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Author | Ariel Amato; Mikhail Mozerov; Andrew Bagdanov; Jordi Gonzalez | ||||
Title | Accurate Moving Cast Shadow Suppression Based on Local Color Constancy detection | Type | Journal Article | ||
Year | 2011 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 20 | Issue | 10 | Pages | 2954 - 2966 |
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Abstract | This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ AMB2011 | Serial | 1716 | ||
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Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou | ||||
Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 23 | Issue | 1 | Pages | 83-95 |
Keywords | color constancy; CRF; multi-illuminant | ||||
Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | ||
Call Number | Admin @ si @ BRW2014 | Serial | 2451 | ||
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Author | Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers | ||||
Title | A Statistical Method for 2D Facial Landmarking | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 21 | Issue | 2 | Pages | 844-858 |
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Abstract | IF = 3.32
Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE). |
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ DSG 2012 | Serial | 1853 | ||
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Author | Noha Elfiky; Theo Gevers; Arjan Gijsenij; Jordi Gonzalez | ||||
Title | Color Constancy using 3D Scene Geometry derived from a Single Image | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 23 | Issue | 9 | Pages | 3855-3868 |
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Abstract | The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g. grey-world and white patch assumption).
In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models; images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods is selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared to the best-performing single color constancy algorithm. |
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ISSN | 1057-7149 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE; 600.078 | Approved | no | ||
Call Number | Admin @ si @ EGG2014 | Serial | 2528 | ||
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Author | Lluis Garrido; M.Guerrieri; Laura Igual | ||||
Title | Image Segmentation with Cage Active Contours | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 24 | Issue | 12 | Pages | 5557 - 5566 |
Keywords | Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates | ||||
Abstract | In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ GGI2015 | Serial | 2673 | ||
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Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
Title | Computational Color Constancy: Survey and Experiments | Type | Journal Article | ||
Year | 2011 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 20 | Issue | 9 | Pages | 2475-2489 |
Keywords | computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting | ||||
Abstract | Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ISE;CIC | Approved | no | ||
Call Number | Admin @ si @ GGW2011 | Serial | 1717 | ||
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Author | Arjan Gijsenij; R. Lu; Theo Gevers; De Xu | ||||
Title | Color Constancy for Multiple Light Source | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 21 | Issue | 2 | Pages | 697-707 |
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Abstract | Impact factor 2010: 2.92
Impact factor 2011/2012?: 3.32 Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic difference between these two illuminants is more than 1° , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%). Otherwise, when the chromatic difference is less than 1° and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods. |
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ GLG2012a | Serial | 1852 | ||
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Author | Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez | ||||
Title | Distributed Learning and Inference with Compressed Images | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 30 | Issue | Pages | 3069 - 3083 | |
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Abstract | Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task. | ||||
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Notes | LAMP; ADAS; 600.120; 600.118 | Approved | no | ||
Call Number | Admin @ si @ KEH2021 | Serial | 3543 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta | ||||
Title | Semantic Pyramids for Gender and Action Recognition | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 23 | Issue | 8 | Pages | 3633-3645 |
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Abstract | Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | CIC; LAMP; 601.160; 600.074; 600.079;MILAB | Approved | no | ||
Call Number | Admin @ si @ KWR2014 | Serial | 2507 | ||
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Author | Fahad Shahbaz Khan; Jiaolong Xu; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez | ||||
Title | Recognizing Actions through Action-specific Person Detection | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 24 | Issue | 11 | Pages | 4422-4432 |
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Abstract | Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test tim- , outperforms on both data sets state-of-the-art methods, which do use person locations. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ADAS; LAMP; 600.076; 600.079 | Approved | no | ||
Call Number | Admin @ si @ KXR2015 | Serial | 2668 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | Accurate stereo matching by two step global optimization | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 24 | Issue | 3 | Pages | 1153-1163 |
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Abstract | In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ISE; LAMP; 600.079; 600.078 | Approved | no | ||
Call Number | Admin @ si @ MoW2015a | Serial | 2568 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 24 | Issue | 12 | Pages | 5842-5853 |
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Abstract | The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | LAMP; 600.079;ISE | Approved | no | ||
Call Number | Admin @ si @ MoW2015b | Serial | 2689 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | One-view occlusion detection for stereo matching with a fully connected CRF model | Type | Journal Article | ||
Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 28 | Issue | 6 | Pages | 2936-2947 |
Keywords | Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter | ||||
Abstract | In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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Notes | LAMP; 600.098; 600.109; 602.133; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MoW2019 | Serial | 3221 | ||
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Author | Mikhail Mozerov | ||||
Title | Constrained Optical Flow Estimation as a Matching Problem | Type | Journal Article | ||
Year | 2013 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 22 | Issue | 5 | Pages | 2044-2055 |
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Abstract | In general, discretization in the motion vector domain yields an intractable number of labels. In this paper we propose an approach that can reduce general optical flow to the constrained matching problem by pre-estimating a 2D disparity labeling map of the desired discrete motion vector function. One of the goals of the proposed paper is estimating coarse distribution of motion vectors and then utilizing this distribution as global constraints for discrete optical flow estimation. This pre-estimation is done with a simple frame-to-frame correlation technique also known as the digital symmetric-phase-only-filter (SPOF). We discover a strong correlation between the output of the SPOF and the motion vector distribution of the related optical flow. The two step matching paradigm for optical flow estimation is applied: pixel accuracy (integer flow), and subpixel accuracy estimation. The matching problem is solved by global optimization. Experiments on the Middlebury optical flow datasets confirm our intuitive assumptions about strong correlation between motion vector distribution of optical flow and maximal peaks of SPOF outputs. The overall performance of the proposed method is promising and achieves state-of-the-art results on the Middlebury benchmark. | ||||
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ISSN | 1057-7149 | ISBN | Medium | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Moz2013 | Serial | 2191 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | Improved Recursive Geodesic Distance Computation for Edge Preserving Filter | Type | Journal Article | ||
Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 26 | Issue | 8 | Pages | 3696 - 3706 |
Keywords | Geodesic distance filter; color image filtering; image enhancement | ||||
Abstract | All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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Notes | LAMP; ISE; 600.120; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ Moz2017 | Serial | 2921 | ||
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