The Experts below are selected from a list of 5604 Experts worldwide ranked by ideXlab platform
Nitin Khanna - One of the best experts on this subject based on the ideXlab platform.
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source printer classification using printer specific local Texture Descriptor
IEEE Transactions on Information Forensics and Security, 2020Co-Authors: Sharad Joshi, Nitin KhannaAbstract:The knowledge of the source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. The development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, the state-of-the-art systems require that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier. In this paper, we attempt to take the first step toward overcoming this limitation. Specifically, we introduce a novel printer specific local Texture Descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model on another dataset having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments.
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source printer classification using printer specific local Texture Descriptor
arXiv: Multimedia, 2018Co-Authors: Sharad Joshi, Nitin KhannaAbstract:The knowledge of source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. Development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, state-of-the-art systems require that the font of letters present in test documents of unknown origin must be available in those used for training the classifier. In this work, we attempt to take the first step towards overcoming this limitation. Specifically, we introduce a novel printer specific local Texture Descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font, and 2) on another dataset\footnote{Code and dataset will be made publicly available with published version of this paper.} having documents printed in four different fonts, the proposed method correctly classifies all test samples when sufficient training data is available in same font setup. In addition, it outperforms state-of-the-art methods for cross font experiments. Moreover, it reduces the confusion between the printers of same brand and model.
Liming Chen - One of the best experts on this subject based on the ideXlab platform.
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robust car license plate localization using a novel Texture Descriptor
Advanced Video and Signal Based Surveillance, 2009Co-Authors: Chu Duc Nguyen, Mohsen Ardabilian, Liming ChenAbstract:This paper presents a novel Texture Descriptor based on line-segment features for text detection in images and video sequences, which is applied to build a robust car license plate localization system.Unlike most of existing approaches which use low level features (color, edge) for text / non-text discrimination, our arm is to exploit more accurate perceptual information. A - scale and rotation invariant - Texture Descriptor which describes the directionality, regularity, similarity, alignment and connectivity of group of segments are proposed. A improved algorithm for feature extraction based on local connective Hough transform has been also investigated.The robustness of our approach is proved throughout a real-time detection / verification scheme of car license plate. First, all possible candidates are detected using a rule based method, which is very robust to illumination change and in varying poses. Then, true license plates are identified by the mean of a SVM classifier trained with proposed Descriptor. Comparison and evaluation are conducted with two complex datasets.
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Real-time license plate localization based on a new scale and rotation invariant Texture Descriptor
2008Co-Authors: Chu Duc Nguyen, Mohsen Ardabilian, Liming ChenAbstract:In this paper, we present a real-time and robust license plate localization method for traffic control applications. According to our approach, edge content of gray-scale image is approximated using line segments features by means of a local connective Hough transform. Then a new, scale and rotation invariant, Texture Descriptor which describes the regularity, similarity, directionality and alignment is proposed for grouping lines segments into potential license plates. After a line-based slope estimation and correction, false candidates are eliminated by using geometrical and statistical constraints. Proposed method has been integrated in a optimal license plate localization system. Evaluation is conducted on two image databases which were taken from real scene under various configurations and variability. The result shows that our method is real-time, robust to illumination condition and viewpoint changes
Sharad Joshi - One of the best experts on this subject based on the ideXlab platform.
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source printer classification using printer specific local Texture Descriptor
IEEE Transactions on Information Forensics and Security, 2020Co-Authors: Sharad Joshi, Nitin KhannaAbstract:The knowledge of the source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. The development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, the state-of-the-art systems require that the font of letters present in the test documents of unknown origin must be available in those used for training the classifier. In this paper, we attempt to take the first step toward overcoming this limitation. Specifically, we introduce a novel printer specific local Texture Descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model on another dataset having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments.
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source printer classification using printer specific local Texture Descriptor
arXiv: Multimedia, 2018Co-Authors: Sharad Joshi, Nitin KhannaAbstract:The knowledge of source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. Development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, state-of-the-art systems require that the font of letters present in test documents of unknown origin must be available in those used for training the classifier. In this work, we attempt to take the first step towards overcoming this limitation. Specifically, we introduce a novel printer specific local Texture Descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font, and 2) on another dataset\footnote{Code and dataset will be made publicly available with published version of this paper.} having documents printed in four different fonts, the proposed method correctly classifies all test samples when sufficient training data is available in same font setup. In addition, it outperforms state-of-the-art methods for cross font experiments. Moreover, it reduces the confusion between the printers of same brand and model.
Chu Duc Nguyen - One of the best experts on this subject based on the ideXlab platform.
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robust car license plate localization using a novel Texture Descriptor
Advanced Video and Signal Based Surveillance, 2009Co-Authors: Chu Duc Nguyen, Mohsen Ardabilian, Liming ChenAbstract:This paper presents a novel Texture Descriptor based on line-segment features for text detection in images and video sequences, which is applied to build a robust car license plate localization system.Unlike most of existing approaches which use low level features (color, edge) for text / non-text discrimination, our arm is to exploit more accurate perceptual information. A - scale and rotation invariant - Texture Descriptor which describes the directionality, regularity, similarity, alignment and connectivity of group of segments are proposed. A improved algorithm for feature extraction based on local connective Hough transform has been also investigated.The robustness of our approach is proved throughout a real-time detection / verification scheme of car license plate. First, all possible candidates are detected using a rule based method, which is very robust to illumination change and in varying poses. Then, true license plates are identified by the mean of a SVM classifier trained with proposed Descriptor. Comparison and evaluation are conducted with two complex datasets.
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Real-time license plate localization based on a new scale and rotation invariant Texture Descriptor
2008Co-Authors: Chu Duc Nguyen, Mohsen Ardabilian, Liming ChenAbstract:In this paper, we present a real-time and robust license plate localization method for traffic control applications. According to our approach, edge content of gray-scale image is approximated using line segments features by means of a local connective Hough transform. Then a new, scale and rotation invariant, Texture Descriptor which describes the regularity, similarity, directionality and alignment is proposed for grouping lines segments into potential license plates. After a line-based slope estimation and correction, false candidates are eliminated by using geometrical and statistical constraints. Proposed method has been integrated in a optimal license plate localization system. Evaluation is conducted on two image databases which were taken from real scene under various configurations and variability. The result shows that our method is real-time, robust to illumination condition and viewpoint changes
Hongcheng Fan - One of the best experts on this subject based on the ideXlab platform.
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Feature based local binary pattern for rotation invariant Texture classification
Expert Systems With Applications, 2017Co-Authors: Zhibin Pan, Hongcheng FanAbstract:The paper presents a novel Texture Descriptor based on Local Binary Pattern.We address the problems of rotation invariance and illumination invariance.The proposed Texture Descriptor FbLBP is low-dimension.The results of FbLBP are compared with the state-of-the-art LBP-like variants. The local binary pattern (LBP) Descriptor is widely used in Texture analysis because of its computational simplicity and robustness to illumination changes. However, LBP has limitations to fully capture discriminative information since only the sign information of the difference vector in a local region is used. To enhance the performance of LBP, we propose a new Descriptor for Texture classificationfeature based local binary pattern (FbLBP). In the proposed FbLBP, difference vector is decomposed into sign part and magnitude part, the sign part is described by conventional LBP, while the magnitude part is described by two features of the mean and the variance of the magnitude vector. The way we extract magnitude information in difference vector shows high complementarity to the sign part and less sensitive to illumination changes with a low dimensionality. Furthermore, an adaptive local threshold is used to convert these two features into binary codes. The proposed low dimensional FbLBP is very fast to construct and no parameters are required to tune for different kinds of databases. Experimental results on four representative Texture databases of Outex, CUReT, UIUC, and XU_HR show that the proposed FbLBP achieves more than 10% improvement compared with conventional LBP and 1%3% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants.
