The Experts below are selected from a list of 255648 Experts worldwide ranked by ideXlab platform
Zeev Zalevsky - One of the best experts on this subject based on the ideXlab platform.
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Optical Spatial Image processor based on aliasing of pseudo-periodic sampling
The Journal of Supercomputing, 2010Co-Authors: Alexander Zlotnik, Melania Paturzo, Pietro Ferraro, Zeev ZalevskyAbstract:In this paper, we present a new configuration for a real-time Spatial Image processor that is based upon a Spatially incoherent imaging setup in which a grating is attached to the object plane. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect along the non-uniform digital sampling realizes a tunable spectral distribution that is applied over the spectrum of the object. Preliminary numerical demonstration of the operation principle is provided.
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Aliasing-based incoherent optical Spatial Image processor
Applied optics, 2009Co-Authors: Melania Paturzo, Pietro Ferraro, Alexander Zlotnik, Zeev ZalevskyAbstract:We present a configuration for a real-time Spatial Image processor that is based upon an imaging setup in which a grating with Fourier coefficients with tunable phase is attached to the object plane. The illumination that is used for the proposed concept is Spatially incoherent. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect due to the digital sampling realizes a nonuniform and tunable spectral distribution (a filter) that is applied over the spectrum of the object. Preliminary numerical and experimental demonstration of the operation principle is provided with a Spatial LiNbO3 hexagonal grating.
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OSC - Incoherent Optical Spatial Image Processing
Lecture Notes in Computer Science, 2009Co-Authors: Melania Paturzo, Pietro Ferraro, Alexander Zlotnik, Zeev ZalevskyAbstract:In this paper we present a new configuration for real-time Spatial Image processor that is based upon an imaging setup in which a grating with Fourier coefficients with tunable phase is attached to the object plane. The illumination that is used for the proposed concept is Spatially incoherent. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect due to the digital sampling realizes a non uniform and tunable spectral distribution (a filter) that is applied over the spectrum of the object.
Muhammad Sajid - One of the best experts on this subject based on the ideXlab platform.
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a hybrid geometric Spatial Image representation for scene classification
PLOS ONE, 2018Co-Authors: Nouman Ali, Bushra Zafar, Faisal Riaz, Saadat Hanif Dar, Naeem Iqbal Ratyal, Khalid Bashir Bajwa, Muhammad Kashif Iqbal, Muhammad SajidAbstract:The recent development in the technology has increased the complexity of Image contents and demand for Image classification becomes more imperative. Digital Images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and Image representation is considered as an important step in scene analysis as it affects the Image classification performance. Automatic classification of Images is an open research problem for Image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve Image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an Image contains no information about the co-occurrence of features in the 2D Image space. This is considered as a limitation, as the Spatial arrangement among visual words in Image space contains the information that is beneficial for Image representation and learning of classification model. To deal with this, researchers have proposed different Image representations. Among these, the division of Image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of Spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the Image. Five standard Image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.
Jiwu Huang - One of the best experts on this subject based on the ideXlab platform.
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a strategy of clustering modification directions in Spatial Image steganography
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Ming Wang, Shunquan Tan, Jiwu HuangAbstract:Most of the recently proposed steganographic schemes are based on minimizing an additive distortion function defined as the sum of embedding costs for individual pixels. In such an approach, mutual embedding impacts are often ignored. In this paper, we present an approach that can exploit the interactions among embedding changes in order to reduce the risk of detection by steganalysis. It employs a novel strategy, called clustering modification directions (CMDs), based on the assumption that when embedding modifications in heavily textured regions are locally heading toward the same direction, the steganographic security might be improved. To implement the strategy, a cover Image is decomposed into several subImages, in which message segments are embedded with well-known schemes using additive distortion functions. The costs of pixels are updated dynamically to take mutual embedding impacts into account. Specifically, when neighboring pixels are changed toward a positive/negative direction, the cost of the considered pixel is biased toward the same direction. Experimental results show that our proposed CMD strategy, incorporated into existing steganographic schemes, can effectively overcome the challenges posed by the modern steganalyzers with high-dimensional features.
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a new cost function for Spatial Image steganography
International Conference on Image Processing, 2014Co-Authors: Ming Wang, Jiwu HuangAbstract:A well defined cost function is crucial to steganography under the scenario of minimizing embedding distortion. In this paper, we present a new cost function for Spatial Image steganography. The proposed cost function is designed by using a high-pass filter to locate the less predictable parts in an Image, and then using two low-pass filters to make the low cost values more clustered. Experiments show that the steganographic method with the proposed cost function makes the embedding changes more concentrated in texture regions, and thus achieves a better performance on resisting the state-of-the-art steganalysis over prior works, including HUGO, WOW, and S-UNIWARD.
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ICIP - A new cost function for Spatial Image steganography
2014 IEEE International Conference on Image Processing (ICIP), 2014Co-Authors: Ming Wang, Jiwu HuangAbstract:A well defined cost function is crucial to steganography under the scenario of minimizing embedding distortion. In this paper, we present a new cost function for Spatial Image steganography. The proposed cost function is designed by using a high-pass filter to locate the less predictable parts in an Image, and then using two low-pass filters to make the low cost values more clustered. Experiments show that the steganographic method with the proposed cost function makes the embedding changes more concentrated in texture regions, and thus achieves a better performance on resisting the state-of-the-art steganalysis over prior works, including HUGO, WOW, and S-UNIWARD.
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Investigation on Cost Assignment in Spatial Image Steganography
IEEE Transactions on Information Forensics and Security, 2014Co-Authors: Shunquan Tan, Ming Wang, Jiwu HuangAbstract:Relating the embedding cost in a distortion function to statistical detectability is an open vital problem in modern steganography. In this paper, we take one step forward by formulating the process of cost assignment into two phases: 1) determining a priority profile and 2) specifying a cost-value distribution. We analytically show that the cost-value distribution determines the change rate of cover elements. Furthermore, when the cost-values are specified to follow a uniform distribution, the change rate has a linear relation with the payload, which is a rare property for content-adaptive steganography. In addition, we propose some rules for ranking the priority profile for Spatial Images. Following such rules, we propose a five-step cost assignment scheme. Previous steganographic schemes, such as HUGO, WOW, S-UNIWARD, and MG, can be integrated into our scheme. Experimental results demonstrate that the proposed scheme is capable of better resisting steganalysis equipped with high-dimensional rich model features.
Nouman Ali - One of the best experts on this subject based on the ideXlab platform.
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a hybrid geometric Spatial Image representation for scene classification
PLOS ONE, 2018Co-Authors: Nouman Ali, Bushra Zafar, Faisal Riaz, Saadat Hanif Dar, Naeem Iqbal Ratyal, Khalid Bashir Bajwa, Muhammad Kashif Iqbal, Muhammad SajidAbstract:The recent development in the technology has increased the complexity of Image contents and demand for Image classification becomes more imperative. Digital Images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and Image representation is considered as an important step in scene analysis as it affects the Image classification performance. Automatic classification of Images is an open research problem for Image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve Image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an Image contains no information about the co-occurrence of features in the 2D Image space. This is considered as a limitation, as the Spatial arrangement among visual words in Image space contains the information that is beneficial for Image representation and learning of classification model. To deal with this, researchers have proposed different Image representations. Among these, the division of Image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of Spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the Image. Five standard Image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.
Melania Paturzo - One of the best experts on this subject based on the ideXlab platform.
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Optical Spatial Image processor based on aliasing of pseudo-periodic sampling
The Journal of Supercomputing, 2010Co-Authors: Alexander Zlotnik, Melania Paturzo, Pietro Ferraro, Zeev ZalevskyAbstract:In this paper, we present a new configuration for a real-time Spatial Image processor that is based upon a Spatially incoherent imaging setup in which a grating is attached to the object plane. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect along the non-uniform digital sampling realizes a tunable spectral distribution that is applied over the spectrum of the object. Preliminary numerical demonstration of the operation principle is provided.
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Aliasing-based incoherent optical Spatial Image processor
Applied optics, 2009Co-Authors: Melania Paturzo, Pietro Ferraro, Alexander Zlotnik, Zeev ZalevskyAbstract:We present a configuration for a real-time Spatial Image processor that is based upon an imaging setup in which a grating with Fourier coefficients with tunable phase is attached to the object plane. The illumination that is used for the proposed concept is Spatially incoherent. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect due to the digital sampling realizes a nonuniform and tunable spectral distribution (a filter) that is applied over the spectrum of the object. Preliminary numerical and experimental demonstration of the operation principle is provided with a Spatial LiNbO3 hexagonal grating.
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OSC - Incoherent Optical Spatial Image Processing
Lecture Notes in Computer Science, 2009Co-Authors: Melania Paturzo, Pietro Ferraro, Alexander Zlotnik, Zeev ZalevskyAbstract:In this paper we present a new configuration for real-time Spatial Image processor that is based upon an imaging setup in which a grating with Fourier coefficients with tunable phase is attached to the object plane. The illumination that is used for the proposed concept is Spatially incoherent. By proper adjusting of the magnification of the imaging system to the Spatial period of the grating and the sampling grid of the camera, the aliasing effect due to the digital sampling realizes a non uniform and tunable spectral distribution (a filter) that is applied over the spectrum of the object.
