| 1. |
- Afzal, H., et al.
(författare)
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RGB-D multi-view system calibration for full 3D scene reconstruction
- 2014
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Ingår i: 2014 22nd International Conference on Pattern Recognition. - : IEEE conference proceedings. ; , s. 2459-2464
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Konferensbidrag (refereegranskat)abstract
- One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We propose BAICP+ which combines Bundle Adjustment (BA) and Iterative Closest Point (ICP) algorithms to take into account both 2D visual and 3D shape information in one minimization formulation to estimate relative pose parameters of each camera. BAICP+ is generic enough to take different types of visual features into account and can be easily adapted to varying quality of 2D and 3D data. We perform experiments on real and simulated data. Results show that with the right weighting factor BAICP+ has an optimal performance when compared to BA and ICP used independently or sequentially.
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| 2. |
- Al Ismaeil, K., et al.
(författare)
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Depth super-resolution by enhanced shift and add
- 2013
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Ingår i: Computer Analysis of Images and Patterns. - Berlin, Heidelberg : Springer. - 9783642402456 ; , s. 100-107
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Konferensbidrag (refereegranskat)abstract
- We use multi-frame super-resolution, specifically, Shift & Add, to increase the resolution of depth data. In order to be able to deploy such a framework in practice, without requiring a very high number of observed low resolution frames, we improve the initial estimation of the high resolution frame. To that end, we propose a new data model that leads to a median estimation from densely upsampled low resolution frames. We show that this new formulation solves the problem of undefined pixels and further allows to improve the performance of pyramidal motion estimation in the context of super-resolution without additional computational cost. As a consequence, it increases the motion diversity within a small number of observed frames, making the enhancement of depth data more practical. Quantitative experiments run on the Middlebury dataset show that our method outperforms state-of-the-art techniques in terms of accuracy and robustness to the number of frames and to the noise level.
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| 3. |
- Al Ismaeil, K., et al.
(författare)
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Dynamic super resolution of depth sequences with non-rigid motions
- 2013
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Ingår i: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. - : IEEE Signal Processing Society. - 9781479923410 ; , s. 660-664
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Konferensbidrag (refereegranskat)abstract
- 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. The proposed approach is based on a new data model that leads to a robust registration of all depth frames after a dense upsampling. The textureless nature of depth images allows to robustly handle sequences with multiple moving objects as confirmed by our experiments.
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| 4. |
- Al Ismaeil, K., et al.
(författare)
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Multi-frame super-resolution by enhanced shift & add
- 2013
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Ingår i: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA). - : IEEE. - 9789531841948 ; , s. 171-176
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Aouada, D., et al.
(författare)
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Surface UP-SR for an improved face recognition using low resolution depth cameras
- 2014
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Ingår i: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). - : IEEE conference proceedings. ; , s. 107-112
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Konferensbidrag (refereegranskat)abstract
- 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.1.
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| 6. |
- Bahnsen, A. C., et al.
(författare)
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Example-dependent cost-sensitive logistic regression for credit scoring
- 2014
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Ingår i: 2014 13th International Conference on Machine Learning and Applications. - : IEEE conference proceedings. ; , s. 263-269
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Konferensbidrag (refereegranskat)abstract
- Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do not take into account the real financial costs associated with the lending business. In this paper, we propose a new example-dependent cost matrix for credit scoring. Furthermore, we propose an algorithm that introduces the example-dependent costs into a logistic regression. Using two publicly available datasets, we compare our proposed method against state-of-the-art example-dependent cost-sensitive algorithms. The results highlight the importance of using real financial costs. Moreover, by using the proposed cost-sensitive logistic regression, significant improvements are made in the sense of higher savings.
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