculative prefetching under a model in which prefetching is neither aborted nor preempted by demand fetch but instead gets equal priority in network bandwidth utilisation. We argue that the non-abortive assumption is appropriate for wireless networks where bandwidth is low and latency is high, and the non-preemptive assumption is appropriate for Internet where prioritization is not always possible. This paper assumes the existence of an access model to provide some knowledge about future accesses and investigates analytically the performance of a prefetcher that utilises this knowledge. In mobile computing, because resources are severely constrained, performance prediction is as important as access prediction. For uniform retrieval time, we derive a theoretical limit of improvement in access time due to prefetching. This leads to the formulation of an optimal algorithrn for prefetching one access ahead. For non-uniform retrieval time, two different types of prefetching of multiple documents, namely mainline and branch prefetch, are evaluated against prefetch of single document. In mainline prefetch, the most probable sequence of future accesses is prefetched. In branch prefetch, a set of different alternatives for future accesses is prefetched. Under some conditions, mainline prefetch may give slight improvement in user-perceived access time over single prefetch with nominal extra retrieval cost, where retrieval cost is defined as the expected network time wasted in non-useful prefetch. Branch prefetch performs better than mainline prefetch but incurs more retrieval cost.

Classifying and detecting group behaviour from visual surveillance data

Abstract: We outline an approach to classifying and detecting behaviours from surveillance data. Simple pairwise movement patterns are learned and used as building blocks to describe behaviour over a temporal sequence, or compared with other pairs to detect group behaviour. As the pair primitives are easy to redefine and learn, and complex behaviour over time is specified by the user as a sequence of pair primitives, this approach provides a flexible yet robust method of detecting complex movement in a wide variety of domains.

An insect-based approach to robotic homing

Abstract: Many animals, including insects, successfully engage in visual homing. We describe a system that allows a mobile robot to home. Specifically we propose a simple extension to our original homing scheme which significantly improves its performance by incorporating a richer view of the environment. The addition of landmark apparent-size cues assists homing by providing a more robust homing vector as well as providing a simple and effective method of reinforcing landmark avoidance. The homing algorithm allows a mobile robot to incrementally home by moving in such a way as to gradually reduce the discrepancy between the current view and the view obtained from the home position. Both simulation and mobile robot experiments are used to demonstrate the feasibility of this approach. By matching the bearings of features extracted from panoramic views and using a vector summation technique to compute a homing vector we are able to provide a simple, parsimonious and robust robotic homing algorithm.

An efficient least common subgraph algorithm for video indexing

Abstract: Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.

Combining NL processing and video data to query American Football

Abstract: We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain, and a recognition system which uses the model to recognise American Football plays.

Efficient graph matching for video indexing

Abstract: Traditionally graph algorithms have been of restricted use due to their exponential computational complexity in the general case. Recently a new class of graph algorithms for subgraph isomorphism detection has been proposed, one of these algorithms having quadratic time complexity. These new algorithms use a preprocessing step to allow rapid matching of an input graph against a database of model graphs. We present a new algorithm for largest common subgraph detection that provides a significant performance improvement over previous algorithms. This new algorithm is based on the work on preprocessed subgraph isomorphism detection by Messmer and Bunke [3].

Extracting common subtrees from decision trees

Abstract: This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning.

QOS Filtering for Video on Wireless Networks

Abstract: Video-on-demand in wireless computing environments is an important application area and several issues have been addressed by researchers in recent times. In this paper we investigate various filtering mechanisms to provide quality of service in video transmissions to mobile clients under low and varying bandwidths. We present design and implementation issues of two novel filtering schemes. Our experimental results on a real wireless network have produced encouraging results.