Object detection and recognition in complex scenes

Abstract

Contour-based object detection and recognition in complex scenes is one of the most dificult problems in computer vision. Object contours in complex scenes can be fragmented, occluded and deformed. Instances of the same class can have a wide range of variations. Clutter and background edges can provide more than 90% of all image edges. Nevertheless, our biological vision system is able to perform this task effortlessly. On the other hand, the performance of state-of-the-art computer vision algorithms is still limited in terms of both speed and accuracy. The work in this thesis presents a simple, efficient and biologically motivated method for contour-based object detection and recognition in complex scenes. Edge segments are extracted from training and testing images using a simple contour-following algorithm at each pixel. Then a descriptor is calculated for each segment using Shape Context, including an offset distance relative to the centre of the object. A Bayesian criterion is used to determine the discriminative power of each segment in a query image by means of a nearest-neighbour lookup, and the most discriminative segments vote for potential bounding boxes. The generated hypotheses are validated using the k nearest-neighbour method in order to eliminate false object detections. Furthermore, meaningful model segments are extracted by finding edge fragments that appear frequently in training images of the same class. Only 2% of the training segments are employed in the models. These models are used as a second approach to validate the hypotheses, using a distancebased measure based on nearest-neighbour lookups of each segment of the hypotheses. A review of shape coding in the visual cortex of primates is provided. The shape-related roles of each region in the ventral pathway of the visual cortex are described. A further step towards a fully biological model for contourbased object detection and recognition is performed by implementing a model for meaningful segment extraction and binding on the basis of two biological principles: proximity and alignment. Evaluation on a challenging benchmark is performed for both k nearestneighbour and model-segment validation methods. Recall rates of the proposed method are compared to the results of recent state-of-the-art algorithms at 0.3 and 0.4 false positive detections per image.