Mr. Madhu Kiran Profile

Madhu Kiran

at Visio Ingenii Ltd

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Publications (1)

Proceedings Article | 19 February 2013 Paper

Determinant of homography-matrix-based multiple-object recognition

Nagachetan Bangalore, Madhu Kiran, Anil Suryaprakash

Proceedings Volume 8656, 86560M (2013) https://doi.org/10.1117/12.2003767

KEYWORDS: Object recognition, Databases, Tolerancing, Image compression, Machine vision, Computer vision technology, Light sources and illumination, Data modeling, Detection and tracking algorithms, Visualization

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Finding a given object in an image or a sequence of frames is one of the fundamental computer vision challenges. Humans can recognize a multitude of objects with little effort despite scale, lighting and perspective changes. A robust computer vision based object recognition system is achievable only if a considerable tolerance to change in scale, rotation and light is achieved. Partial occlusion tolerance is also of paramount importance in order to achieve robust object recognition in real-time applications. In this paper, we propose an effective method for recognizing a given object from a class of trained objects in the presence of partial occlusions and considerable variance in scale, rotation and lighting conditions. The proposed method can also identify the absence of a given object from the class of trained objects. Unlike the conventional methods for object recognition based on the key feature matches between the training image and a test image, the proposed algorithm utilizes a statistical measure from the homography transform based resultant matrix to determine an object match. The magnitude of determinant of the homography matrix obtained by the homography transform between the test image and the set of training images is used as a criterion to recognize the object contained in the test image. The magnitude of the determinant of homography matrix is found to be very near to zero (i.e. less than 0.005) and ranges between 0.05 and 1, for the out-of-class object and in-class objects respectively. Hence, an out-of-class object can also be identified by using low threshold criteria on the magnitude of the determinant obtained. The proposed method has been extensively tested on a huge database of objects containing about 100 similar and difficult objects to give positive results for both out-of-class and in-class object recognition scenarios. The overall system performance has been documented to be about 95% accurate for a varied range of testing scenarios.

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