Parts-based object detection using multiple views

One of the most important problems in image understanding is robust object detection. Small changes in object appearance due to illumination, viewpoint, and occlusion can drastically change the performance of many object detection methods. Non-rigid object can be even more difficult to reliably detect. The unique contribution of this thesis was to extend the approach of parts-based object detection to include support for multiple viewing angles. Bayesian networks were used to integrate the parts detection of each view in a flexible manner, so that the experimental performance of each part detector could be incorporated into the decision. The detectors were implemented using neural networks trained using the bootstrapping method of repeated backpropagation, where false-positives are introduced to the training set as negative examples. The Bayesian networks were trained with a separate dataset to gauge the performance of each part detector. The final decision of object detection system was made with a logical OR operation. The domain of human face detection was used to demonstrate the power of this approach. The FERET human face database was selected to provide both training and testing images; a frontal and a side view were chosen from the available poses. Part detectors were trained on four features from each view?the right and left eyes, the nose, and the mouth. The individual part detection rates ranged from 85% to 95% against testing images. Crossvalidation was used to test the system as a whole, giving average view detection rates of 96.7% and 97.2% respectively for the frontal and side views, and an overall face detection rate of 96.9% amongst true-positive images. A 5.7% false-positive rate was demonstrated against background clutter images. These results compare favorably with existing methods, but provide the additional benefit of face detection at different view angles

An Automatic Human Face Detection Method

This article contains a proposal for an automatic human face detection method, that tries to join several theories proposed by different authors. The method is based on detection of shape features (eye pairs) and skin color. The method assumes certain circumstances and constraints, respectively. Therefore it is not applicable universally. Given the constraints, it is effective enough for applications where fast execution is required. Test results are given and at the end some directives for future work are discussed

Automatic vehicle tracking and recognition from aerial image sequences

This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences. Motivated by its successes in the existing literature focus on the use of linear appearance subspaces to describe multi-view object appearance and highlight the challenges involved in their application as a part of a practical system. A working solution which includes steps for data extraction and normalization is described. In experiments on real-world data the proposed methodology achieved promising results with a high correct recognition rate and few, meaningful errors (type II errors whereby genuinely similar targets are sometimes being confused with one another). Directions for future research and possible improvements of the proposed method are discussed