By Lazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh, Antonios Gasteratos
This e-book constitutes the refereed court cases of the tenth foreign convention on computing device imaginative and prescient platforms, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The forty eight papers awarded have been rigorously reviewed and chosen from ninety two submissions. The paper are equipped in topical sections on organic and cognitive imaginative and prescient; hardware-implemented and real-time imaginative and prescient structures; high-level imaginative and prescient; studying and variation; robotic imaginative and prescient; and imaginative and prescient platforms applications.
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Extra resources for Computer Vision Systems: 10th International Conference, ICVS 2015, Copenhagen, Denmark, July 6-9, 2015, Proceedings
In Fig. 6 the ten best candidates per method are shown. It can be seen that two of the reference methods (RP and OM) generally produce very large candidates that capture either multiple objects or very large structures. The proposed method on the other hand adequately produces candidates for the individual objects. 4 Conclusion We have presented a method for ﬁnding arbitrary, unknown objects in RGB-D data that utilizes principles of human object perception —visual attention and Gestalt psychology.
De Computer Science Department III, Rheinische Friedrich-Wilhelms Universit¨ at, R¨ omerstr. 164, 53117 Bonn, Germany Abstract. We present a new method for generating general object candidates for cluttered RGB-D scenes. Starting from an over-segmentation of the image, we build a graph representation and deﬁne an object candidate as a subgraph that has maximal internal similarity as well as minimal external similarity. These candidates are created by successively adding segments to a seed segment in a saliency-guided way.
We use the colorspace of , but shifted and scaled to the range [0, 1]. Next, a saliency map and an over-segmentation are generated from the color data. From the oversegmented map, a graph is constructed which has segments as vertices and stores the similarity of neighboring segments in edge weights. Then, we introduce the saliency-guided Prim’s algorithm that generates object candidates by iteratively adding segments to a set of salient seed segments. Finally, we rank the candidates by a combination of Gestalt principles which is learned with an SVM.
Computer Vision Systems: 10th International Conference, ICVS 2015, Copenhagen, Denmark, July 6-9, 2015, Proceedings by Lazaros Nalpantidis, Volker Krüger, Jan-Olof Eklundh, Antonios Gasteratos