Abstract
Object recognition is the task of finding an object from a given image. A human being can recognize objects that are in a video sample or an image with different size, position or viewpoint. This ability is becoming a common and developing subject of research. Objective: The main aim of this paper is to implement such human ability as an object recognition system onto a computer and address the issues related to scaling, rotation invariance, and computational complexity and to recognize an object in a cluttered background. Methods: In this paper, object recognition is performed based on attention based object recognition algorithm. Gaussian and Difference of Gaussian filters are used for image pre-processing. Saliency map is generated, and keypoints are extracted using Laplace of Gaussian approach. Then keypoint descriptor vectors are generated, and matching of test image and training image is performed using a fast nearest neighbour algorithm. Findings: The scaling factor of 1.2 is applied to increase the detection rate under differing image rotations. Implementation is done using parallelization concept in a multicore processor using OpenMP programming model. Improvement: The performance of serial and parallel execution of object recognition is analyzed, and the performance can still be increased by using OpenMP directives such as sections and tasks.
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