Colour object search

The visual search process is required when locating an object in some region of space. To perform this search two capabilities must be available: the ability to recognise the object when it comes into view; and a way of selecting these views. Visual search is often complicated by object occlusion and low spatial resolutions of the object. Although the human visual system performs this task effortlessly, the mechanisms of it are not properly understood. Object colour and geometry, however do play an important role. This thesis develops an object search methodology which assumes that a computer vision system captures both wide-angle and zoomed images of the scene containing the object. Since most of the research has focused on object recognition using geometry, this system is purely colour-based. It is not expected that object colour will always give a definitive solution, however database pruning will often occur leading to reduced search times. The thesis argues that because colour is salient and more resilient than geometry to decreases in spatial resolution, it is more appropriate for visual search when the object occupies a small spatial resolution in an image with a large field of view. It also demonstrates that colour can be used to recognise objects when they occupy most of the field of view; as well as discriminate between database models with similar colour proportions but different region topologies. These conclusions are supported by the results produced by three algorithms, two of which perform colour object search and one that performs colour object recognition. The first object search algorithm uses image locations containing salient object colours as a method of selecting views. Each of these views are ranked indicating which view most likely contains the object. The second object search algorithm identifies image regions with similar colour and topology as the object. These results are produced in a best-first order. The object recognition algorithm uses an invariant based on region area to identify three corresponding model and image regions. A transformation is calculated to bring the model and object into the same viewpoint where region matches are based on position and colour. Each of these methods produced good results in complex indoor scenes with fluorescence and/or tungsten filament lighting; also the search speeds were impressive

Use of Colour in Machine Vision: Colour Representation, Edge Detection with Colour, Segmentation of Colour Space and Colour Constancy

This report is a study of the role of colour and its use in machine vision. Intuitively, for low level image processing, colour provides greater discrimination than grey level for separating different homogenous regions in an image. The first part describes the use of colour in edge detection. In current vision systems, extracting object features such as lines and arcs relies on edge detection. The use of colour images (RGB) can offer additional confidence in the existence of an edge element in one plane when it is corroborated by pixels at the location on one or more of the other planes. The second part investigates the problems and techniques associated with colour image segmentation. A spectral segmentation algorithm based on locating the boundaries of each colour cluster in the spectral space is proposed. The third part investigates the use of colour features for object recognition. Colour information also provides a useful cue for object localisation and identification. The major issues that have to be addressed are colour constancy and representation, and also, their connections to segmentation. Finally, a system for locating object surfaces based on a simplified colour constancy and its colour representation is proposed

A colour object search algorithm

In this paper a colour object search algorithm is presented. Given an image, areas of interest are generated (for each database model) by isolating regions whose colours are similar to model colours. Since the model may exist at one or more of these region locations, each is examined individually. At each region location the object size is estimated and a growing process initiated to include all pixels with model colours. Growing is terminated when a match measure (based on object size and the number of pixels with each model colour) is maximised; if it exceeds a predefined threshold then the object is assumed to be present. Several experiments are presented which demonstrate the algorithm's robustness to scale, affine object distortion, varying illumination, image clutter and occlusion. 1 Introduction Object search requires two capabilities: the ability to recognise an object when it comes into view and a mechanism that brings the object into view. The first of these probl..