• Title/Summary/Keyword: sobel edge

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The Obstacle Avoidance Algorithm of Mobile Robot using Line Histogram Intensity (Line Histogram Intensity를 이용한 이동로봇의 장애물 회피 알고리즘)

  • 류한성;최중경;구본민;박무열;방만식
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.8
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    • pp.1365-1373
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    • 2002
  • In this paper, we present two types of vision algorithm that mobile robot has CCD camera. for obstacle avoidance. This is simple algorithm that compare with grey level from input images. Also, The mobile robot depend on image processing and move command from PC host. we has been studied self controlled mobile robot system with CCD camera. This system consists of digital signal processor, step motor, RF module and CCD camera. we used wireless RF module for movable command transmitting between robot and host PC. This robot go straight until recognize obstacle from input image that preprocessed by edge detection, converting, thresholding. And it could avoid the obstacle when recognize obstacle by line histogram intensity. Host PC measurement wave from various line histogram each 20 pixel. This histogram is (x, y) value of pixel. For example, first line histogram intensity wave from (0, 0) to (0, 197) and last wave from (280, 0) to (2n, 197. So we find uniform wave region and nonuniform wave region. The period of uniform wave is obstacle region. we guess that algorithm is very useful about moving robot for obstacle avoidance.

Fast Image Pre-processing Algorithms Using SSE Instructions (SSE 명령어를 이용한 영상의 고속 전처리 알고리즘)

  • Park, Eun-Soo;Cui, Xuenan;Kim, Jun-Chul;Im, Yu-Cheong;Kim, Hak-Il
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.2
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    • pp.65-77
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    • 2009
  • This paper proposes fast image processing algorithms using SSE (Streaming SIMD Extensions) instructions. The CPU's supporting SSE instructions have 128bit XMM registers; data included in these registers are processed at the same time with the SIMD (Single Instruction Multiple Data) mode. This paper develops new SIMD image processing algorithms for Mean filter, Sobel horizontal edge detector, and Morphological erosion operation which are most widely used in automated optical inspection systems and compares their processing times. In order to objectively evaluate the processing time, the developed algorithms are compared with OpenCV 1.0 operated in SISD (Single Instruction Single Data) mode, Intel's IPP 5.2 and MIL 8.0 which are fast image processing libraries supporting SIMD mode. The experimental result shows that the proposed algorithms on average are 8 times faster than the SISD mode image processing library and 1.4 times faster than the SIMD fast image processing libraries. The proposed algorithms demonstrate their applicability to practical image processing systems at high speed without commercial image processing libraries or additional hardwares.

Study on the Front Detection Techniques by using Satellite Data (위성 자료를 이용한 전선 탐지 기법 연구)

  • Hwang, Do-Hyun;Bak, Su-Ho;Enkhjargal, Unuzaya;Jeong, Min-Ji;Kim, Na-Kyeong;Park, Mi-So;Kim, Bo-Ram;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.6
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    • pp.1201-1208
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    • 2020
  • A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.