Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human-Robot Teaming for Search and Rescue
IEEE Pervasive Computing
A system to detect houses and residential street networks in multispectral satellite images
Computer Vision and Image Understanding
Background estimation under rapid gain change in thermal imagery
Computer Vision and Image Understanding
Influence of image compression on object detection in natural images segmented with mean shift
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
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This paper presents a novel two-stage data segmentation approach for detection of artificial materials and objects in non-urban terrain. High resolution image of the unknown terrain taken with the digital camera from relatively large distance is divided into smaller sub-images for further processing. Each sub-image is segmented and information about obtained cluster centers is transferred to the next stage. Second segmentation stage uses information about cluster centers from all sub-images and applies the same clustering method to this data set. Finally, decision-making module evaluates all potential candidate segments and eventually proposes segments that have high possibility of presenting artificial material or object in the input image.