In this paper, a classification method for classifying subjects’ ability to follow some predefined trajectories is proposed
using phase only filter (POF) . In this research, we use three predefined trajectory patterns of different difficulty levels,
and a set of data comprising four different classes of movements. We propose a POF to classify the data in those classes.
POF can be assumed as a Complex Match Filter (CMF) where the amplitude of the object function is set to unity. The POF
searches the entire image to find a match to the input filter. The trajectory in all three patterns contains edges and sharp
turns which could considerably help to distinguish between the classes. Therefore, in this method, the reference pattern is
segmented to several parts and critical segments of the trajectory used as an input filter or the pattern to search for. The
classification task is applied for each pattern separately and the results obtained are fused based on different weights. The
optimum weights for the fusion are obtained by using the training data and the linear regression technique.
A feature extraction-based classification method is proposed in this paper for verifying the capability of human’s neck in target tracking. Here, the target moves in predefined trajectory patterns in three difficulty levels. Dataset used for each pattern is obtained from two groups of people, one with whiplash associated disorder (WAD) and asymptomatic group, who behave in both sincere and feign manner. The aim is to verify the WAD group from asymptomatic one and also to discriminate the sincere behavior from the feigned one. Sparse and low-rank feature extraction is proposed to extract the most informative feature from training samples and then each sample is classified into the group which has the highest correlation coefficient with. The classification results are improved by fusing the results of the three patterns.
In this paper a new hyperspectral image based on wavelets and sparse regularization is proposed. This new method is called Wavelet Based Sparse Restoration (WBSR). The hyperspectral signal is restored by utilizing penalized least squares and the `1 penalty. Iterative Soft Thresholding (IST) algorithm is used to solve the convex optimization problem. It is shown that not only WBSR improves the denioising results both visually and based on Signal to Noise Ratio (SNR) but also increases the classification accuracies.
In this paper a penalized least squares cost function with a new spatial-spectral penalty is proposed for hyper-
spectral image restoration. The new penalty is a combination of a Group LASSO (GLASSO) and First Order
Roughness Penalty (FORP) in the wavelet domain. The restoration criterion is solved using the Alternative
Direction Method of Multipliers (ADMM). The results are compared with other restoration methods where the
proposed method outperforms them for the simulated noisy data set based on Signal to Noise Ratio (SNR) and
visually outperforms them on a real degraded data set.