Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all ...Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point patterns.However,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual occlusions.To tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency detection.The proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak search.Upon having accurate co-peaks,one can efficiently infer responses of the targeted instance.Experiments on four benchmark datasets validate the superior performance of the proposed method.展开更多
Co-analyzing a set of 3D shapes is a challenging task considering a large geometrical variability of the shapes. To address this challenge, this paper proposes a new automatic 3D shape co-segmentation algorithm by usi...Co-analyzing a set of 3D shapes is a challenging task considering a large geometrical variability of the shapes. To address this challenge, this paper proposes a new automatic 3D shape co-segmentation algorithm by using spectral graph method. Our method firstly represents input shapes as a set of weighted graphs and extracts multiple geometric features to measure the similarities of faces in each individual shape. Secondly all graphs are embedded into the spectral domain to find meaningful correspondences across the set, After that we build a joint weighted matrix for the graph set and then apply normalized cut criterion to find optimal co-segmentation of the input shapes. Finally we evaluate our approach on different categories of 3D shapes, and the experimental results demonstrate that our method can accurately co-segment a wide variety of shapes, which may have different poses and significant topology changes.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos.U21A20520,62172112)the Key-Area Research and Development of Guangdong Province (2022A0505050014,2020B1111190001)+1 种基金the National Key Research and Development Program of China (2022YFE0112200)the Key-Area Research and Development Program of Guangzhou City (202206030009).
文摘Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point patterns.However,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual occlusions.To tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency detection.The proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak search.Upon having accurate co-peaks,one can efficiently infer responses of the targeted instance.Experiments on four benchmark datasets validate the superior performance of the proposed method.
基金supported by the National Basic Research 973 Program of China under Grant No. 2013CB329505the National Natural Science Foundation of China Guangdong Joint Fund under Grant Nos. U1135005, U1201252the National Natural Science Foundation of China under Grant Nos. 61103162, 61232011
文摘Co-analyzing a set of 3D shapes is a challenging task considering a large geometrical variability of the shapes. To address this challenge, this paper proposes a new automatic 3D shape co-segmentation algorithm by using spectral graph method. Our method firstly represents input shapes as a set of weighted graphs and extracts multiple geometric features to measure the similarities of faces in each individual shape. Secondly all graphs are embedded into the spectral domain to find meaningful correspondences across the set, After that we build a joint weighted matrix for the graph set and then apply normalized cut criterion to find optimal co-segmentation of the input shapes. Finally we evaluate our approach on different categories of 3D shapes, and the experimental results demonstrate that our method can accurately co-segment a wide variety of shapes, which may have different poses and significant topology changes.
