We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by ...We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.展开更多
Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet...Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established;then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.展开更多
Signal Detection Theory (SDT) offers an unparalleled deterministic set of decision variables necessary to formulate applied risks in transportation. SDT has distinct advantages over basic prediction models since the...Signal Detection Theory (SDT) offers an unparalleled deterministic set of decision variables necessary to formulate applied risks in transportation. SDT has distinct advantages over basic prediction models since the latter may not represent an entirely accurate analysis. Thresholds based on elements of stimulus (signal and noise) and response for: a Type I discrimination of response variable where decision outcomes and rates are computed for metacognition to discriminate a Type II of decision outcomes was set. We also adapted the classical Dijkstra's shortest path algorithm within a GIS environment using Avenue programming. Contours derived from LiDARwere used to set flood levels while satellite imagery corresponding to Red River of the North inundated (signal) areas were acquired amongst other spatial datasets. The signal information was further dichotomized using a binary yes-no model. Origin and destination points constrained within Fargo-Morehead were generated using a random point generator. From these points, trips were generated with some connected segments traversing through flooded areas. By analyzing False Alarm Rate (FAR) and Corrected Rejection (CRR) computation, we found out that, when Hit Rate (HR) and FAR are both low then there was an increased corresponding sensitivity. At 30-35 ft flood level, the values for FAR and HR was 0.97 and 0.91 respectively.When FAR〉HR, lower set flood levels offered numerous route choices. Corresponding routes with associated impedance can be classified for risk-averse drivers or risk-takers While the risk-averse avoid risky and unfavorable routes, the risk-taker optimizes at an adjustment factor of ω = 0.1 or ω = 0.2. An idealistic stage is achieved for a conservative, co, equal to 0.4 or 0.5, which indicates maximum achievement in terms of time gain and safety simultaneously. At ω = 0.0 the prevailing conditions can be considered unrealistic since they incorporate areas considered impassable with absolute resistance like segments with展开更多
To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking sche...To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking scheme with superior localization, and further discusses the probability of false rejection (PFR) and the probability of false acceptance (PFA) of the proposed scheme. Moreover, four measurements are defined to evaluate the quality of a recovered image. In the proposed algorithm, the original image is divided into 2×2 blocks to improve localization precision and decrease PFR under occurrence of random tampering. The PFR under occurrence of region tampering can be effectively decreased by randomly embedding the watermark of each block in conjunction with a novel method of tamper detection. Compared with the current self-recovery fragile watermarking algorithms, the proposed scheme not only resolves the tamper detection problem of self-embedding watermarking, but also improves the robustness against the random tampering of self-embedding watermarking. In addition, the subjective measurements are provided to evaluate the performance of the self-recovery watermarking schemes for image authentication.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61171194,61120106004)"111"Project of China(Grant No.B14010)
文摘We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.
文摘Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established;then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.
文摘Signal Detection Theory (SDT) offers an unparalleled deterministic set of decision variables necessary to formulate applied risks in transportation. SDT has distinct advantages over basic prediction models since the latter may not represent an entirely accurate analysis. Thresholds based on elements of stimulus (signal and noise) and response for: a Type I discrimination of response variable where decision outcomes and rates are computed for metacognition to discriminate a Type II of decision outcomes was set. We also adapted the classical Dijkstra's shortest path algorithm within a GIS environment using Avenue programming. Contours derived from LiDARwere used to set flood levels while satellite imagery corresponding to Red River of the North inundated (signal) areas were acquired amongst other spatial datasets. The signal information was further dichotomized using a binary yes-no model. Origin and destination points constrained within Fargo-Morehead were generated using a random point generator. From these points, trips were generated with some connected segments traversing through flooded areas. By analyzing False Alarm Rate (FAR) and Corrected Rejection (CRR) computation, we found out that, when Hit Rate (HR) and FAR are both low then there was an increased corresponding sensitivity. At 30-35 ft flood level, the values for FAR and HR was 0.97 and 0.91 respectively.When FAR〉HR, lower set flood levels offered numerous route choices. Corresponding routes with associated impedance can be classified for risk-averse drivers or risk-takers While the risk-averse avoid risky and unfavorable routes, the risk-taker optimizes at an adjustment factor of ω = 0.1 or ω = 0.2. An idealistic stage is achieved for a conservative, co, equal to 0.4 or 0.5, which indicates maximum achievement in terms of time gain and safety simultaneously. At ω = 0.0 the prevailing conditions can be considered unrealistic since they incorporate areas considered impassable with absolute resistance like segments with
基金the Program for New Century Excellent Talents in University of China (Grant No.NCET-05-0794)Southwest Jiaotong University Doctors Innovation Funds (2007)Application Basic Foundation of Sichuan Province, China (Grant No.2006 J13-10-5)
文摘To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking scheme with superior localization, and further discusses the probability of false rejection (PFR) and the probability of false acceptance (PFA) of the proposed scheme. Moreover, four measurements are defined to evaluate the quality of a recovered image. In the proposed algorithm, the original image is divided into 2×2 blocks to improve localization precision and decrease PFR under occurrence of random tampering. The PFR under occurrence of region tampering can be effectively decreased by randomly embedding the watermark of each block in conjunction with a novel method of tamper detection. Compared with the current self-recovery fragile watermarking algorithms, the proposed scheme not only resolves the tamper detection problem of self-embedding watermarking, but also improves the robustness against the random tampering of self-embedding watermarking. In addition, the subjective measurements are provided to evaluate the performance of the self-recovery watermarking schemes for image authentication.