The Experts below are selected from a list of 2583 Experts worldwide ranked by ideXlab platform
Alberto Del Bimbo - One of the best experts on this subject based on the ideXlab platform.
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Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010Co-Authors: Walter Nunziati, Stan Sclaroff, Alberto Del BimboAbstract:Identifying correspondences between trajectory segments observed from nonsynchronized cameras is important for reconstruction of the complete trajectory of moving targets in a large scene. Such a reconstruction can be obtained from motion data by comparing the trajectory segments and estimating both the spatial and temporal alignments. Exhaustive testing of all possible correspondences of trajectories over a temporal window is only viable in the cases with a limited number of moving targets and large view overlaps. Therefore, alternative solutions are required for situations with several trajectories that are only partially visible in each view. In this paper, we propose a new method that is based on view-Invariant Representation of trajectories, which is used to produce a sparse set of salient points for trajectory segments observed in each view. Only the neighborhoods at these salient points in the view-Invariant Representation are then used to estimate the spatial and temporal alignment of trajectory pairs in different views. It is demonstrated that, for planar scenes, the method is able to recover with good precision and efficiency both spatial and temporal alignments, even given relatively small overlap between views and arbitrary (unknown) temporal shifts of the cameras. The method also provides the same capabilities in the case of trajectories that are only locally planar, but exhibit some nonplanarity at a global level.
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CIVR - An Invariant Representation for matching trajectories across uncalibrated video streams
Lecture Notes in Computer Science, 2005Co-Authors: W. Nunziati, Stan Sclaroff, Alberto Del BimboAbstract:We introduce a view–point Invariant Representation of moving object trajectories that can be used in video database applications. It is assumed that trajectories lie on a surface that can be locally approximated with a plane. Raw trajectory data is first locally–approximated with a cubic spline via least squares fitting. For each sampled point of the obtained curve, a projective Invariant feature is computed using a small number of points in its neighborhood. The resulting sequence of Invariant features computed along the entire trajectory forms the view–Invariant descriptor of the trajectory itself. Time parametrization has been exploited to compute cross ratios without ambiguity due to point ordering. Similarity between descriptors of different trajectories is measured with a distance that takes into account the statistical properties of the cross ratio, and its symmetry with respect to the point at infinity. In experiments, an overall correct classification rate of about 95% has been obtained on a dataset of 58 trajectories of players in soccer video, and an overall correct classification rate of about 80% has been obtained on matching partial segments of trajectories collected from two overlapping views of outdoor scenes with moving people and cars.
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An Invariant Representation for MatchingTrajectories across Uncalibrated Video Streams
2005Co-Authors: Stan Sclaroff, Alberto Del Bimbo, W. NunziatiAbstract:We introduce a view–point Invariant Representation of moving object trajectories that can be used in video database applications. It is assumed that trajectories lie on a surface that can be locally approximated with a plane. Raw trajectory data is first locally–approximated with a cubic spline via least squares fitting. For each sampled point of the obtained curve, a projective Invariant feature is computed using a small number of points in its neighborhood. The resulting sequence of Invariant features computed along the entire trajectory forms the view– Invariant descriptor of the trajectory itself. Time parametrization has been exploited to compute cross ratios without ambiguity due to point ordering. Similarity between descriptors of different trajectories is measured with a distance that takes into account the statistical properties of the cross ratio, and its symmetry with respect to the point at infinity. In experiments, an overall correct classification rate of about 95% has been obtained on a dataset of 58 trajectories of players in soccer video, and an overall correct classification rate of about 80% has been obtained on matching partial segments of trajectories collected from two overlapping views of outdoor scenes with moving people and cars.
Ashfaq Khokhar - One of the best experts on this subject based on the ideXlab platform.
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ICIP - Bilinear Invariant Representation for video classification and retrieval
2010 IEEE International Conference on Image Processing, 2010Co-Authors: Xu Chen, Dan Schonfeld, Ashfaq KhokharAbstract:In this paper, we present a novel bilinear Invariant Representation for video classification and retrieval. We rely on the kernel space in functional analysis to formulate a general Invariants theory. We show that null-space Invariants is a special case of the general theory when the transformation is linear. Subsequently, we derive an Invariant basis Representation for bilinear transformations. We also extend the basis Representation to tensor bilinear Invariants. We demonstrate that the proposed bilinear Invariant basis provides a much more powerful tool than null-space Invariants for video classification and retrieval when the different data elements undergo distinct transformations. Simulation results illustrate the superior performance of the proposed bilinear Invariant basis Representation compared to traditional approaches to Invariant video classification and retrieval.
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Non-linear kernel space Invariant Representation for view-Invariant motion trajectory retrieval and classification
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Xu Chen, Dan Schonfeld, Ashfaq KhokharAbstract:View-Invariant Representation has been shown to be a powerful tool in classification and retrieval of motion events due to camera motions. Traditional null space Representation is Invariant only for linear transformations and does not yield high accuracy for camera with non-linear motions. In this paper, we propose a novel general framework for non-linear kernel space Invariant Representation (NKSI), which is Invariant to non-linear transformations due to camera motions with standard perspective transformation. We first derive NKSI and then propose an efficient classification and retrieval system relying on NKSI for archiving and searching motion events consisting of motion trajectories. The simulation results demonstrate superior performance of the proposed systems over traditional approaches.
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ICASSP - Non-linear kernel space Invariant Representation for view-Invariant motion trajectory retrieval and classification
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Xu Chen, Dan Schonfeld, Ashfaq KhokharAbstract:View-Invariant Representation has been shown to be a powerful tool in classification and retrieval of motion events due to camera motions. Traditional null space Representation is Invariant only for linear transformations and does not yield high accuracy for camera with non-linear motions. In this paper, we propose a novel general framework for non-linear kernel space Invariant Representation (NKSI), which is Invariant to non-linear transformations due to camera motions with standard perspective transformation. We first derive NKSI and then propose an efficient classification and retrieval system relying on NKSI for archiving and searching motion events consisting of motion trajectories. The simulation results demonstrate superior performance of the proposed systems over traditional approaches.
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Bilinear Invariant Representation for video classification and retrieval
2010 IEEE International Conference on Image Processing, 2010Co-Authors: Xu Chen, Dan Schonfeld, Ashfaq KhokharAbstract:In this paper, we present a novel bilinear Invariant Representation for video classification and retrieval. We rely on the kernel space in functional analysis to formulate a general Invariants theory. We show that null-space Invariants is a special case of the general theory when the transformation is linear. Subsequently, we derive an Invariant basis Representation for bilinear transformations. We also extend the basis Representation to tensor bilinear Invariants. We demonstrate that the proposed bilinear Invariant basis provides a much more powerful tool than null-space Invariants for video classification and retrieval when the different data elements undergo distinct transformations. Simulation results illustrate the superior performance of the proposed bilinear Invariant basis Representation compared to traditional approaches to Invariant video classification and retrieval.
Takahiko Horiuchi - One of the best experts on this subject based on the ideXlab platform.
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Color Invariant Representation and Applications
Handbook of Research on Machine Learning Innovations and Trends, 2020Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko Horiuchi, Aboul Ella HassanienAbstract:Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant Representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-Invariant Representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the Invariant Representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the Representation for effective edge detection is introduced and compared with the state-of-the-art illumination-Invariant methods.
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ECCV Workshops (2) - An effective method for illumination-Invariant Representation of color images
Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012Co-Authors: Takahiko Horiuchi, Abdelhameed Ibrahim, Hideki Kadoi, Shoji TominagaAbstract:This paper proposes a method for illumination-Invariant Representation of natural color images. The Invariant Representation is derived, not using spectral reflectance, but using only RGB camera outputs. We suppose that the materials of target objects are composed of dielectric or metal, and the surfaces include illumination effects such as highlight, gloss, or specularity. We preset the procedure for realizing the Invariant Representation in three steps: (1) detection of specular highlight, (2) illumination color estimation, and (3) Invariant Representation for reflectance color. The performance of the proposed method is examined in experiments using real-world objects including metals and dielectrics in detail. The limitation of the method is also discussed. Finally, the proposed Representation is applied to the edge detection problem of natural color images.
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Illumination-Invariant Representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and specular highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-Invariant Representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed Representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-Invariant methods.
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SSIAI - Illumination-Invariant Representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and specular highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-Invariant Representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed Representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-Invariant methods.
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Invariant Representation for spectral reflectance images and its application
EURASIP Journal on Image and Video Processing, 2011Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko HoriuchiAbstract:Spectral images as well as color images observed from object surfaces are much influenced by various illumination conditions such as shading and specular highlight. Several Invariant Representations were proposed for these conditions using the standard dichromatic reflection model of dielectric materials. However, these Representations are inadequate for other materials like metal. This article proposes an Invariant Representation that is derived from the standard dichromatic reflection model for dielectric and the extended dichromatic reflection model for metal. We show that a normalized surface-spectral reflectance by the minimum reflectance is Invariant to highlights, shading, surface geometry, and illumination intensity. Here the illumination spectrum and the spectral sensitivity functions of the imaging system are measured in a separate way. As an application of the proposed Invariant Representation, a segmentation algorithm based on the proposed Representation is presented for effectively segmenting spectral images of natural scenes and bare circuit boards.
Abdelhameed Ibrahim - One of the best experts on this subject based on the ideXlab platform.
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Color Invariant Representation and Applications
Handbook of Research on Machine Learning Innovations and Trends, 2020Co-Authors: Abdelhameed Ibrahim, Shoji Tominaga, Takahiko Horiuchi, Aboul Ella HassanienAbstract:Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant Representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-Invariant Representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the Invariant Representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the Representation for effective edge detection is introduced and compared with the state-of-the-art illumination-Invariant methods.
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Block-based illumination-Invariant Representation for color images
Ain Shams Engineering Journal, 2016Co-Authors: Abdelhameed Ibrahim, Muhammed SalemAbstract:Abstract Reflection effects such as shading, gloss, and highlight affect the appearance of color images greatly. Therefore, image Representations Invariant to these effects were proposed for color images. Most of the conventional Invariant methods used the dichromatic reflection model assuming the presence of dielectric material in the captured image. Recently, a pixel-based Invariant Representation for color images, assuming that the image includes dielectric materials and metals, was introduced. However, the pixel-based Representation was noisy and did not have sharp edges. This paper proposes a block-based illumination-Invariant Representation for color images including dielectric materials and metals. The proposed algorithm divides image into sub-blocks and applies the Invariant equations within each block. Experiments show that the proposed algorithm has clear and sharp edges over the pixel-based algorithm. The results show the performance and stability of the proposed algorithm. As an application, the proposed Invariant method is applied to color image segmentation problem.
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Quick-shift framework for color image segmentation based on Invariant Representation
Proceedings of SPIE, 2014Co-Authors: Abdelhameed IbrahimAbstract:An automatic quick-shift framework is proposed for color image segmentation based on illumination Invariant Representation. In practice, the quick-shift method is sensitive to the choice of parameters, thus a quick tuning by hand is not sufficient. Changing parameters values make the proposed framework flexible and robust against different image characteristics. We eliminate the factors that may affect natural image acquisition such as shadow and highlight by applying an Invariant method. This method is valid for large size images. The effectiveness of the proposed framework for a variety of images including different objects of metals and dielectrics are examined in experiments.
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ECCV Workshops (2) - An effective method for illumination-Invariant Representation of color images
Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012Co-Authors: Takahiko Horiuchi, Abdelhameed Ibrahim, Hideki Kadoi, Shoji TominagaAbstract:This paper proposes a method for illumination-Invariant Representation of natural color images. The Invariant Representation is derived, not using spectral reflectance, but using only RGB camera outputs. We suppose that the materials of target objects are composed of dielectric or metal, and the surfaces include illumination effects such as highlight, gloss, or specularity. We preset the procedure for realizing the Invariant Representation in three steps: (1) detection of specular highlight, (2) illumination color estimation, and (3) Invariant Representation for reflectance color. The performance of the proposed method is examined in experiments using real-world objects including metals and dielectrics in detail. The limitation of the method is also discussed. Finally, the proposed Representation is applied to the edge detection problem of natural color images.
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Illumination-Invariant Representation for natural color images and its application
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012Co-Authors: Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji TominagaAbstract:Illumination factors such as shading, shadow, and specular highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-Invariant Representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed Representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-Invariant methods.
Stéphane Mallat - One of the best experts on this subject based on the ideXlab platform.
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Deep Scattering Spectrum
IEEE Transactions on Signal Processing, 2014Co-Authors: Joakim Andén, Stéphane MallatAbstract:A scattering transform defines a locally translation Invariant Representation which is stable to time-warping deformation. It extends MFCC Representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A frequency transposition Invariant Representation is obtained by applying a scattering transform along log-frequency. State-the-of-art classification results are obtained for musical genre and phone classification on GTZAN and TIMIT databases, respectively.
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rotation scaling and deformation Invariant scattering for texture discrimination
Computer Vision and Pattern Recognition, 2013Co-Authors: Laurent Sifre, Stéphane MallatAbstract:An affine Invariant Representation is constructed with a cascade of Invariants, which preserves information for classification. A joint translation and rotation Invariant Representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
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CVPR - Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Laurent Sifre, Stéphane MallatAbstract:An affine Invariant Representation is constructed with a cascade of Invariants, which preserves information for classification. A joint translation and rotation Invariant Representation of image patches is calculated with a scattering transform. It is implemented with a deep convolution network, which computes successive wavelet transforms and modulus non-linearities. Invariants to scaling, shearing and small deformations are calculated with linear operators in the scattering domain. State-of-the-art classification results are obtained over texture databases with uncontrolled viewing conditions.
