Electroencephalography (EEG) is the signal generated by electrical activity in the human brain. EEG topographic maps (topo-maps) give an idea of brain activation. Functional connectivity helps to find functionally integrated relationship between spatially separated brain regions. Brain connectivity can be measured by several methods. The classical methods calculate the coherence and correlation of the signal. We have developed an algorithm to map functional neural connectivity in the brain by using a full search block matching motion estimation algorithm. We have used oddball paradigm to examine the flow of activation across brain lobes for a specific activity. In the first step, the EEG signal is converted into topo-maps. The flow of activation between consecutive frames is tracked using full search block motion estimation, which appears in the form of motion vectors. In the second step, vector median filtering is used to obtain a smooth motion field by removing the unwanted noise. For each topo-map, several activation paths are tracked across various brain lobes. We have also developed correlation activity maps by following the correlation coefficient paths between electrodes. These paths are selected when the correlation coefficient between electrodes is >70%. We have compared the motion estimation path with the correlation coefficient activation maps. The tracked paths obtained by using motion estimation and correlation give very similar results. The inter-subject comparison shows that four out of five subjects tracked path involves all four (occipital, temporal, parietal, frontal) brain lobes for the same stimuli. The intra-subject analysis shows that three out of five subjects show different tracked lobes for different stimuli.
KEYWORDS: Digital filtering, 3D image processing, Fermium, Frequency modulation, Distortion, Cameras, Control systems, Statistical analysis, Device simulation, CCD cameras
The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different
focus settings is called shape from focus (SFF). Conventional SFF methods sum up the focus values within a small
window of each pixel in the image. It produces a surface distortion effect, and an inaccurate depth map is obtained. In
this paper, a fast and accurate SFF method based on averaging filter is proposed. We suggest that instead of averaging
focus values, averaging depth values produces more accurate depth map. The experimental results demonstrate the
effectiveness and the efficiency of the proposed method in comparison to the conventional methods.
We introduce a new approach for 3-D shape recovery based on discrete wavelet transform (DWT) and principal component analysis (PCA). A small 3-D neighborhood is considered to incorporate the effect of pixels from previous as well as next frames. The intensity values of the pixels in the neighborhood are then arranged into a vector. DWT is applied on each vector to decompose it into approximation and wavelet coefficients. PCA is then applied on modified energies of wavelet components. The first feature in the eigenspace, as it contains maximum variation, is employed to compute the depth. The performance of the proposed approach is tested and is compared with existing methods by using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Resolution, accuracy, root mean square error (RMSE), and correlation metrics have been applied to evaluate the performance. Experimental results and comparative analysis demonstrate the effectiveness of the proposed method.
The objective of 3D shape recovery using focus is to estimate depth map of the scene or object based on best focus points
from camera lens. In Shape From Focus (SFF), the measure of
focus - sharpness - is the crucial part for final 3D shape
estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of
the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for
curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a
new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that
have the same initial depth as of the center pixel and then it
re-adjusts the center value accordingly. Experimental results
show that the proposed technique generates better shape and takes less computation time in comparison to previous SFF
methods based on Focused Image Surface (FIS) and dynamic programming.
This paper introduces a new approach for 3D shape recovery based on Discrete Wavelet Transform (DWT) and Principal
Component Analysis (PCA). Contrary to computing focus quality locally by summing all values in a 2D or 3D window
obtained after applying a focus measure, a vector consisting of seven neighboring pixels is populated for each pixel in
the image volume. Each vector in the sequence is decomposed by using DWT and then PCA is applied on the energies of
detailed coefficients to transform the data into eigenspace. The first feature, as it contains maximum variation, is
employed to compute the depth. Though DWT and PCA are both computationally expensive transformations, the
reduced data elements and algorithm iterations have made the proposed method efficient. The new approach was
experimented and its performance was compared with other methods by using synthetic and real image sequences. The
evaluation is gauged on the basis of unimodality, monotonicity and resolution of the focus curve. Two other global
statistical metrics Root Mean Square Error (RMSE) and correlation have also been applied for synthetic image sequence.
Experimental results demonstrate the effectiveness and the robustness of the new method.
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