کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6896514 | 1445999 | 2015 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Ant colony optimization based binary search for efficient point pattern matching in images
ترجمه فارسی عنوان
جستجوی باینری مبتنی بر بهینه سازی مورچه ها برای تطبیق الگوی کارایی بهتر در تصاویر
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
Point Pattern Matching (PPM) is a task to pair up the points in two images of a same scene. There are many existing approaches in literature for point pattern matching. However, the drawback lies in the high complexity of the algorithms. To overcome this drawback, an Ant Colony Optimization based Binary Search Point Pattern Matching (ACOBSPPM) algorithm is proposed. According to this approach, the edges of the image are stored in the form of point patterns. To match an incoming image with the stored images, the ant agent chooses a point value in the incoming image point pattern and employs a binary search method to find a match with the point values in the stored image point pattern chosen for comparison. Once a match occurs, the ant agent finds a match for the next point value in the incoming image point pattern by searching between the matching position and maximum number of point values in the stored image point pattern. The stored image point pattern having the maximum number of matches is the image matching with the incoming image. Experimental results are shown to prove that ACOBSPPM algorithm is efficient when compared to the existing point pattern matching approaches in terms of time complexity and precision accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 246, Issue 1, 1 October 2015, Pages 154-169
Journal: European Journal of Operational Research - Volume 246, Issue 1, 1 October 2015, Pages 154-169
نویسندگان
N.K. Sreeja, A. Sankar,