Abstract Views :195 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, SRM University, Chennai – 603203, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 39 (2016), Pagination:
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.
Keywords
Keypoint Descriptor, Multicore, Object Recognition, Parallelism, Saliency Map.
Full Text
