Browsing by Author "Shen, Chin-Han"

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RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework
(2018-01-01)
Huang, Posheng
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Shen, Chin-Han
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Hsiao, Hsu-Feng
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資訊工程學系
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Department of Computer Science
In this paper, a learning based salient object detection method for RGBD images is introduced. With the assistance of depth information, the silhouette features of an object can be retrieved primarily, and it can lead to better detection of salient objects. In addition, many recent works still rely on some image post-processing methods to improve their performance. We develop a more efficient end-to-end model with a modified design of loss function used in our training network. The design of the new loss function is to increase the spatial coherence of detected salient objects. From the evaluation results, the proposed approach shows good performance compared with the methods that are considered to be state-of-the-art.
Saliency Detection with Multi-Contextual Models and Spatially Coherent Loss Function
(2019-01-01)
Huang, Po-Sheng
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Shen, Chin-Han
;
Hsiao, Hsu-Feng
;
資訊工程學系
;
Department of Computer Science
We have proposed a multi-contextual model architecture with color and depth information considered independently in this work. To utilize the feature maps of different levels better, short connection structures are used to integrate the knowledge from color and depth data separately. A novel loss function considering three criteria is proposed to improve the detection accuracy and spatial coherence of the detected results. The training process of the proposed network is divided into two stages, a pre-training phase and a refinement phase to increase the efficiency of the network.