| 1. |
- Bengtsson, Tomas, 1983, et al.
(author)
-
Regularized Optimization for Joint Super-Resolution and High Dynamoc Range Image Reconstruction in a Percetually Uniform Domain.
- 2012
-
In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781467300469 ; , s. 1097-1100
-
Conference paper (peer-reviewed)abstract
- This paper discusses resolution enhancement of a set of images with varying exposure durations, having a high combined dynamic range. So far, little has been said in relation to the Human Visual System when it comes to Super-Resolution and High Dynamic Range fusion, unlike the case for traditional Super-Resolution where errors are measured with respect to human perception in the pixel domain. We propose a Super-Resolution method in the L*a*b* domain to bridge the gap and present some image reconstruction results.
|
|
| 2. |
- Feng, S, et al.
(author)
-
3D Face Recognition Using Affine Integral Invariants
- 2006
-
In: IEEE international conf. on ASSP (ICASSP-06). ; II, s. 189-192
-
Conference paper (peer-reviewed)abstract
- A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a smallsubset of those affine integral invariant curves. A recognition procedure based on the Discriminant Analysis and Jensen-Shannon Divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%.
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Wang, Tiesheng, 1975, et al.
(author)
-
Tracking moving objects in video using enhanced mean shift and region-based motion field
- 2007
-
In: proc. of European Signal Processing Conference ( EUSIPCO '07), Sept. 2007, Poland.
-
Conference paper (peer-reviewed)abstract
- In this paper, we propose a scheme for moving object tracking from videos by combining mean shift and motion field statistics. For mean shift, we employ an enhanced spatial-range mean shift that enables a reduced number of oversegmentation. For motion statistics, we combine the optical flow and high-order moment to generate motion regions that are associated with moving objects (or object parts). Experiments have been conducted on several indoor and outdoor (color/gray-scale) image sequences ranging from simple to median complexity. To evaluate the performance, three objective criteria are applied in addition to the visual inspection. The results show that the proposed method is promising for moving object tracking in video, with an averaging detection rate of 95%. Further, the proposed scheme is compared with that using the conventional mean shift for the tracking, indicating a significantly reduction in false alarm (≈ 30%).
|
|
