Abstract A system implemented in MATLAB is described, which may be deployed over a Campus Grid utilizing the Condor job management system. Our approach can re‐distribute jobs as node availability changes. The architecture of the system, its components, and their deployment across the Cardiff University Campus Grid (consisting of 2500 machines) are presented. Challenges in image processing applications that can be deployed over such infrastructure are presented, along with performance results that demonstrate the use of our system alongside a standard Condor deployment, demonstrating a significant increase in throughput using our approach ‡ . Copyright © 2008 John Wiley & Sons, Ltd.

Diffusion tensor imaging (DTI) provides insight into the white matter of the human brain, which is affected by schizophrenia. By comparing a patient group to a control group, the DTI-images are on average expected to be different for white matter regions. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to classify the groups. In this work, the number of principal components is optimised for obtaining the minimal classification error. A robust estimate of this error is computed in a cross-validation framework, using different compositions of the data into a training and a testing set Previously, sequential runs were performed in MATLAB, resulting in long execution times. In this paper we describe an experiment where this application was run on a grid with minimal modifications and user effort. We have adopted a service-based approach that autonomously launches image analysis services onto a campus-wide Condor pool comprising of volunteer resources. This allows high throughput analysis of our data in a dynamic resource pool. The challenge in adopting such an approach comes from the nature of the resources, which change randomly with time and thus require fault tolerance. Through this approach we have reduced the computation time of each dataset from 90 minutes to less than 10. A minimal classification error of 22% was obtained, using 15 principal components.

Interactive image processing is an important requirement in many industrial applications, such as the inspection of industrial parts within a manufacturing environment, or the processing of images from surveillance cameras. Being able to achieve this quickly and accurately is often essential for the success of such industrial applications. A service-based approach that autonomously launches Image Analysis Services (accessible through a Central Service Manager) onto spare network resources through a Condor system is presented. This allows high throughput analysis of these images in a dynamic resource pool. The Central Service Manager reacts to new tasks submitted to the Image Analysis Services and is able to add new service instances to manage these tasks dynamically. Each service instance here corresponds to a computational resource that is able to execute image processing algorithms. New service instances may be requested by the Central Service Manager from the Condor system, based on the number of tasks that need to be processed. This enables entire image repositories to be acted upon interactively and in parallel, as opposed to the analysis of single images individually. The approach is demonstrated through a campus-wide test bed utilising a Condor system with 90 machines.

The ability to manipulate previously unseen objects under visual control is one of the key tasks in the successful implementation of robotic, automated assembly and adaptive material handling systems. The automation of such complex industrial environments will require the development of machine vision systems that are highly adaptable and capable of dealing with a wide range of variable products. An important group of applications of this type is found in the automated packing and nesting of arbitrary shapes. The aim of this work has been to produce an efficient packing strategy that is flexible enough for a wide variety of industrial uses and which can be implemented using fast moderately priced hardware. A systems approach, as distinct from a purely algorithmic one, has been deliberately adopted since the work is concerned with industrial vision applications in which significant problem constraints exist. This paper also outlines the background to this research, and reviews a selection of industrial packing applications. The packing procedure that has been devised, consists of two major components. The first is a geometric packing technique that is based on morphological image processing operations. This is used in conjunction with a prolog based heuristic packing procedure. Some of the factors considered at the heuristic level include shape ordering and shape orientation, both of which must be carried out prior to the implementation of the geometric packer. The heuristic procedures deal with problem constraints that are specific to a given application.

In this correspondence, we present a new computationally efficient three-dimensional (3-D) object segmentation technique. The technique is based on the detection of edges in the image. The edges can be classified as belonging to one of the three categories: fold edges, semistep edges (defined here), and secondary edges. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points are extracted from the EDCs. A subset of the edge pixels is extracted first using these critical points. The edges are grown from these pixels through the application of some masks proposed in this correspondence. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort is small since the masks are applied only over a small neighborhood of critical points (edge regions). Furthermore, the algorithm can be implemented in parallel, as edge growing from different regions can be carried out independently of each other.>

A method for describing shape is discussed. The technique is specifically intended for use in the automatic visual inspection of flat industrial artifacts, where the shapes are normally very stable but occasionally defects are encountered which display either small overall changes or severe local changes. To represent a given shape, a treelike structure is derived in which the nodes represent convex polygons. The root of the tree is the convex hull of the figure. Nodes in the first level correspond to the convex hulls of the concavities; those in the second level are the convex hulls of metaconcavities, and so on. The generation of standardized trees is discussed so that tree/shape matching may be accomplished with a minimum of computational effort.

Twelve shape descriptors are defined and their characteristics discussed. Eight are linear measurements, two are angles between defined chords and the other two are area estimates. These same measurements may be calculated for the given shape, or its concavities, metaconcavities, etc. In this way, a concavity tree (1,2) may be labeled, to produce a convenient representation of shape that combines local and global information.