Object Detection in Real Images
This addresses the need for more practical and intelligent object detection methods in computer vision, but it appears incremental as it builds on existing approaches.
The paper tackles the problem of object detection in real images by proposing a new method that uses geometric shapes as additional features and a hierarchical codebook for robust inference, with preliminary work completed on detecting geometric shapes.
Object detection and recognition are important problems in computer vision. Since these problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real-time, and dynamic object detection/recognition methods are still unavailable. We propose a new object detection/recognition method, which improves over the existing methods in every stage of the object detection/recognition process. In addition to the usual features, we propose to use geometric shapes, like linear cues, ellipses and quadrangles, as additional features. The full potential of geometric cues is exploited by using them to extract other features in a robust, computationally efficient, and less meta-heuristic manner. We also propose a new hierarchical codebook, which provides good generalization and discriminative properties. The codebook enables fast multi-path inference mechanisms based on propagation of conditional likelihoods, that make it robust to occlusion and noise. It has the capability of dynamic learning. We also propose a new learning method that has generative and discriminative learning capabilities, does not need large and fully supervised training dataset, and is capable of online learning. The preliminary work of detecting geometric shapes in real images has been completed. This preliminary work is the focus of this report. Future path for realizing the proposed object detection/recognition method is also discussed in brief.