the need for establishing authenticity of digital images is becoming inevitably important given the ease with which images may be tempered. Moreover, the images are tampered with such expertise that it is impossible, ...
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ISBN:
(纸本)9781538630044
the need for establishing authenticity of digital images is becoming inevitably important given the ease with which images may be tempered. Moreover, the images are tampered with such expertise that it is impossible, at least visually, to figure out if they are tampered or not. In recent years, copy-move forgery has emerged as one of the most researched topics in the field of image forensics. The common detection techniques used are either block based (for smoothed regions) or keypoint based (for non-smoothed regions), each having its own share of pros and cons. While a block based approach provides higher accuracy, it is computationally taxing and fails to handle geometric transformations. Similarly, a Keypoint based approach fails to deal with smoothed regions. Our hybrid approach handles these limitations in an intelligent and adaptive way. The image fed to our software tool is adaptively segmented into semantically meaningful non-overlapped regions/segments using Simple Linear Iterative Clustering (SLIC) algorithm. Feature points as keypoints are extracted using the Scale Invariant Feature Transform (SIFT) algorithm. Given these keypoints, a segment is classified as either smoothed or non-smoothed depending upon a predetermined threshold. Once done with this classification, the proposed hybrid approach engages one of the aforementioned detection strategies to detect image forgery. Accordingly, a block based approach is implemented using Zernike moments with the matching algorithm based on Euclidean distances, while a keypoint based approach is implemented using SIFT features in tandem with the FLANN matching algorithm for the detection of matching pairs. The proposed hybrid approach has been compared with the individual approaches for an optimal value of threshold. Experimental results on different types of original as well as forged images establish that our proposed approach is able to detect image forgery in smoothed and non-smoothed images with reasonable ac
Due to the existence of the ultrasonic sensor distance error and angle error causing environment contour detection uncertainty,the ultrasonic distance measurement mode based on the foundation of the uncertainty repres...
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ISBN:
(纸本)9781509009107
Due to the existence of the ultrasonic sensor distance error and angle error causing environment contour detection uncertainty,the ultrasonic distance measurement mode based on the foundation of the uncertainty representation was firstly proposed and the data uncertainty of ultrasonic measurement was reduced by the Dezert-Smarandache Theory(DSmT).Then Hough transform and random sample consensus algorithm are employed to identify the data fusion results to obtain environmental contour ***,the experiment of ultrasonic sensors scanning the indoor environment are performed,and the contours information of indoor environment obtained by using the proposed method is consistent with the size of true *** result validly illustrates the feasibility and effectiveness of the proposed *** proposed method has certain reference value for studying the localization and environment map detection of the mobile robot by using ultrasonic sensor.
In this paper we introduce a novel framework for detecting static/moving obstacles in order to assist visually impaired/blind persons to navigate safely. Firstly, a set of interest points is extracted base on an image...
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ISBN:
(纸本)9781479912919
In this paper we introduce a novel framework for detecting static/moving obstacles in order to assist visually impaired/blind persons to navigate safely. Firstly, a set of interest points is extracted base on an image grid and tracked using the multiscale Lucas - Kanade algorithm. Next, the camera/background motion is determined through a set of homographic transforms, estimated by recursively applying the RANSAC algorithm on the interest point correspondence while other types of movements are identified using an agglomerative clustering technique. Finally, obstacles are classified as urgent/normal based on their distance to the subject and motion vectors orientation. The experimental results performed on various challenging scenes demonstrate that our approach is effective in videos with important camera movement, including noise and low resolution data.
Currently, different hand-held devices as domestic cameras, smart-phones, tablets, or on-board cameras for robots are becoming popular for video capturing. A main concern with these gadgets is undesired movement betwe...
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ISBN:
(纸本)9781479938667
Currently, different hand-held devices as domestic cameras, smart-phones, tablets, or on-board cameras for robots are becoming popular for video capturing. A main concern with these gadgets is undesired movement between consecutive frames. Video stabilization is a technique with increasing impact for solving this problem. In this paper, a proposal is introduced for robust video stabilization, in particular for on-board cameras in micro aerial vehicles. It is based on a combination of the RANSAC (randomsampleconsensus) algorithm and gray level differences as cost function for local motion parameter estimation, as well as a low-pass filter for global motion smoothing. Experimentation will illustrate about of the robustness proposed solution.
Many low-or middle-level three-dimensional reconstruction algorithms involve a robust estimation and selection step whereby parameters of the best model are estimated and inliers fitting this model are selected. The R...
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Many low-or middle-level three-dimensional reconstruction algorithms involve a robust estimation and selection step whereby parameters of the best model are estimated and inliers fitting this model are selected. The RANSAC (randomsampleconsensus) algorithm is the most widely used robust algorithm for this task. A new version of RANSAC, called distributed RANSAC (D-RANSAC), is proposed, to save computation time and improve accuracy. The authors compare their results with those of classical RANSAC and randomised RANSAC (R-RANSAC). Experiments show that D-RANSAC is superior to RANSAC and R-RANSAC in computational complexity and accuracy in most cases, particularly when the inlier proportion is below 65%.
Stereo matching is currently one of the most important research topics in domain of computer vision. The improved SURF based on stereo matching algorithm is proposed in this paper, in order to match feature points mor...
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ISBN:
(纸本)9783642319181;9783642319198
Stereo matching is currently one of the most important research topics in domain of computer vision. The improved SURF based on stereo matching algorithm is proposed in this paper, in order to match feature points more efficiently and accurately. The procedure of this method is following: Firstly, we used the algorithm based on Speeded-Up Robust Features (SURF) to detect and descript the feature points of image sequence, used normalized correlation (NCC) for the initial match. Secondly, we eliminated mismatching points by using random sample consensus algorithm (RANSAC). Lastly, we used the least square method for precision matching. Three Experiments and table analysis show that the matching accuracy of this algorithm is better than the traditional SIFT, SURF based on stereo matching algorithm and the running time is quite fast. So, it can be used in the pure software feature-point-based stereo vision system.
RANSAC (randomsampleconsensus) algorithm is widely used in the field of image mosaic. However its efficiency is not satisfying when it is processing massive date .So, to improve the efficiency of this algorithm with...
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RANSAC (randomsampleconsensus) algorithm is widely used in the field of image mosaic. However its efficiency is not satisfying when it is processing massive date .So, to improve the efficiency of this algorithm without dropping its accuracy is of great significance. And that is what our paper aims at .We adopt within-class scatter matrix to primarily select date in order to effectively improve the arithmetic speed and reduce the iterations. Our experiment shows that the speed is improved by 20 percent without reducing the accuracy.
This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network (MFNN). Almost all previous efforts to solve this problem have focused on...
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ISBN:
(纸本)9781424496365
This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network (MFNN). Almost all previous efforts to solve this problem have focused on using a training algorithm that minimizes an M-estimator based error criterion. However the robustness gained from M-estimators is still low. Using a training algorithm based on the randomsampleconsensus (RANSAC) framework improves significantly the robustness of the algorithm. However the algorithm typically requires prolonged period of time before a final solution is reached. In this paper, we propose a new strategy to improve the time performance of the RANSAC algorithm for training MFNNs. A statistical pre-test based on Wald's sequential probability ratio test (SPRT) is performed on each randomly generated sample to decide whether it deserves to be used for model estimation. The proposed algorithm is evaluated on synthetic data, contaminated with varying degrees of outliers, and have demonstrated faster performance compared to the original RANSAC algorithm with no significant sacrifice of the robustness.
The paper presents a multiple sound sources mapping system from a robot embedded microphone array. The robot localizes sound direction and recognizes what sound it is while the robot is in motion. Then the system esti...
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ISBN:
(纸本)9781424466757
The paper presents a multiple sound sources mapping system from a robot embedded microphone array. The robot localizes sound direction and recognizes what sound it is while the robot is in motion. Then the system estimates the positions of the sound sources using triangulation from a short time period of directional localization results. Three key components are denoted: 1) accurate directional localization and separation of multiple sound sources using a microphone array 2) separated sound recognition from a several tens of milliseconds input signal 3) sound position estimation using the randomsampleconsensus (RANSAC) algorithm from a tracked sound stream. By combining these techniques, the proposed system provides surrounding sound information: "Where does the sound come from?" and "What is the sound?". It works with short term signal input, and is helpful to initially notice surrounding events.
This paper proposes an improved direct fingerprint pore matching method. It measures the differences between pores by using the sparse representation technique. The coarse pore correspondences are then established and...
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