  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.
