Abstract
Multi-object recognition software on Remote Controlled Weapon Station (RCWS) had been implemented in previous paper using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods, but the processing time in one cycle is quite slow so it is need to be optimized using parallel processing. In this paper, implementation of parallel processing on multi-object recognition software has been done on a multicore processor. The Openmp Application Programming Interface (API), C programming language, and Visual studio Integrated Development Environment (IDE) is used to implement the parallel processing in this paper. The parallel processing was implemented in the for loop of the matching process between the capturing object from the camera and the database under two conditions, i.e., the original of the for loop syntax and after optimization of the for loop syntax. Experiments have been done on the core processor i7-4790 @ 3.60Ghz, 8 GB DDR3 of memory, windows 8.1 os using two, four, six, and eight cores to recognize one, two, three and four objects at once using SIFT and SURF methods. Based on the experiments, it was found that the processing time in parallel is faster than sequential process, where the fastest of the processing time is obtained after optimization in the loop syntax, with the processing time in recognizing one to four objects using SIFT method is 927.13 ms (8 core), 1019.31 ms (6 core), 1190.72 ms (8 core), and 1283.05 ms (4 core), where the sequential processing time in recognizing one to four objects is 1067.35 ms, 1164.78 ms, 1352.93 ms, and 1497.35 ms, while the processing time in recognizing one to four objects using SURF method is 1157.13 ms (8 core), 1517.83 ms (6 core), 1572.14 ms (4 core), dan 1472.64 ms (6 core), where the sequential processing time in recognizing one to four objects is 5635.99 ms, 6268.47 ms, 3256.63 ms, dan 3883.78 ms.
Highlights
The image processing applications are the application that requires a high specification computer or parallel processing techniques to speed up the processing time, especially in applications that use a complex algorithms or methods
Implementation of parallel processing on multi-object recognition software has been done on a multicore processor
Some publication about image processing have been reported, Park et al have implemented the direct calculation of interparticle distance in suspension by image processing using monte carlo method [1], Saleem et al explain a comparison of feature points method on multisensor images [2], Husin et al [3] on the poisonous shrimp detection system for litopenaeus vannamei using k-Nearest Neighbor method, and Mirdanies et al [4] has successfully implemented the multi-object object recognition software on Remote Controlled Weapon Station (RCWS) using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods
Summary
The image processing applications are the application that requires a high specification computer or parallel processing techniques to speed up the processing time, especially in applications that use a complex algorithms or methods. In the publication of mirdanies et al [4], the application program created has been divided into three parts i.e. reading data from kinect and simulating the results, object recognition process, and data transfer to the ballistic computer, where each part communicate using shared memory. This technique is effective to speed up the process and avoiding any collision or delay, because it is not necessary to wait the other unfinished processes. It is necessary to optimize the object recognition process using parallel processing techniques
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