The Experts below are selected from a list of 14766 Experts worldwide ranked by ideXlab platform
Farid Boussaid - One of the best experts on this subject based on the ideXlab platform.
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Keypoints based surface representation for 3d modeling and 3d object recognition
Pattern Recognition, 2017Co-Authors: Syed Afaq Ali Shah, Mohammed Bennamoun, Farid BoussaidAbstract:The three-dimensional (3D) modeling and recognition of 3D objects have been traditionally performed using local features to represent the underlying 3D surface. Extraction of features requires cropping of several local surface patches around detected Keypoints. Although an important step, the extraction and representation of such local patches adds to the computational complexity of the algorithms. This paper proposes a novel Keypoints-based Surface Representation (KSR) technique. The proposed technique has the following two characteristics: (1) It does not rely on the computation of features on a small surface patch cropped around a detected keypoint. Rather, it exploits the geometrical relationship between the detected 3D Keypoints for local surface representation. (2) KSR is computationally efficient, requiring only seconds to process 3D models with over 50,000 points with a MATLAB implementation. Experimental results on the UWA and Stanford 3D models dataset suggest that it can accurately perform pairwise and multiview range image registration (3D modeling). KSR was also tested for 3D object recognition with occluded scenes. Recognition results on the UWA dataset show that the proposed technique outperforms existing methods including 3D-Tensor, VD-LSD, keypoint-depth based feature, spherical harmonics and spin image with a recognition rate of 95.9%. The proposed approach also achieves a recognition rate of 93.5% on the challenging Ca'Fascori dataset compared to 92.5% achieved by game-theoretic. The proposed method is computationally efficient compared to state-of-the-art local feature methods. HighlightsWe propose a novel technique, called Keypoint-based Surface Representation (KSR).The proposed technique does not require local features around detected Keypoints.KSR exploits geometrical relationships between Keypoints for surface representation.KSR is tested on 3 popular datasets for 3D modeling and 3D object recognition.KSR achieves superior 3D modeling and recognition results.
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a curvelet based approach for textured 3d face recognition
Pattern Recognition, 2015Co-Authors: S Elaiwat, Mohammed Bennamoun, Farid Boussaid, Amar A ElsallamAbstract:In this paper, we present a fully automated multimodal Curvelet-based approach for textured 3D face recognition. The proposed approach relies on a novel multimodal keypoint detector capable of repeatably identifying Keypoints on textured 3D face surfaces. Unique local surface descriptors are then constructed around each detected keypoint by integrating Curvelet elements of different orientations, resulting in highly descriptive rotation invariant features. Unlike previously reported Curvelet-based face recognition algorithms which extract global features from textured faces only, our algorithm extracts both texture and 3D local features. In addition, this is achieved across a number of frequency bands to achieve robust and accurate recognition under varying illumination conditions and facial expressions. The proposed algorithm was evaluated using three well-known and challenging datasets, namely FRGC v2, BU-3DFE and Bosphorus datasets. Reported results show superior performance compared to prior art, with 99.2%, 95.1% and 91% verification rates at 0.001 FAR for FRGC v2, BU-3DFE and Bosphorus datasets, respectively. HighlightsIdentifying distinctive Keypoints on textured 3D face surfaces rich with features.These Keypoints are identified in the Curvelet domain across mid-frequency bands.The repeatability of these Keypoints is high in both neutral and nonneutral faces.Building local surface descriptors around the Keypoints in the Curvelet domain.Reported results show superior performance on three datasets, namely FRGC, BU-3DFE and Bosphorus, compared to prior art.
Axel Ehrhold - One of the best experts on this subject based on the ideXlab platform.
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Key-point Based Analysis of Sonar Images : Application to Seabed Recognition
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Huu-giao Nguyen, Ronan Fablet, J.-m. Boucher, Axel EhrholdAbstract:In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the Keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual Keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian Keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures.
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Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Huu-giao Nguyen, Ronan Fablet, Axel Ehrhold, J.-m. BoucherAbstract:In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the Keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual Keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian Keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures.
Mauro Barni - One of the best experts on this subject based on the ideXlab platform.
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Forensic Analysis of SIFT Keypoint Removal and Injection
IEEE Transactions on Information Forensics and Security, 2014Co-Authors: Andrea Costanzo, Irene Amerini, Roberto Caldelli, Mauro BarniAbstract:Attacks capable of removing SIFT Keypoints from images have been recently devised with the intention of compromising the correct functioning of SIFT-based copy-move forgery detection. To tackle with these attacks, we propose three novel forensic detectors for the identification of images whose SIFT Keypoints have been globally or locally removed. The detectors look for inconsistencies like the absence or anomalous distribution of Keypoints within textured image regions. We first validate the methods on state-of-the-art keypoint removal techniques, then we further assess their robustness by devising a counter-forensic attack injecting fake SIFT Keypoints in the attempt to cover the traces of removal. We apply the detectors to a practical image forensic scenario of SIFT-based copy-move forgery detection, assuming the presence of a counterfeiter who resorts to keypoint removal and injection to create copy-move forgeries that successfully elude SIFT-based detectors but are in turn exposed by the newly proposed tools.
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Removal and injection of Keypoints for SIFT-based copy-move counter-forensics
EURASIP Journal on Information Security, 2013Co-Authors: Irene Amerini, Mauro Barni, Roberto Caldelli, Andrea CostanzoAbstract:Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing Keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT Keypoints in an image whose authentic Keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few Keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection.
Huu-giao Nguyen - One of the best experts on this subject based on the ideXlab platform.
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Key-point Based Analysis of Sonar Images : Application to Seabed Recognition
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Huu-giao Nguyen, Ronan Fablet, J.-m. Boucher, Axel EhrholdAbstract:In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the Keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual Keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian Keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures.
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Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Huu-giao Nguyen, Ronan Fablet, Axel Ehrhold, J.-m. BoucherAbstract:In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the Keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual Keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian Keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures.
Andrea Costanzo - One of the best experts on this subject based on the ideXlab platform.
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Forensic Analysis of SIFT Keypoint Removal and Injection
IEEE Transactions on Information Forensics and Security, 2014Co-Authors: Andrea Costanzo, Irene Amerini, Roberto Caldelli, Mauro BarniAbstract:Attacks capable of removing SIFT Keypoints from images have been recently devised with the intention of compromising the correct functioning of SIFT-based copy-move forgery detection. To tackle with these attacks, we propose three novel forensic detectors for the identification of images whose SIFT Keypoints have been globally or locally removed. The detectors look for inconsistencies like the absence or anomalous distribution of Keypoints within textured image regions. We first validate the methods on state-of-the-art keypoint removal techniques, then we further assess their robustness by devising a counter-forensic attack injecting fake SIFT Keypoints in the attempt to cover the traces of removal. We apply the detectors to a practical image forensic scenario of SIFT-based copy-move forgery detection, assuming the presence of a counterfeiter who resorts to keypoint removal and injection to create copy-move forgeries that successfully elude SIFT-based detectors but are in turn exposed by the newly proposed tools.
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Removal and injection of Keypoints for SIFT-based copy-move counter-forensics
EURASIP Journal on Information Security, 2013Co-Authors: Irene Amerini, Mauro Barni, Roberto Caldelli, Andrea CostanzoAbstract:Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing Keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT Keypoints in an image whose authentic Keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few Keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection.
