No description available.

No description available.

No description available.

No description available.

No description available.

It is argued that a visual system, especially one which handles imperfect data, needs a way of selecting the best consistent combination from among the many interrelated, locally plausible hypotheses about how parts or aspects of the visual input may be interpreted. A method is presented in which each hypothesis is given a supposition value between 0 and 1. A parallel relaxation I operator, based on the plausibilities of hypotheses and the logical relations between them, is then used to modify the supposition values, and the process is repeated until the best consistent set of hypotheses have supposition values of approximately 1, and the rest have values of approximately 0. The method is incorporated in a program which can interpret configurations of overlapping rectangles as puppets. For this task it is possible to formulate all the potentially relevant hypotheses before using relaxation to select the best consistent set. For more complex tasks, it is necessary to use relaxation on the locally plausible interpretations to guide the search for locally less obvious ones. Ways of doing this are discussed. Finally, an implemented system is presented which allows the user to specify schemas and inference rules, and uses relaxation to control the building of a network of instances of the schemas, when presented with data about some instances and relations between them

Various techniques are described for the computer perception of curved objects (cups, mugs, toruses, etc.). Research has been conducted in the areas of image-processing, object recognition, 3-d analysis, and scene analysis. A representation of images in terms of lines is argued to be superior to one in terms of regions. Line-finding is accomplished by an edge-follower which can track round curved boundaries. A new method of segmentation coupled to an improved procedure for fitting conic sections is used to obtain a line-drawing from the object boundaries. A topological description of the image is built up in which junctions are classified according to a comprehensive scheme applicable to pictures with both curved and straight lines. 'The performance of Barrow & Popplestone's program for recognising irregular objects is evaluated, and it is demonstrated that considerable improvements in speed. may be obtained by letting gross features of an object, such as its outline, direct the matching. Objects may also be identified with a new form of Waltz's techniques, based on labelling-constraints derived from topological, 3-d object models. It is shown how the hierarchical synthesis method for object recognition may be implemented so as to facilitate flexible interaction. Good tolerance of imperfections and rapidity of matching are achieved with this technique. A qualitative measure of the shape of object surfaces is obtained from examination of the intensity contours created by the shading of reflected light. 3-d information is also acquired by matching image descriptions with "procedural" models of a set of prototype objects. Scene analysis is performed by generalisations of the methods devised by Waltz. Description and partition of a scene is carried out using either models of specific objects or models of object classes. Procedures are explained for generating labels for the junctions caused by curved objects; the labels for a variety of types of surface interactions are tabulated in an appendix. A complete scene analysis system is described which integrates the research efforts in these areas. Its performance on simple scenes containing both curved and polyhedral objects is assessed. The thesis concludes with an appraisal of the results achieved, with particular regard to future lines of development.

The Scottish Office Department of Agriculture and Fisheries Department has been concerned to know the area of bracken (Pteridium aquilinum ) in Scotland and how rapidly it is spreading. It offers a potential danger to animal health and a devaluation of land for agricultural practice. Bracken is present in patches which are often difficult to delimit because of variable density and appearance throughout the year and is not consistently identifiable at a single minimum spatial resolution. Bracken provided a candidate vegetation type for prototyping a knowledge-based system (KBS) which synthesized environmental data expressed spatially, with knowledge on vegetation dynamics and vegetation succession in particular. Temporal models of vegetation types, which have different degrees of reliability with analysis of static spatial data were incorporated. The knowledge-based system permits the user access to knowledge about botany, ecology, mapping, image processing and spatial data analysis for an informed and informative answer to their query. Bracken was mapped at three experimental sites using land survey, photogrammetric techniques and satellite image processing to quantify the extent of bracken and bracken change. An existing data model was enhanced by building-in the hierarchy of survey observations and associated error bands. The model was then implemented within an expert system shell with KBS routines written in POP-11 and Fortran. The coupling of the GIS and KBS routines was of the form of: 1. organisation of the transfer of descriptive values as parameters between the data-models of the KBS and the GIS routines and 2. read/write of the spatial data into the data-models for analyses by KBS routines.

No description available.

One of the fundamental problems in the field of computer vision is the task of classifying objects, which are present in an image or sequence of images, based on their appearance. This task is commonly referred to as the object recognition problem. A system designed to perform this task must be able to learn visual cues such as shape, colour and texture from examples of objects presented to it. These cues are then later used to identify examples of the known objects in previously unseen scenes. The work presented in this thesis is based on a statistical representation of shape known as a pairwise geometric histogram which has been demonstrated by other researchers in 2-dimensional object recognition tasks. An analysis of the performance of recognition based on this representation has been conducted and a number of contributions to the original recognition algorithm have been made. An important property of an object recognition system is its scalability. This is the. ability of the system to continue performing as the number of known objects is increased. The analysis of the recognition algorithm presented here considers this issue by relating the classification error to the number of stored model objects. An estimate is also made of the number of objects which can be represented uniquely using geometric histograms. One of the main criticisms of the original recognition algorithm based on geometric histograms was the inability to recognise objects at different scales. An algorithm is presented here that is able to recognise objects over a range of scale using the geometric histogram representation. Finally, a novel pairwise geometric histogram representation for arbitrary surfaces has been proposed. This inherits many of the advantages of the 2-dimensional shape descriptor but enables recognition of 3-dimensional object from arbitrary viewpoints.