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.
This paper describes a spectral-spatial model (for colour object recognition) which exploits the shape, colour and position of regions on the surface of a rigid object in describing it. Given a model and test image (with colour constancy pre-processing) suitably segmented into colour regions, model and test regions with similar shape and colour are identified. If at least three model regions have matching test regions then the consistency of these matches are verified using distance/area affine invariant ratios. Subsequently, model regions are affine transformed into image space for matching, from which a match probability is determined. Experimental results demonstrate that this model is significantly invariant to illumination changes, affine deformity and partial occlusion.
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