Adaptive segmentation for visual salient object

Hong-wei, 赵宏伟 Zhao; Xiao, 陈霄 Chen; Ping-ping, 刘萍萍 Liu; Qing-tian, 耿庆田 Geng

doi:10.3788/ope.20132102.0531

Cited by 5 publications

(4 citation statements)

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“…In the watermarking extraction step, the inverse transformation of DCT-SVD is used to extract the watermarking from the pending detection image, which is the saliency map of the original image. After that the local energy model [16] is also applied to get the saliency map of the watermarked image. Then the two saliency maps are substracted to get the offset image.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

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A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria

Zhao

Qin

Liu

2015

Entropy

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This paper proposes a novel image watermarking method based on local energy and maximum entropy aiming to improve the robustness. First, the image feature distribution is extracted by employing the local energy model and then it is transformed as a digital watermark by employing a Discrete Cosine Transform (DCT). An offset image is thus obtained according to the difference between the extracted digital watermarking and the feature distribution of the watermarked image. The entropy of the pixel value distribution is computed first. The Lorenz curve is used to measure the polarization degree of the pixel value distribution. In the pixel location distribution flow, the maximum entropy criteria is applied in segmenting the offset image into potentially tampered regions and unchanged regions. All-connected graph and 2-D Gaussian probability are utilized to obtain the probability distribution of the pixel location. Finally, the factitious tampering probability value of a pending detected image is computed through combining the weighting factors of pixel value and pixel location distribution. Experimental results show that the proposed method is more robust against the commonly used image processing operations, such as Gaussian noise, impulse noise, etc. Simultaneously, the proposed method achieves high sensitivity against factitious tampering.

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Section: Motivations and Contributionsmentioning

confidence: 99%

“…Entropy 2015, 17,[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] 3 are necessary in this study: pixel value and pixel location distributions. Therefore, the image tampering detection algorithm is divided into two workflows: pixel value and pixel location.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria

Zhao

Qin

Liu

2015

Entropy

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“…In this paper, the maximum entropy algorithm [16] is adopted to extract the saliency region from the image. Unlike the original Itti model, the currently proposed model adds local energy features.…”

Section: Introductionmentioning

confidence: 99%

A New Mobile Visual Search System Based on the Human Visual System

Zhao

Liu

et al. 2013

AMM

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We describe a novel mobile visual search system based on the saliencymechanism and sparse coding principle of the human visual system (HVS). In the featureextraction step, we first divide an image into different regions using thesaliency extraction algorithm. Then scale-invariant feature transform (SIFT)descriptors in all regions are extracted while regional identities arepreserved based on their various saliency levels. According to the sparsecoding principle in the HVS, we adopt a local neighbor preserving Hash functionto establish the binary sparse expression of the SIFT features. In the searchingstep, the nearest neighbors matched to the hashing codes are processed accordingto different saliency levels. Matching scores of images in the database arederived from the matching of hashing codes. Subsequently, the matching scoresof all levels are weighed by degrees of saliency to obtain the initial set of results. In order to further ensure matching accuracy, we propose an optimized retrieval scheme based on global texture information. We conduct extensive experiments on an actual mobile platform in large-scale datasets by using Corel-1000. The resultsshow that the proposed method outperforms the state-of-the-art algorithms on accuracyrate, and no significant increase in the running time of the feature extractionand retrieval can be observed.

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“…12a (with optical filter installed). After implementing the algorithms, such as the graying algorithm and expansion corrosion algorithm [31,32], the processed image as shown in Fig. 12b.…”

Section: Image Recognition Of the Water Jetmentioning

confidence: 99%

Firefighting robot with video full-closed loop control

Wu¹,

Li²,

Ye³

et al. 2016

Int. J. SAFE

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In view of the many problems in firefighting robots, such as complicated flame positioning, poor environmental adaptability, and difficult installation and debugging, a new firefighting robot design is presented with video full closed-loop feedback to extinguish ground fire in this paper. The firefighting robot consists of a 2-DOF robot, a monocular camera, and a controller. The monocular camera installed on the second link of the robot is utilized to detect and locate ground flames. The robot can dynamically adjust the water landing point to track a flame in real-time through motion control, as the camera is specifically designed with an additional infrared narrowband pass filter and a filter-switching mechanism. An algorithm of detecting and positioning for flame and water landing point is proposed based on image processing and robotic kinematics. Experimental results show that the firefighting robot with video full closed-loop feedback can realize real-time flame detection, location, and sprinkler, and can dynamically track fire location within the monitoring scope. The distance error between fire and the landing point of the water jet can be controlled within a narrow range. Moreover, this firefighting robot is easy to install, debug and have good environment adaptability, and provides efficient and safe solutions for complicated firefighting environment. At the same time, due to its small size and convenient calibration features, this firefighting robot is especially suitable for large space environments.

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Adaptive segmentation for visual salient object

Cited by 5 publications

References 0 publications

A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria

A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria

A New Mobile Visual Search System Based on the Human Visual System

Firefighting robot with video full-closed loop control

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