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RISS 인기검색어
- 검색키워드
- 저자 : ( Yongfeng Ren ) (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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An Improved Saliency Detection for Different Light Conditions
( Yongfeng Ren ),( Jingbo Zhou ),( Zhijian Wang ),( Yunyang Yan ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.3
In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.
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Approximate Optimal Stabilization Control of Servo Mechanisms based on Reinforcement Learning Scheme
Yongfeng Lv,Xuemei Ren,Shuangyi Hu,Hao Xu 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.10
A reinforcement learning (RL) based adaptive dynamic programming (ADP) is developed to learn the approximate optimal stabilization input of the servo mechanisms, where the unknown system dynamics are approximated with a three-ayer neural network (NN) identifier. First, the servo mechanism model is constructed and a three-layer NN identifier is used to approximate the unknown servo system. The NN weights of both the hidden layer and output layer are synchronously tuned with an adaptive gradient law. An RL-based critic three-layer NN is then used to learn the optimal cost function, where NN weights of the first layer are set as constants, NN weights of the second layer are updated by minimizing the squared Hamilton-Jacobi-Bellman (HJB) error. The optimal stabilization input of the servomechanism is obtained based on the three-layer NN identifier and RL-based critic NN scheme, which can stabilize the motor speed from its initial value to the given value. Moreover, the convergence analysis of the identifier and RL-based critic NN is proved, the stability of the cost function with the proposed optimal input is analyzed. Finally, a servo mechanism model and a complex system are provided to verify the correctness of the proposed methods.
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Salient Object Detection via Multiple Random Walks
( Jiyou Zhai ),( Jingbo Zhou ),( Yongfeng Ren ),( Zhijian Wang ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.4
In this paper, we propose a novel saliency detection framework via multiple random walks (MRW) which simulate multiple agents on a graph simultaneously. In the MRW system, two agents, which represent the seeds of background and foreground, traverse the graph according to a transition matrix, and interact with each other to achieve a state of equilibrium. The proposed algorithm is divided into three steps. First, an initial segmentation is performed to partition an input image into homogeneous regions (i.e., superpixels) for saliency computation. Based on the regions of image, we construct a graph that the nodes correspond to the superpixels in the image, and the edges between neighboring nodes represent the similarities of the corresponding superpixels. Second, to generate the seeds of background, we first filter out one of the four boundaries that most unlikely belong to the background. The superpixels on each of the three remaining sides of the image will be labeled as the seeds of background. To generate the seeds of foreground, we utilize the center prior that foreground objects tend to appear near the image center. In last step, the seeds of foreground and background are treated as two different agents in multiple random walkers to complete the process of salient object detection. Experimental results on three benchmark databases demonstrate the proposed method performs well when it against the state-of-the-art methods in terms of accuracy and robustness.
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Background Prior-based Salient Object Detection via Adaptive Figure-Ground Classification
( Jingbo Zhou ),( Jiyou Zhai ),( Yongfeng Ren ),( Ali Lu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.3
In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.
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Salient Object Detection via Adaptive Region Merging
( Jingbo Zhou ),( Jiyou Zhai ),( Yongfeng Ren ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.9
Most existing salient object detection algorithms commonly employed segmentation techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q`s adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.
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