A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
An intensity similarity measure in low-light conditions
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Difference-Based Image Noise Modeling Using Skellam Distribution
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
In this paper, we propose a novel derivative measure based on the probability of intensity difference that is defined by observed intensities and their true intensities. Because the true intensity cannot be estimated accurately only using two observed intensities, the probability is marginalized to consider an entire set of possible true values. The proposed measure not only considers intensity dependent noise effectively using a distribution of intensity difference, but also computes the relevant difference of two corresponding pixels that have different true intensities by extending the same intensity assumption in previous works. Using the proposed measure, the estimation result of image derivative for synthetic noisy signals is closer to the ground truth than most of previous measures. We apply the proposed measure for block matching and corner detection that compute intensity similarity in the temporal domain and image derivative in the spatial domain, respectively.