The problem of finding relevant resources from those available across the World Wide Web is well recognised. Improved search engines provide part of the answer, but we also need to support the user in assessing for themselves the relevance of documents suggested by a search engine, prior to download. One way to do this is to provide them with descriptions tailored to their profile and query. This paper presents examples of how XSLT may be used to create tailored descriptions from RDF metadata, and explores whether XSLT is an adequate tool for this task.

Presenting tailored resource descriptions: will XSLT do the job?

Model indexing and object recognition using 3D viewpoint invariance

This paper presents a bibliography of nearly 1200 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection and segmentation; image analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, such as geometry, graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition and artificial intelligence, as well as on applications.

Image analysis and computer vision: 1991

: The reliable detection of an object of interest in an input image with arbitrary background clutter and occlusion has to a large extent remained an elusive goal in computer vision. Traditional model-based approaches are inappropriate for a multi-class object detection task primarily due to difficulties in modeling arbitrary object classes. Instead, we develop a detection framework whose core component is a nearest neighbor search over object parts. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input measurements belong to different classes. This pair-wise probability is not in general a metric distance measure. Furthermore, it can out-perform any metric distance, approaching even the Bayes optimal performance. In practice, we seek a model for the optimal distance measure that combines the discriminative powers of more elementary distance measures associated with a collection of simple feature spaces that are easy and efficient to implement; in our work, we use histograms of various feature types like color, texture and local shape properties. For performing efficient nearest neighbor search over large training sets, the linear model was extended to discretized distance measures that combines distance measures associated with discriminators organized in a tree-like structure. Finally, the nearest neighbor search over object parts was integrated into a whole object detection system and evaluated against both an indoor detection task as well as a face recognition task yielding promising results.

/pdf/discriminative-distance-measures-for-object-detection-57x5itqltu.pdf

Discriminative distance measures for object detection

https://cs.ou.edu/~atiq/papers/underhill-mm.pdf

Performance of the Hough transform on a distributed memory multiprocessor

Developing parallel algorithms for intermediate and high levels of computer vision systems is addressed. Because the algorithms are complex and the nature and size of the input and output data sets vary for each application, the authors have directly developed parallel algorithms for dynamic control of both processing and communication complexity during execution. They have also examined the merits of functional prototyping and transforming programs into imperative execution code for final implementation. To evaluate and give direction to their work, they have implemented algorithms for plane detection and object recognition on a flexible transputer network. >

Dynamic control and prototyping of parallel algorithms for intermediate- and high-level vision

Developing parallel algorithms for intermediate and high levels of computer vision systems is addressed. Because the algorithms are complex and the nature and size of the input and output data sets vary for each application, the authors have directly developed parallel algorithms for dynamic control of both processing and communication complexity during execution. They have also examined the merits of functional prototyping and transforming programs into imperative execution code for final implementation. To evaluate and give direction to their work, they have implemented algorithms for plane detection and object recognition on a flexible transputer network.<<ETX>>

Prototyping of Parallel Algorithms for Intermediate- and High-Level Vision

Describes a practical approach to match 2D scenes to 3D models in which the scene data is described as a set of curvilinear features extracted from a grey scale image of a rigid object taken from an arbitrary viewpoint, and the model data is derived from a CSG modeller. In contrast to systems that directly acquire depth data, the low and intermediate processing is more reliable and less complex, which can lead to a more effective system using a rapid hypothesis and verification cycle. This may change as research in 3D increasingly concentrates on improving the sensor technology and low-level segmentation algorithms so that surface descriptions become less fragmented and include plausible curved surfaces. >

Rapid invocation and matching of 3D models to 2D images using curvilinear data

A general strategy for the interpretation of twodimensional views of manufactured components is presented. The model database contains as primitives parameterised two-dimensional shape descriptions including feature type, position, and orientation where appropriate. Depending on the extent of each feature either a point or a point vector is associated with each constituent feature within the model or scene. Attributed geometric pairwise relations are formed between relevant primitives. The complexity of the search space of possible scene-model feature correspondences is limited in two ways. Firstly, the pre-formed pairwise relations are used to indicate the probable occurrence of both specific components and specific features. Secondly, a measure of saliency is employed which directs the search to those features which most precisely determine particular components. This is an extension and generalisation of previous work on the matching of two-dimensional descriptions and complements work on low and intermediate level processing of the scene data, based on the use of the generalised Hough transformation.

/pdf/interpretation-of-2d-scenes-using-a-general-relational-model-4jg0wp1kmx.pdf

Interpretation of 2D Scenes Using a General Relational Model.

In the visual context, a reasoning system should he capable of inferring a scene description using evidence derived from data-driven processing of the iconic image data This evidence may consist of a set of curvilinear boundaries, which are obtained by grouping local edge data into extended features Using linear primitives, a framework is described which represents the information contained in pre-formed models of possible objects in the scene, and in the segmented scenes themselves A method based on maximum entropy is developed which assigns measures of likelihood for the presence of objects in the two-dimensional image This method is applied to and evaluated on real and simulated image data, and the effectiveness of the approach is discussed

Model-based object recognition using probabilistic logic and maximum entropy

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