Learning adaptive contrast combinations for visual saliency detection

Zhou, Quan; Cheng, Jie; Lu, Huimin; Yi, Fan; Zhang, Suofei; Wu, Xiaofu; Zheng, Baoyu; Ou, Weihua; Latecki, Longin Jan

doi:10.1007/s11042-018-6770-2

Cited by 9 publications

(3 citation statements)

References 39 publications

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Section: Introductionmentioning

confidence: 99%

“…The Turing test is used by humans to test whether the computer could exhibit intelligence that is equivalent to or indistinguishable from humans, while the reverse Turing test is used by computers to judge whether users are humans, thus producing the words only humans could recognize. [1][2][3][4][5][6][7][8][9] The captcha (also known as Completely Automated Public Turing Test to Tell Computers and Humans Apart) is a completely automated public program that uses HCI (Human-Computer Interaction) to distinguish computers from humans. As the first defense line of Internet security, captcha is widely used to prevent fraudsters from performing illicit actions.…”

Section: Introductionmentioning

confidence: 99%

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AR CAPTCHA: Recognizing robot by augmented reality

Jin

Han

Duan

et al. 2019

Concurrency and Computation

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Summary We propose a novel AR CAPTCHA that first uses Augmented Reality to design CAPTCHA. Users should use their cameras on mobile devices to capture a marker in the 3D physical world or PC screens to find the appropriate angle to recognize each 3D character rendered on the marker in a 3D registration manner, which is very hard for machines or robots to do the same thing. Besides, we add many random 2D characters on the marker to make the state‐of‐the‐art scene text recognition methods fail to recognize the target 3D characters. To ensure availability, we design dual‐channel scenario setting (mobile phone and physical world) that enhances the security level of CAPTCHA and single‐channel scenario setting (mobile phone only) that the gyroscope could be used for our CAPTCHA. The experimental results reveal that the recognition accuracy of machines is nearly 0%, while a human could recognize AR CAPTCHA in only a few seconds.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AR CAPTCHA: Recognizing robot by augmented reality

Jin

Han

Duan

et al. 2019

Concurrency and Computation

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“…Saliency quality assessment is a kind of non-reference image quality evaluation method [1], which estimates the quality of a saliency map without ground-truth. A good saliency quality assessment method can boost the saliency detection [2] performance from multi saliency algorithms by choosing optimal saliency maps from candidates [3].…”

Section: Introductionmentioning

confidence: 99%

Multi Information Fusion Network for Saliency Quality Assessment

Tan

Meng

et al. 2019

IEICE Trans. Inf. & Syst.

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Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

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Intelligent Control of Ultrasonic Motor Using PID Control Combined with Artificial Bee Colony Type Neural Networks

Shibata

Yamamoto

et al. 2020

3rd EAI International Conference on Robotic Sensor Networks

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Learning adaptive contrast combinations for visual saliency detection

Cited by 9 publications

References 39 publications

AR CAPTCHA: Recognizing robot by augmented reality

AR CAPTCHA: Recognizing robot by augmented reality

Multi Information Fusion Network for Saliency Quality Assessment

Intelligent Control of Ultrasonic Motor Using PID Control Combined with Artificial Bee Colony Type Neural Networks

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