For both eyes, I want to go to image processing now. According to my current understanding, double visual, one aspect is the camera labeled, one aspect is the matching problem of feature points. Since there is no specific test, there is still a lot of things to improve.
For the calibration of the camera, it is now desirable to use a very mature way to do it directly with the toolbox calibrated in Matlab.
Therefore, the current difficulty is that the image processing is issued, that is, look for feature points to match.
Think about it, now you have to identify things are very simple, the goal is also very clear, if you don't consider the special situation. That is, a color of a color of the red and a yellow, the middle is the blue corner column, and the target is not small. However, the difficulty is that because the image used is the image captured by the USB Camera, it is captured when the movement is moved, and there will be jitter, so that the image will be blurred. Now I want to discover the problem when I do image processing in the first two days, then, I want to solve the method.
There are many ways to split images. Based on color and in shape. I feel now, although the problem doesn't seem very difficult, but if, if you use an image-division method, it is estimated that it is not good.
A few days ago, it has always considered that it is just a color-based segmentation. That is to convert the RGB color space to the HSI color space, then combine the S value, but mainly according to the h value. This is a threshold set in the program because of the total feeling, these still have a color. Such an effect is that there will be some messy points. In fact, it does not belong to the area I want is divided, and there will be some obvious to the point in the area I want, and I have not been split. For those who are split, it is not the target, it can be removed in the form of opening, but, in this way, the goal will also be lost. When you just consider the method of color, it is to get the coordinates of each point in the target, then find the value of the center point. According to this processing effect, the center points will definitely have a lot of deviation. For both eyesight, the matching of the feature point is the most important, so this is definitely not. It is also considered a method of excessive regional growth, but the method of regional growth should take into account the selection, growth conditions of the seed point, and the conditions of termination. The conditions that grow in this should be the most difficult. And the key is that I haven't compiled similar algorithms before, and now I don't have time to consider do this. Also, I have thought about it, in this way, in the end, there will be some problems that have not been split. Overall, the method of using color segmentation is not!
Think now, since it is a high requirement, then, it should be better to use edge detection. Perhaps the edge detection of the color image can be used. This morning, I tried to use ordinary Sobel and other operators to detect because of the movement of the camera, the edges of the camera (here, it is first converted into grayscale map), and finally, the edge is almost not detected, if used It is a Canny operator, then there are many things that have been detected. The Canny operator is really good, but the details are too much, so that the truly thing will be overwhelmed. The above test is used as a grayscale map, so that the characteristics of the angle column and the ball, they all have a surface, smooth, so brightness light on their influence is quite big, and when some light is strong, Then, there will be some RGB values that are biased in the same, so that the result is that one can be sure that the S value is close to 0 when converting into an HSI model, so that the H value will not make too much meaning. (This, let my color-based segmentation is not very good). And another influence, it is now not sure, that is, the smoothness of the contour, allows the edge detection based on the grayscale value, except the Canny operator). Now, I want to combine the color-based segmentation and edge detection, and finally, use the hough transform to detect the straight line and circle (small ball). Because which one is used alone, it seems that it is not very good. Perhaps the edge detection of the color image can also achieve such an effect, but first, I have not found any good color image edge detection methods, I have found this content, but there is no harvest, see The image processing is said to be said to the edge detection, the color image can be done like the grayscale image, that is, the RGB three components are done, so that the effect of HSI is also ok. Secondly, I am still thinking, if the light is detected, in the end, it is necessary to use hough to transform. If the last thing to do hough transformation is large, then, first, there will be many unwanted things, still It will increase the amount of calculation. So, want to use, color-based segmentation first segmentation area, then, then edge detection and hough transform in this area. In this way, the color based on color and shape-based transformations are combined.
Now the edge detection encounters trouble. I want to try the algorithm in Matlab, as long as it is the algorithm effect, the program, I think it should be too difficult. Then, in the evening or tomorrow, we must write the last program. Tomorrow is the week 4.
The key is algorithm, afraid of not finding a good algorithm. Image processing is so important to the entire binding vision. If the image processing is not good, then the entire system is definitely not.
Must, take a step, it is guaranteed that there is no too much error in one step.
