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- 实验方案
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Online Mutual Foreground Segmentation for Multispectral Stereo Videos
摘要： The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.
关键词： Multispectral imagery,Energy minimization,Cosegmentation,Video signal processing,Video object segmentation
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spatially Regularzied Sparsecem for Target Detection in Hyperspectral Images
摘要： Constrained energy minimization (CEM) is a popular method for target detection in hyperspectral images. Its variant Sparse CEM uses a sparsity regularization term to promote the sparsity of the detection output. However, these approaches do not consider the spatial correlation of hyperspectral pixels, and target detection can further benefit from exploiting the spatial information. In this paper, we propose a novel constrained detection algorithm, referred to as Spatial-Sparse CEM, to simultaneously force the sparsity of the output and piecewise continuity via proper regularizations. The formulated problem is solved efficiently by using alternating direction method of multipliers (ADMM). We illustrate the enhanced performance of the Spatial-Sparse CEM algorithm via both synthetic and real hyperspectral data.
关键词： Hyperspectral image,spatially-regularized detection,target detection,(cid:96)1-norm regularization,ADMM,constrained energy minimization
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Single-Sample Aeroplane Detection in High-Resolution Optimal Remote Sensing Imagery
摘要： In remote sensing images, detecting aeroplanes of special shapes is difficult due to limited number of samples. Without enough training samples, most supervised learning based algorithms will fail. Focusing on the specially-shaped aeroplanes in high-resolution optical remote sensing imagery, this paper presents a single-sample approach. The proposed approach takes one sample as input and directly searches for similar matches from the image. Unlike the supervised learning algorithms which extracts information from positive and negative samples, the hyperspectral algorithm estimates the statistics of background by analyzing the global information of the target image, needless to provide negative samples. Furthermore, this algorithm tries to find a hyperplane projected on which the background is compressed while the target is preserved, making it more data-adaptive than the conventional similarity measurements. Experiments on real data have presented the robustness of the proposed method.
关键词： locally adaptive regression kernels,Aeroplane detection,constrained energy minimization
更新于2025-09-10 09:29:36