Transformation invariance in hand shape recognition

In hand shape recognition, transformation invariance is key for successful recognition. We propose a system that is invariant to small scale, translation and shape variations. This is achieved by using a-priori knowledge to create a transformation subspace for each hand shape. Transformation subspaces are created by performing principal component analysis (PCA) on images produced using computer animation. A method to increase the efficiency of the system is outlined. This is achieved using a technique of grouping subspaces based on their origin and then organising them into a hierarchical decision tree. We compare the accuracy of this technique with that of the tangent distance technique and display the result

Dynamic gesture recognition using PCA with multi-scale theory and HMM

In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete Hidden Markov Models (DHMM). We use a hierarchical decision tree based on multiscale theory. Firstly we convolve all members of the training data with a Gaussian kernel, which blurs differences between images and reduces their separation in feature space. This reduces the number of eigenvectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space into two clusters using the k-means algorithm. Then the level of blurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided into two and the process is repeated. We thus produce a binary tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures in real time. The output of the decision tree is then input into DHMM to recognize temporal information

Bayesian fusion of hidden Markov models for understanding bimanual movements

Understanding hand and body gestures is a part of a wide spectrum of current research in computer vision and human-computer interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing hidden Markov models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity

A dynamic model for real-time tracking of hands in bimanual movements

The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the hands tend to be synchronised effortlessly. Different aspects of this synchronisation are the basis of our research to track the hands. The spatial synchronisation in bimanual movements is modelled by the position and the temporal synchronisation by the velocity and acceleration of each hand. Based on a dynamic model, we introduce algorithms for occlusion detection and hand tracking

Predictive text entry in immersive environments

One of the classic problems with immersive environments is data entry; with a head-mounted display (HMD) the user can no longer see the keyboard. Although for many applications data entry is not a requirement, for some it is essential: communicating in collaborative environments, entering a filename to which work can be saved, or accessing system controls. Combining data gloves and a graphically represented keyboard with a predictive spelling paradigm, we describe an effective text entry technique for immersive environments; we explore the key issues when using such a technique, and report the results of preliminary usability testing

Participatory research in natural resources

Abstract not availabl

Studies in optical super-resolution

SIGLEAvailable from British Library Document Supply Centre- DSC:D90752 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A multistage hierarchical algorithm for hand shape recognition

This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm

Institutionalising Performance Management in R&D Organisations: Key Concepts and Aspects

In an era in which accountability, cost effectiveness and impact orientation are at premium, Research and Technological Organisations are under pressure not only to improve their performance but also to be able to demonstrate this improvement. This pressure is particularly hard-felt by agricultural research organisations, where funders’ perceptions of a lack of evidence for the uptake and impact of products and services are raising questions about their efficacy and existence. Such pressures can be traced back to several factors, including changes in management trends and the growing scarcity of donor funding in the face of proliferation of Non-Governmental Organisations. These pressures have focussed R&D Organisations attention on the need to develop monitoring and evaluation systems that are capable of ensuring and demonstrating improved performance. In recognising that the developmental impact of research is notoriously difficult to assess, the paper is predicated on the belief that indicators of organisational uptake can provide reliable proxies, or ‘leading’ indicators of development impact. The background to this paper is a DFID-funded pilot action research project that ran between September 2001 and December 2002. The project aimed to adapt and test a novel approach to performance management within three agricultural research and development agencies. The key concepts and aspects of this novel approach and similar work done are discussed.Performance Management; Impact; Evaluation; DFID

The economics of maintaining the breed diversity with reference to the United Kingdom dairy herd

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