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See detailSuper-Resolution Approaches for Depth Video Enhancement
Al Ismaeil, Kassem UL

Doctoral thesis (2015)

Sensing using 3D technologies has seen a revolution in the past years where cost-effective depth sensors are today part of accessible consumer electronics. Their ability in directly capturing depth videos ... [more ▼]

Sensing using 3D technologies has seen a revolution in the past years where cost-effective depth sensors are today part of accessible consumer electronics. Their ability in directly capturing depth videos in real-time has opened tremendous possibilities for multiple applications in computer vision. These sensors, however, have major shortcomings due to their high noise contamination, including missing and jagged measurements, and their low spatial resolutions. In order to extract detailed 3D features from this type of data, a dedicated data enhancement is required. We propose a generic depth multi-frame super-resolution framework that addresses the limitations of state-of-the-art depth enhancement approaches. The proposed framework does not need any additional hardware or coupling with di erent modalities. It is based on a new data model that uses densely upsampled low resolution observations. This results in a robust median initial estimation, further refined by a deblurring operation using a bilateral total variation as the regularization term. The upsampling operation ensures a systematic improvement in the registration accuracy. This is explored in different scenarios based on the motions involved in the depth video. For the general and most challenging case of objects deforming non-rigidly in full 3D, we propose a recursive dynamic multi-frame super-resolution algorithm where the relative local 3D motions between consecutive frames are directly accounted for. We rely on the assumption that these 3D motions can be decoupled into lateral motions and radial displacements. This allows to perform a simple local per-pixel tracking where both depth measurements and deformations are optimized. As compared to alternative approaches, the results show a clear improvement in reconstruction accuracy and in robustness to noise, to relative large non-rigid deformations, and to topological changes. Moreover, the proposed approach, implemented on a CPU, is shown to be computationally efficient and working in real-time. [less ▲]

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See detailReal-Time Non-Rigid Multi-Frame Depth Video Super-Resolution
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Solignac, Thomas et al

in IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'15), (Best paper award) (2015, June 12)

This paper proposes to enhance low resolution dynamic depth videos containing freely non–rigidly moving objects with a new dynamic multi–frame super–resolution algorithm. Existent methods are either ... [more ▼]

This paper proposes to enhance low resolution dynamic depth videos containing freely non–rigidly moving objects with a new dynamic multi–frame super–resolution algorithm. Existent methods are either limited to rigid objects, or restricted to global lateral motions discarding radial displacements. We address these shortcomings by accounting for non–rigid displacements in 3D. In addition to 2D optical flow, we estimate the depth displacement, and simultaneously correct the depth measurement by Kalman filtering. This concept is incorporated efficiently in a multi–frame super–resolution framework. It is formulated in a recursive manner that ensures an efficient deployment in real–time. Results show the overall improved performance of the proposed method as compared to alternative approaches, and specifically in handling relatively large 3D motions. Test examples range from a full moving human body to a highly dynamic facial video with varying expressions. [less ▲]

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See detailPatch-based Statistical Performance Analysis of Upsampling for Precise Super–Resolution
Aouada, Djamila UL; Al Ismaeil, Kassem UL; Ottersten, Björn UL

in 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'15) (2015, March)

All existent methods for the statistical analysis of super–resolution approaches have stopped at the variance term, not accounting for the bias in the mean square error. In this paper we give an original ... [more ▼]

All existent methods for the statistical analysis of super–resolution approaches have stopped at the variance term, not accounting for the bias in the mean square error. In this paper we give an original derivation of the bias term. We propose to use a patch-based method inspired by the work of (Chatterjee and Milanfar, 2009). Our approach, however, is completely new as we derive a new affine bias model dedicated for the multi-frame super resolution framework. We apply the proposed statistical performance analysis to the Upsampling for Precise Super–Resolution (UP-SR) algorithm. This algorithm was shown experimentally to be a good solution for enhancing the resolution of depth sequences in both cases of global and local motions. Its performance is herein analyzed theoretically in terms of its approximated mean square error, using the proposed derivation of the bias. This analysis is validated experimentally on simulated static and dynamic depth sequences with a known ground truth. This provides an insightful understanding of the effects of noise variance, number of observed low resolution frames, and super–resolution factor on the final and intermediate performance of UP–SR. Our conclusion is that increasing the number of frames should improve the performance while the error is increased due to local motions, and to the upsampling which is part of UP-SR. [less ▲]

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See detailSurface UP-SR for an Improved Face Recognition Using Low Resolution Depth Cameras
Aouada, Djamila UL; Al Ismaeil, Kassem UL; Kedir Idris, Kedija et al

in 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS'14) (2014)

We address the limitation of low resolution depth cameras in the context of face recognition. Considering a face as a surface in 3-D, we reformulate the recently proposed Upsampling for Precise ... [more ▼]

We address the limitation of low resolution depth cameras in the context of face recognition. Considering a face as a surface in 3-D, we reformulate the recently proposed Upsampling for Precise Super–Resolution algorithm as a new approach on three dimensional points. This reformulation allows an efficient implementation, and leads to a largely enhanced 3-D face reconstruction. Moreover, combined with a dedicated face detection and representation pipeline, the proposed method provides an improved face recognition system using low resolution depth cameras. We show experimentally that this system increases the face recognition rate as compared to directly using the low resolution raw data. [less ▲]

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See detailKinectDeform: Enhanced 3D Reconstruction of Non-Rigidly Deforming Objects
Afzal, Hassan UL; Al Ismaeil, Kassem UL; Aouada, Djamila UL et al

in Second International Conference on 3D Vision (2014)

In this work we propose KinectDeform, an algorithm which targets enhanced 3D reconstruction of scenes containing non-rigidly deforming objects. It provides an innovation to the existing class of ... [more ▼]

In this work we propose KinectDeform, an algorithm which targets enhanced 3D reconstruction of scenes containing non-rigidly deforming objects. It provides an innovation to the existing class of algorithms which either target scenes with rigid objects only or allow for very limited non-rigid deformations or use pre-computed templates to track them. KinectDeform combines a fast non-rigid scene tracking algorithm based on octree data representation and hierarchical voxel associations with a recursive data filtering mechanism. We analyze its performance on both real and simulated data and show improved results in terms of smoothness and feature preserving 3D reconstructions with reduced noise. [less ▲]

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See detailDynamic Super Resolution of Depth Sequences with Non-Rigid Motions
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Mirbach, Bruno et al

in 20th International Conference on Image Processing (2013, September)

We enhance the resolution of depth videos acquired with low resolution time-of-flight cameras. To that end, we propose a new dedicated dynamic super-resolution that is capable to accurately super-resolve a ... [more ▼]

We enhance the resolution of depth videos acquired with low resolution time-of-flight cameras. To that end, we propose a new dedicated dynamic super-resolution that is capable to accurately super-resolve a depth sequence containing one or multiple moving objects without strong constraints on their shape or motion, thus clearly outperforming any existing super-resolution techniques that perform poorly on depth data and are either restricted to global motions or not precise because of an implicit estimation of motion. Our proposed approach is based on a new data model that leads to a robust registration of all depth frames after a dense upsampling. The texture-less nature of depth images allows to robustly handle sequences with multiple moving objects as confirmed by our experiments. [less ▲]

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See detailDepth Super-Resolution by Enhanced Shift and Add
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Mirbach, Bruno et al

in Computer Analysis of Images and Patterns, 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II (2013)

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See detailMutli-Frame Super-Resolution by Enhanced Shift & Add
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Mirbach, Bruno et al

in 8th International Symposium on Image and Signal Processing and Analysis (2013)

A critical step in multi-frame super-resolution is the registration of frames based on their motion. We improve the performance of current state-of-the-art super-resolution techniques by proposing a more ... [more ▼]

A critical step in multi-frame super-resolution is the registration of frames based on their motion. We improve the performance of current state-of-the-art super-resolution techniques by proposing a more robust and accurate registration as early as in the initialization stage of the high resolution estimate. Indeed, we solve the limitations on scale and motion inherent to the classical Shift & Add approach by upsampling the low resolution frames up to the super-resolution factor prior to estimating motion or to median filtering. This is followed by an appropriate selective optimization, leading to an enhanced Shift & Add. Quantitative and qualitative evaluations have been conducted at two levels; the initial estimation and the final optimized super-resolution. Results show that the proposed algorithm outperforms existing state-of-art methods. [less ▲]

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See detailMulti-frame super-resolution by enhanced shift & add
Al Ismaeil, Kassem UL; Aouada, D.; Ottersten, Björn UL et al

in International Symposium on Image and Signal Processing and Analysis, ISPA (2013)

A critical step in multi-frame super-resolution is the registration of frames based on their motion. We improve the performance of current state-of-the-art super-resolution techniques by proposing a more ... [more ▼]

A critical step in multi-frame super-resolution is the registration of frames based on their motion. We improve the performance of current state-of-the-art super-resolution techniques by proposing a more robust and accurate registration as early as in the initialization stage of the high resolution estimate. Indeed, we solve the limitations on scale and motion inherent to the classical Shift & Add approach by upsampling the low resolution frames up to the super-resolution factor prior to estimating motion or to median filtering. This is followed by an appropriate selective optimization, leading to an enhanced Shift & Add. Quantitative and qualitative evaluations have been conducted at two levels; the initial estimation and the final optimized superresolution. Results show that the proposed algorithm outperforms existing state-of-art methods. © 2013 University of Trieste and University of Zagreb. [less ▲]

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See detailBilateral Filter Evaluation Based on Exponential Kernels
Al Ismaeil, Kassem UL; Aouada, Djamila UL; Mirbach, Bruno et al

in Pattern Recognition (ICPR), 2012 21st International Conference on (2012)

The well-known bilateral filter is used to smooth noisy images while keeping their edges. This filter is commonly used with Gaussian kernel functions without real justification. The choice of the kernel ... [more ▼]

The well-known bilateral filter is used to smooth noisy images while keeping their edges. This filter is commonly used with Gaussian kernel functions without real justification. The choice of the kernel functions has a major effect on the filter behavior. We propose to use exponential kernels with L1 distances instead of Gaussian ones. We derive Stein's Unbiased Risk Estimate to find the optimal parameters of the new filter and compare its performance with the conventional one. We show that this new choice of the kernels has a comparable smoothing effect but with sharper edges due to the faster, smoothly decaying kernels. [less ▲]

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See detailA New Set of Quartic Trivariate Polynomial Equations for Stratied Camera Self-calibrationunder Zero-Skew and Constant Parameters Assumptions
Habed, Adlane; Al Ismaeil, Kassem UL; Fofi, David

in Computer Vision – ECCV 2012, 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI (2012)

This paper deals with the problem of self-calibrating a moving camera with constant parameters. We propose a new set of quartic trivariate polynomial equations in the unknown coordinates of the plane at ... [more ▼]

This paper deals with the problem of self-calibrating a moving camera with constant parameters. We propose a new set of quartic trivariate polynomial equations in the unknown coordinates of the plane at infinity derived under the no-skew assumption. Our new equations allow to further enforce the constancy of the principal point across all images while retrieving the plane at infinity. Six such polynomials, four of which are independent, are obtained for each triplet of images. The proposed equations can be solved along with the so-called modulus constraints and allow to improve the performance of existing methods. [less ▲]

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