Search Results for author:
Found 12 papers, 0 papers with code
Time-Varying Graph Mode Decomposition
The graph modes can be interpreted in terms of their temporal, spectral and topological characteristics.
Data-driven Signal Decomposition Approaches: A Comparative Analysis
Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation.
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis
We propose a novel multivariate signal denoising method that performs long-range correlation analysis of multiple modes in input data by considering inherent inter-channel dependencies of the data.
A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation
We develop a data-driven approach for signal denoising that utilizes variational mode decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic.
Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics
We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate signal denoising framework.
FPGA based design for online computation of Multivariate EMD (MEMD)
MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets.
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.
A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors
Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed.
Automatic Selection of the Optimal Local Feature Detector
The efficiency and the good accuracy in determining the optimal feature detector for any operating condition, make the proposed tool suitable to be utilized in real visual applications.
Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems
Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44. 44%) in the memory requirements.
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.
Assessing The Performance Bounds Of Local Feature Detectors: Taking Inspiration From Electronics Design Practices
Since local feature detection has been one of the most active research areas in computer vision, a large number of detectors have been proposed.
