In this section, we discuss some properties of the proposed approach. The way we estimate the error is as follows:
Only the case that synthesizes two texture patterns in Brodatz texture is considered. In the algorithm, every boundary is independently. It is hard to judge the accuracy if we consider multi-boundaries simultaneously. Especially when some boundaries are detected and the others are not.
The distance between the answer and the result detected by the algorithm is measured in the condition of boundary which is detectable. We define the error by dividing measured distance into the number of total pixels.
Fig. 5-5 is a histogram of error estimation in our experiment, and the results with error less than 5% is account for 85% for test images. The average of the error is less than 5%. The smallest error is 0.76%. Note that the images with bigger estimated errors are reasonable. In these examples, the boundaries between different textures (middle line) exist, but they are weaker than local boundaries caused by non-uniform regions. For the sake of simplicity, only the largest peaks are kept during error estimation, so the boundaries in the middle are not kept in the results. Although in these examples, the outputs are consistent to human visual perception. Their errors are quite big. We have found that it is hard to define a generally
“correct answer” for all test images in human vision system, and the method we measure the error is probably not suited for those kind of test images. For this reason, the measurement is not necessary for the input images synthesized by the rest 42 textures in Brodatz textures.
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0
10 20 30 40 50 60
statistics of test images(synthesize two textures randomly)
error
number of test images
Fig. 5-5: Histogram of error estimation
In summary, a simple framework for hybrid-order boundary detection is proposed. It mimics the mechanism of the early stage of the human vision, and experimental results are generally consistent to the human visual sensation. After post processing, the detected boundaries also have adequate accuracy for the other image processing applications such as stereo, and pattern recognition. By implementing the proposed algorithm on the Cellular Neural Networks (CNN), the computational time will greatly decrease. The real-time processing capability is critical in some applications, such as the object tracking. As same as the other algorithms for textures analysis which is also based on the Gabor filters, there are too many parameters need to be determined. Determining the parameters will much more complex when the synthesized texture patterns increased. For the sake of keeping the structure simple and combining the hybrid-order features easily without the clustering methods, we use same resolution for all of the Gabor filters in the approach. In this approach,
we only consider the first and the second order features. According to some research results, there are still some higher-order features that can be utilized. For example, color is one of them. We do believe proposed approach can be extended to color textures by integrating color information.
6 Chapter 6 Conclusion
This study investigates behavioral and EEG responses under multiple cases and multiple distraction levels. Firstly, the response time for mathematical problem solving in dual-task condition is significantly higher than that in single-task condition. Therefore, distraction effects occur while processing two tasks during driving. Comparing to the mathematical problems, however, the response time for driving tasks under multiple cases is almost the same without differences. This is due to the order of task appearance and the relative difficulty of the two tasks, which suggesting these factors are important considerations in dual-task performance. Secondly, theta power increases in the frontal area are higher with higher response time. The phasic changes around the theta band in the case, in which the mathematic task is presented before the deviation task, show the strongest increase as the same as that in the simultaneous-task case. This is because subjects already process a task in the brain and need more brain resources to manage the second task presented after the first task. In conclusions, this study suggests that the power increases of the 4~7.8 Hz frequency band in the frontal area is related to driver distraction and represents the strength of distraction in real-life driving.
For the future work, we will still work hard on EEG research for keeping safety during driving. Can we detect the mistake happened by watching the EEG power before it happens?
How long can we detect it before it happens?
Based on the good properties of CNN, on the other hand, we are working on implementing the EEG analysis on CNN which was considered for a multidimensional signal analysis. In the future, I will like to implement EEG hardware by CNN features and architecture.
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