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Segmenting Salient Objects in 3D Point Clouds of Indoor Scenes Using Geodesic Distances

Journal of Signal and Information Processing 2013年第3期4卷 102-108页

作者： Shashank.Bhatia stephan k. chalup School of Electrical Engineering and Computer Science The University of Newcastle Callaghan 2308 NSW Australia.

Visual attention mechanisms allow humans to extract relevant and important information from raw input percepts. Many applications in robotics and computer vision have modeled human visual attention mechanisms using a bottom-up data centric approach. In contrast, recent studies in cognitive science highlight advantages of a top-down approach to the attention mechanisms, especially in applications involving goal-directed search. In this paper, we propose a top-down approach for extracting salient objects/regions of space. The top-down methodology first isolates different objects in an unorganized point cloud, and compares each object for uniqueness. A measure of saliency using the properties of geodesic distance on the object’s surface is defined. Our method work. on 3D point cloud data, and identifies salient objects of high curvature and unique silhouette. These being the most unique features of a scene, are robust to clutter, occlusions and view point changes. We provide the details of the proposed method and initial experimental results.

关键词： Saliency Detection 3D Image Analysis Image Segmentation

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Classifying cok. using CT scans and landmark.multidimensional scaling

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International Journal of Coal Science & Technology 2023年第1期10卷 160-172页

作者： k.ith Nesbitt Fayeem Aziz Merrick.Mahoney stephan chalup Bishnu P.Lamichhane School of Information and Physical Sciences Universityof NewcastleCallaghanNSWAustralia

One factor that limits development of fundamental research on the infuence of cok. microstructure on its strength is the difculty in quantifying the way that microstructure is both classifed and distributed in three *** support such fundamental studies,this study evaluated a novel volumetric approach for classifying small(approx.450μm^(3))block. of cok. microstructure from 3D computed tomography *** automated process for classifying microstructure block. was *** is based on Landmark.Multi-Dimensional Scaling and uses the Bhattacharyya metric and k.means *** approach was evaluated using 27 cok. samples across a range of cok. with diferent properties and reliably identifed 6 ordered class of cok. microstructure based on the distribution of voxel intensities associated with structural *** lower class(1–2)subblock. tend to be dominated by pores and thin ***,there is an increase in wall thick.ess and reduced pore sizes in the higher *** features are also lik.ly to be seen in higher classes(5–6).In general,this approach provides an efcient automated means for identifying the 3D spatial distribution of microstructure in CT scans of cok..

关键词： Cok. Microstructure Clustering Classifcation Computer tomography

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