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Miandji, Ehsan
Publications (10 of 15) Show all publications
Miandji, E., Hajisharif, S. & Unger, J. (2019). A Unified Framework for Compression and Compressed Sensing of Light Fields and Light Field Videos. ACM Transactions on Graphics, 38(3), 1-18, Article ID 23.
Open this publication in new window or tab >>A Unified Framework for Compression and Compressed Sensing of Light Fields and Light Field Videos
2019 (English)In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 38, no 3, p. 1-18, article id 23Article in journal (Refereed) Published
Abstract [en]

In this article we present a novel dictionary learning framework designed for compression and sampling of light fields and light field videos. Unlike previous methods, where a single dictionary with one-dimensional atoms is learned, we propose to train a Multidimensional Dictionary Ensemble (MDE). It is shown that learning an ensemble in the native dimensionality of the data promotes sparsity, hence increasing the compression ratio and sampling efficiency. To make maximum use of correlations within the light field data sets, we also introduce a novel nonlocal pre-clustering approach that constructs an Aggregate MDE (AMDE). The pre-clustering not only improves the image quality but also reduces the training time by an order of magnitude in most cases. The decoding algorithm supports efficient local reconstruction of the compressed data, which enables efficient real-time playback of high-resolution light field videos. Moreover, we discuss the application of AMDE for compressed sensing. A theoretical analysis is presented that indicates the required conditions for exact recovery of point-sampled light fields that are sparse under AMDE. The analysis provides guidelines for designing efficient compressive light field cameras. We use various synthetic and natural light field and light field video data sets to demonstrate the utility of our approach in comparison with the state-of-the-art learning-based dictionaries, as well as established analytical dictionaries.

Place, publisher, year, edition, pages
ACM Digital Library, 2019
Keywords
Light field video compression, compressed sensing, dictionary learning, light field photography
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-158026 (URN)10.1145/3269980 (DOI)000495415600005 ()
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-11-27Bibliographically approved
Baravdish, G., Miandji, E. & Unger, J. (2019). GPU Accelerated Sparse Representation of Light Fields. In: VISIGRAPP - 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech Republic, February 25-27, 2019.: . Paper presented at VISAPP - 14th International Conference on Computer Vision Theory and Applications, Prague, Czech Republic, February 25-27, 2019. (pp. 177-182). , 4
Open this publication in new window or tab >>GPU Accelerated Sparse Representation of Light Fields
2019 (English)In: VISIGRAPP - 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech Republic, February 25-27, 2019., 2019, Vol. 4, p. 177-182Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for GPU accelerated compression of light fields. The approach is by using a dictionary learning framework for compression of light field images. The large amount of data storage by capturing light fields is a challenge to compress and we seek to accelerate the encoding routine by GPGPU computations. We compress the data by projecting each data point onto a set of trained multi-dimensional dictionaries and seek the most sparse representation with the least error. This is done by a parallelization of the tensor-matrix product computed on the GPU. An optimized greedy algorithm to suit computations on the GPU is also presented. The encoding of the data is done segmentally in parallel for a faster computation speed while maintaining the quality. The results shows an order of magnitude faster encoding time compared to the results in the same research field. We conclude that there are further improvements to increase the speed, and thus it is not too far from an interacti ve compression speed.

Keywords
Light Field Compression, Gpgpu Computation, Sparse Representation
National Category
Media and Communication Technology
Identifiers
urn:nbn:se:liu:diva-157009 (URN)10.5220/0007393101770182 (DOI)978-989-758-354-4 (ISBN)
Conference
VISAPP - 14th International Conference on Computer Vision Theory and Applications, Prague, Czech Republic, February 25-27, 2019.
Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-06-14Bibliographically approved
Hajisharif, S., Miandji, E., Per, L., Tran, K. & Unger, J. (2019). Light Field Video Compression and Real Time Rendering. Paper presented at Pacific Graphics 2019. Computer graphics forum (Print), 8, 265-276
Open this publication in new window or tab >>Light Field Video Compression and Real Time Rendering
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2019 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 8, p. 265-276Article in journal, Editorial material (Refereed) Published
Abstract [en]

Light field imaging is rapidly becoming an established method for generating flexible image based description of scene appearances. Compared to classical 2D imaging techniques, the angular information included in light fields enables effects such as post‐capture refocusing and the exploration of the scene from different vantage points. In this paper, we describe a novel GPU pipeline for compression and real‐time rendering of light field videos with full parallax. To achieve this, we employ a dictionary learning approach and train an ensemble of dictionaries capable of efficiently representing light field video data using highly sparse coefficient sets. A novel, key element in our representation is that we simultaneously compress both image data (pixel colors) and the auxiliary information (depth, disparity, or optical flow) required for view interpolation. During playback, the coefficients are streamed to the GPU where the light field and the auxiliary information are reconstructed using the dictionary ensemble and view interpolation is performed. In order to realize the pipeline we present several technical contributions including a denoising scheme enhancing the sparsity in the dataset which enables higher compression ratios, and a novel pruning strategy which reduces the size of the dictionary ensemble and leads to significant reductions in computational complexity during the encoding of a light field. Our approach is independent of the light field parameterization and can be used with data from any light field video capture system. To demonstrate the usefulness of our pipeline, we utilize various publicly available light field video datasets and discuss the medical application of documenting heart surgery.

Keywords
Computational photography, Light Fields, Light Fields Compression, Light Field Video
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-162100 (URN)10.1111/cgf.13835 (DOI)
Conference
Pacific Graphics 2019
Available from: 2019-11-19 Created: 2019-11-19 Last updated: 2019-11-19
Emadi, M., Miandji, E. & Unger, J. (2018). A Performance Guarantee for Orthogonal Matching Pursuit Using Mutual Coherence. Circuits, systems, and signal processing, 37(4), 1562-1574
Open this publication in new window or tab >>A Performance Guarantee for Orthogonal Matching Pursuit Using Mutual Coherence
2018 (English)In: Circuits, systems, and signal processing, ISSN 0278-081X, E-ISSN 1531-5878, Vol. 37, no 4, p. 1562-1574Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a new performance guarantee for the orthogonal matching pursuit (OMP) algorithm. We use mutual coherence as a metric for determining the suitability of an arbitrary overcomplete dictionary for exact recovery. Specifically, a lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise and an upper bound for the mean square error is derived. Compared to the previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a much closer correlation to empirical results of OMP.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Compressed sensing; Sparse representation; Orthogonal matching pursuit; Sparse recovery
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-147092 (URN)10.1007/s00034-017-0602-x (DOI)000427149100010 ()
Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-06-12
Miandji, E. (2018). Sparse representation of visual data for compression and compressed sensing. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Sparse representation of visual data for compression and compressed sensing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ongoing advances in computational photography have introduced a range of new imaging techniques for capturing multidimensional visual data such as light fields, BRDFs, BTFs, and more. A key challenge inherent to such imaging techniques is the large amount of high dimensional visual data that is produced, often requiring GBs, or even TBs, of storage. Moreover, the utilization of these datasets in real time applications poses many difficulties due to the large memory footprint. Furthermore, the acquisition of large-scale visual data is very challenging and expensive in most cases. This thesis makes several contributions with regards to acquisition, compression, and real time rendering of high dimensional visual data in computer graphics and imaging applications.

Contributions of this thesis reside on the strong foundation of sparse representations. Numerous applications are presented that utilize sparse representations for compression and compressed sensing of visual data. Specifically, we present a single sensor light field camera design, a compressive rendering method, a real time precomputed photorealistic rendering technique, light field (video) compression and real time rendering, compressive BRDF capture, and more. Another key contribution of this thesis is a general framework for compression and compressed sensing of visual data, regardless of the dimensionality. As a result, any type of discrete visual data with arbitrary dimensionality can be captured, compressed, and rendered in real time.

This thesis makes two theoretical contributions. In particular, uniqueness conditions for recovering a sparse signal under an ensemble of multidimensional dictionaries is presented. The theoretical results discussed here are useful for designing efficient capturing devices for multidimensional visual data. Moreover, we derive the probability of successful recovery of a noisy sparse signal using OMP, one of the most widely used algorithms for solving compressed sensing problems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 158
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1963
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-152863 (URN)10.3384/diss.diva-152863 (DOI)9789176851869 (ISBN)
Public defence
2018-12-14, Domteatern, Visualiseringscenter C, Kungsgatan 54, Campus Norrköping, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2018-11-23 Created: 2018-11-23 Last updated: 2018-11-23Bibliographically approved
Miandji, E., Emadi, M., Unger, J. & Ehsan, A. (2017). On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence. IEEE Signal Processing Letters, 24(11), 1646-1650
Open this publication in new window or tab >>On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence
2017 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 11, p. 1646-1650Article in journal (Refereed) Published
Abstract [en]

In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a closer match to empirically obtained results of the OMP algorithm.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2017
Keywords
Compressed Sensing (CS), Sparse Recovery, Orthogonal Matching Pursuit (OMP), Mutual Coherence
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-141613 (URN)10.1109/LSP.2017.2753939 (DOI)000412501600001 ()
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-11-23Bibliographically approved
Miandji, E. & Unger, J. (2016). ON NONLOCAL IMAGE COMPLETION USING AN ENSEMBLE OF DICTIONARIES. In: 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP): . Paper presented at 23rd IEEE International Conference on Image Processing (ICIP) (pp. 2519-2523). IEEE
Open this publication in new window or tab >>ON NONLOCAL IMAGE COMPLETION USING AN ENSEMBLE OF DICTIONARIES
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE , 2016, p. 2519-2523Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we consider the problem of nonlocal image completion from random measurements and using an ensemble of dictionaries. Utilizing recent advances in the field of compressed sensing, we derive conditions under which one can uniquely recover an incomplete image with overwhelming probability. The theoretical results are complemented by numerical simulations using various ensembles of analytical and training-based dictionaries.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Conference on Image Processing ICIP, ISSN 1522-4880
Keywords
compressed sensing; image completion; nonlocal; inverse problems; uniqueness conditions
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-134107 (URN)10.1109/ICIP.2016.7532813 (DOI)000390782002114 ()978-1-4673-9961-6 (ISBN)
Conference
23rd IEEE International Conference on Image Processing (ICIP)
Available from: 2017-01-22 Created: 2017-01-22 Last updated: 2018-11-23
Miandji, E., Kronander, J. & Unger, J. (2015). Compressive Image Reconstruction in Reduced Union of Subspaces. Paper presented at Eurographics 2015. Computer Graphics Forum, 34(2), 33-44
Open this publication in new window or tab >>Compressive Image Reconstruction in Reduced Union of Subspaces
2015 (English)In: Computer Graphics Forum, ISSN 1467-8659, Vol. 34, no 2, p. 33-44Article in journal (Refereed) Published
Abstract [en]

We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light-fields. The algorithm relies on a learning-based basis representation. We train an ensemble of intrinsically two-dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K-SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light-fields). We show that our method outperforms state-of-the-art algorithms in computer graphics and image processing literature.

Place, publisher, year, edition, pages
John Wiley & Sons Ltd, 2015
Keywords
Image reconstruction, compressed sensing, light field imaging
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-119639 (URN)10.1111/cgf.12539 (DOI)000358326600008 ()
Conference
Eurographics 2015
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IIS11-0081
Available from: 2015-06-23 Created: 2015-06-23 Last updated: 2018-11-23Bibliographically approved
Mohseni, S., Zarei, N. & Miandji, E. (2015). Facial Expression Recognition Using Facial Graph. In: FACE AND FACIAL EXPRESSION RECOGNITION FROM REAL WORLD VIDEOS: . Paper presented at International Workshop on Face and Facial Expression Recognition from Real World Videos (FFER) (pp. 58-66). SPRINGER-VERLAG BERLIN, 8912
Open this publication in new window or tab >>Facial Expression Recognition Using Facial Graph
2015 (English)In: FACE AND FACIAL EXPRESSION RECOGNITION FROM REAL WORLD VIDEOS, SPRINGER-VERLAG BERLIN , 2015, Vol. 8912, p. 58-66Conference paper, Published paper (Refereed)
Abstract [en]

Automatic analysis of human facial expression is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we developed a new method for automatic facial expression recognition based on verifying movable facial elements and tracking nodes in sequential frames. The algorithm plots a face model graph in each frame and extracts features by measuring the ratio of the facial graph sides. Seven facial expressions, including neutral pose are being classified in this study using support vector machine and other classifiers on JAFFE databases. The approach does not rely on action units, and therefore eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of action units. Experimental results show that analyzing facial movements gives accurate and efficient information in order to identify different facial expressions.

Place, publisher, year, edition, pages
SPRINGER-VERLAG BERLIN, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Facial expression analysis; Facial feature points; Facial graph; Support vector machine; Adaboost classifier
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-122124 (URN)10.1007/978-3-319-13737-7_6 (DOI)000361702900006 ()978-3-319-13737-7 (ISBN)978-3-319-13736-0 (ISBN)
Conference
International Workshop on Face and Facial Expression Recognition from Real World Videos (FFER)
Available from: 2015-10-19 Created: 2015-10-19 Last updated: 2018-07-19
Kronander, J., Banterle, F., Gardner, A., Miandji, E. & Unger, J. (2015). Photorealistic rendering of mixed reality scenes. Paper presented at The 36th Annual Conference of the European Association of Computer Graphics, Eurographics 2015, Zürich, Switzerland, 4th–8th May 2015. Computer graphics forum (Print), 34(2), 643-665
Open this publication in new window or tab >>Photorealistic rendering of mixed reality scenes
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2015 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 34, no 2, p. 643-665Article in journal (Refereed) Published
Abstract [en]

Photo-realistic rendering of virtual objects into real scenes is one of the most important research problems in computer graphics. Methods for capture and rendering of mixed reality scenes are driven by a large number of applications, ranging from augmented reality to visual effects and product visualization. Recent developments in computer graphics, computer vision, and imaging technology have enabled a wide range of new mixed reality techniques including methods of advanced image based lighting, capturing spatially varying lighting conditions, and algorithms for seamlessly rendering virtual objects directly into photographs without explicit measurements of the scene lighting. This report gives an overview of the state-of-the-art in this field, and presents a categorization and comparison of current methods. Our in-depth survey provides a tool for understanding the advantages and disadvantages of each method, and gives an overview of which technique is best suited to a specific problem.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2015
Keywords
Picture/Image Generation—Illumination Estimation, Image-Based Lighting, Reflectance and Shading
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-118542 (URN)10.1111/cgf.12591 (DOI)000358326600060 ()
Conference
The 36th Annual Conference of the European Association of Computer Graphics, Eurographics 2015, Zürich, Switzerland, 4th–8th May 2015
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IIS11-0081Linnaeus research environment CADICS
Available from: 2015-05-31 Created: 2015-05-31 Last updated: 2017-12-04Bibliographically approved
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