The Experts below are selected from a list of 31203 Experts worldwide ranked by ideXlab platform
Harish Karnick - One of the best experts on this subject based on the ideXlab platform.
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video colorization using cnns and keyframes extraction an application in saving bandwidth
International Conference on Computer Vision, 2019Co-Authors: Ankur Singh, Anurag Chanani, Harish KarnickAbstract:A raw colored video takes up around three times more memory size than it’s Grayscale version. We can exploit this fact and send the Grayscale version of a colored video along with a colorization model instead of the colored video to save bandwidth usage while transmission. In this paper, we tackle the problem of colorization of Grayscale videos to reduce bandwidth usage. For this task, we use some colored keyframes as reference images from the colored version of the Grayscale video. We propose a model that extracts keyframes from a colored video and trains a Convolutional Network from scratch on these colored frames. Through the extracted keyframes we get a good knowledge of the colors that have been used in the video which helps us in colorizing the Grayscale version of the video efficiently.
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video colorization using cnns and keyframes extraction an application in saving bandwidth
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Ankur Singh, Anurag Chanani, Harish KarnickAbstract:In this paper, we tackle the problem of colorization of Grayscale videos to reduce bandwidth usage. For this task, we use some colored keyframes as reference images from the colored version of the Grayscale video. We propose a model that extracts keyframes from a colored video and trains a Convolutional Neural Network from scratch on these colored frames. Through the extracted keyframes we get a good knowledge of the colors that have been used in the video which helps us in colorizing the Grayscale version of the video efficiently. An application of the technique that we propose in this paper, is in saving bandwidth while sending raw colored videos that haven't gone through any compression. A raw colored video takes up around three times more memory size than its Grayscale version. We can exploit this fact and send a Grayscale video along with out trained model instead of a colored video. Later on, in this paper we show how this technique can help to save bandwidth usage to upto three times while transmitting raw colored videos.
Ya Qing Chen - One of the best experts on this subject based on the ideXlab platform.
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contrast enhanced transrectal ultrasonography for detection and localization of prostate index tumor correlation with radical prostatectomy findings
Urology, 2014Co-Authors: Ya Qing Chen, Jun Jiang, Yun Kai Zhu, Lifeng WangAbstract:Objective To evaluate the ability of contrast-enhanced transrectal ultrasonography (CETRUS) to detect and localize prostate index tumor. Methods Eighty-three patients with biopsy-proven prostate cancer (PCa), who were scheduled to undergo radical prostatectomy, were enrolled in this prospective study. Each patient underwent baseline Grayscale and CETRUS imaging of the prostate according to a standardized protocol before the operation. Ultrasonography findings (CETRUS and Grayscale imaging) were correlated with step-section histopathology. Results Overall, 53 and 68 tumor foci were detected by Grayscale imaging and CETRUS, respectively. The combination of Grayscale imaging and CETRUS allowed identification of 89 of the 232 cancer foci (38.4%). The sensitivity of combined imaging was significantly superior to that of Grayscale imaging ( P P P P P >.05). Conclusion Our study has demonstrated significantly improved detection of both PCa and index tumor with a combined approach of CETRUS and Grayscale imaging compared with baseline Grayscale technique only, and this technique may be applicable to focal therapy of PCa.
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contrast enhanced transrectal ultrasonography measurement of prostate cancer tumor size and correlation with radical prostatectomy specimens
International Journal of Urology, 2013Co-Authors: Ya Qing Chen, Jun Jiang, Yun Kai Zhu, Xiao Hong YaoAbstract:Objectives To determine the accuracy of contrast-enhanced transrectal ultrasonography for tumor size measurements of hypoechoic prostate cancer foci located in the peripheral zone. Methods A total of 55 men scheduled for radical prostatectomy, with biopsy-proven cancer in hypoechoic foci located in the peripheral zone, were consecutively enrolled in the present prospective study. Each patient underwent Grayscale ultrasound and contrast-enhanced transrectal ultrasonography of the prostate according to a standardized protocol. The maximum tumor diameter on Grayscale imaging and contrast-enhanced transrectal ultrasonography was compared with that determined using histopathology. Results A mean underestimation was documented to be approximately 3.9 mm and 0.6 mm for Grayscale and contrast-enhanced transrectal ultrasonography imaging, respectively. Grayscale and contrast-enhanced transrectal ultrasonography imaging underestimated measurements by 76.67% (46 of 60) and 48.33% (29 of 60), whereas overestimated measurements were 20% (12 of 60) and 26.67% (16 of 60), respectively. A strong correlation was observed between contrast-enhanced transrectal ultrasonography and histopathological measurements (r = 0.91, P 10 mm, 40% (6 of 15) and 86.67% (39 of 45) were index tumors, respectively (P < 0.0001). Conclusions Contrast-enhanced transrectal ultrasonography is significantly more accurate than conventional Grayscale imaging for measuring prostate tumor size, especially for tumors with a diameter >10 mm, and it might have a role in preoperative assessment of prostatic index tumor sizes.
Hitoshi Kiya - One of the best experts on this subject based on the ideXlab platform.
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Grayscale based block scrambling image encryption using ycbcr color space for encryption then compression systems
APSIPA Transactions on Signal and Information Processing, 2019Co-Authors: Warit Sirichotedumrong, Hitoshi KiyaAbstract:A novel Grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of Grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, generating the Grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional Grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.
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Grayscale based block scrambling image encryption using ycbcr color space for encryption then compression systems
APSIPA Transactions on Signal and Information Processing, 2019Co-Authors: Warit Sirichotedumrong, Hitoshi KiyaAbstract:A novel Grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of Grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, generating the Grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional Grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.
Ankur Singh - One of the best experts on this subject based on the ideXlab platform.
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video colorization using cnns and keyframes extraction an application in saving bandwidth
International Conference on Computer Vision, 2019Co-Authors: Ankur Singh, Anurag Chanani, Harish KarnickAbstract:A raw colored video takes up around three times more memory size than it’s Grayscale version. We can exploit this fact and send the Grayscale version of a colored video along with a colorization model instead of the colored video to save bandwidth usage while transmission. In this paper, we tackle the problem of colorization of Grayscale videos to reduce bandwidth usage. For this task, we use some colored keyframes as reference images from the colored version of the Grayscale video. We propose a model that extracts keyframes from a colored video and trains a Convolutional Network from scratch on these colored frames. Through the extracted keyframes we get a good knowledge of the colors that have been used in the video which helps us in colorizing the Grayscale version of the video efficiently.
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video colorization using cnns and keyframes extraction an application in saving bandwidth
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Ankur Singh, Anurag Chanani, Harish KarnickAbstract:In this paper, we tackle the problem of colorization of Grayscale videos to reduce bandwidth usage. For this task, we use some colored keyframes as reference images from the colored version of the Grayscale video. We propose a model that extracts keyframes from a colored video and trains a Convolutional Neural Network from scratch on these colored frames. Through the extracted keyframes we get a good knowledge of the colors that have been used in the video which helps us in colorizing the Grayscale version of the video efficiently. An application of the technique that we propose in this paper, is in saving bandwidth while sending raw colored videos that haven't gone through any compression. A raw colored video takes up around three times more memory size than its Grayscale version. We can exploit this fact and send a Grayscale video along with out trained model instead of a colored video. Later on, in this paper we show how this technique can help to save bandwidth usage to upto three times while transmitting raw colored videos.
Jun Jiang - One of the best experts on this subject based on the ideXlab platform.
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contrast enhanced transrectal ultrasonography for detection and localization of prostate index tumor correlation with radical prostatectomy findings
Urology, 2014Co-Authors: Ya Qing Chen, Jun Jiang, Yun Kai Zhu, Lifeng WangAbstract:Objective To evaluate the ability of contrast-enhanced transrectal ultrasonography (CETRUS) to detect and localize prostate index tumor. Methods Eighty-three patients with biopsy-proven prostate cancer (PCa), who were scheduled to undergo radical prostatectomy, were enrolled in this prospective study. Each patient underwent baseline Grayscale and CETRUS imaging of the prostate according to a standardized protocol before the operation. Ultrasonography findings (CETRUS and Grayscale imaging) were correlated with step-section histopathology. Results Overall, 53 and 68 tumor foci were detected by Grayscale imaging and CETRUS, respectively. The combination of Grayscale imaging and CETRUS allowed identification of 89 of the 232 cancer foci (38.4%). The sensitivity of combined imaging was significantly superior to that of Grayscale imaging ( P P P P P >.05). Conclusion Our study has demonstrated significantly improved detection of both PCa and index tumor with a combined approach of CETRUS and Grayscale imaging compared with baseline Grayscale technique only, and this technique may be applicable to focal therapy of PCa.
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contrast enhanced transrectal ultrasonography measurement of prostate cancer tumor size and correlation with radical prostatectomy specimens
International Journal of Urology, 2013Co-Authors: Ya Qing Chen, Jun Jiang, Yun Kai Zhu, Xiao Hong YaoAbstract:Objectives To determine the accuracy of contrast-enhanced transrectal ultrasonography for tumor size measurements of hypoechoic prostate cancer foci located in the peripheral zone. Methods A total of 55 men scheduled for radical prostatectomy, with biopsy-proven cancer in hypoechoic foci located in the peripheral zone, were consecutively enrolled in the present prospective study. Each patient underwent Grayscale ultrasound and contrast-enhanced transrectal ultrasonography of the prostate according to a standardized protocol. The maximum tumor diameter on Grayscale imaging and contrast-enhanced transrectal ultrasonography was compared with that determined using histopathology. Results A mean underestimation was documented to be approximately 3.9 mm and 0.6 mm for Grayscale and contrast-enhanced transrectal ultrasonography imaging, respectively. Grayscale and contrast-enhanced transrectal ultrasonography imaging underestimated measurements by 76.67% (46 of 60) and 48.33% (29 of 60), whereas overestimated measurements were 20% (12 of 60) and 26.67% (16 of 60), respectively. A strong correlation was observed between contrast-enhanced transrectal ultrasonography and histopathological measurements (r = 0.91, P 10 mm, 40% (6 of 15) and 86.67% (39 of 45) were index tumors, respectively (P < 0.0001). Conclusions Contrast-enhanced transrectal ultrasonography is significantly more accurate than conventional Grayscale imaging for measuring prostate tumor size, especially for tumors with a diameter >10 mm, and it might have a role in preoperative assessment of prostatic index tumor sizes.
