Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Multi-Frame Structure-from-Motion Algorithm under Perspective Projection
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Global optimal image reconstruction from blurred noisy data by a Bayesian approach
Journal of Optimization Theory and Applications
Super-Resolution from Image Sequences - A Review
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Superresolution restoration of an image sequence: adaptive filtering approach
IEEE Transactions on Image Processing
Simultaneous motion estimation and filtering of image sequences
IEEE Transactions on Image Processing
Modeling for edge detection problems in blurred noisy images
IEEE Transactions on Image Processing
A genetic algorithm for the identification and segmentation of known motion-blurred objects
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
Nonrigid motion estimation from a sequence of degraded images
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.09 |
In this paper, we consider the problem of filtering sequences of images taken from moving objects, with the aim of recovering information about the object itself as well as its underlying motion. We first provide a formal description of the admissible classes of images and (possibly nonrigid) motions, and of the functional relationship between the original image and the observed one (blurring and noisy effects). We then focus on the problem of edge detection, assuming full information about the motion. We propose a procedure that includes a preliminary preprocessing of the measured image, aimed to localize the detection problem and to improve the signal-to-noise ratio. Then, the edge identification is accomplished by an algorithm which implements recursive linear quadratic estimation and hypothesis testing. Finally, the procedure is tested against simulated and real data.