matlab-cvst

问题 I am working on the following code: filename = 'C:\li_walk.avi'; hVidReader = vision.VideoFileReader(filename, 'ImageColorSpace', 'RGB','VideoOutputDataType', 'single'); hOpticalFlow = vision.OpticalFlow('OutputValue', 'Horizontal and vertical components in complex form', 'ReferenceFrameDelay', 3); hMean1 = vision.Mean; hMean2 = vision.Mean('RunningMean', true); hMedianFilt = vision.MedianFilter; hclose = vision.MorphologicalClose('Neighborhood', strel('line',5,45)); hblob = vision

问题 I have following code that was given to me, but I am not sure at all as to what the logic here is. The idea, I believe, is that this will histogram/quantize my data. Here is the code: The input: x = 180.*rand(1,1000); %1000 points from 0 to 180 degrees. binWidth = 20; %I want the binWidth to be 20 degrees. The main function: % ------------------------------------------------------------------------- % Compute the closest bin center x1 that is less than or equal to x % ------------------------

问题 I need a pre-built (i.e. already trained) OCR library that recognizes easy characters (standard fonts like Arial, Times New Roman, Courier, etc). Does Matlab have anything like that in one of its toolboxes? Or do I have to use an external program like Tesseract (and interface using system calls)? 回答1: I'm not familiar with an official MATLAB OCR toolbox. However, you can find all sorts of gems in the MATLAB File Exchange, like this OCR tool. It's pretty neat. 回答2: In MATLAB R2014a there's a

问题 When I run go_calib_optim on the calibration data I get: Error using - Matrix dimensions must agree. Error in compute_extrinsic_init (line 66) Y = X_kk - (X_mean*ones(1,Np)); Error in comp_ext_calib (line 22) [omckk,Tckk] = compute_extrinsic_init(x_kk,X_kk,fc,cc,kc,alpha_c); Error in go_calib_optim_iter (line 293) comp_ext_calib; Error in go_calib_optim (line 56) go_calib_optim_iter; Error in calibration_script (line 176) go_calib_optim; What could be causing this? Alternatively, is this

问题 I'm working on a PIV-Workflow and I'm currently pre-processing the images. I need to get rid of the perspective distortion in the images. I do have the "image processing toolbox" and the "camera calibrator". I already got rid of the lens distortion with "undistortImage();" and the cameraParams object, which is inferred through a chessboard pattern. First Question: Is it possible to use the cameraParams object to distort the image perspectively, so that my chessboard is rectified in the image?

问题 I am trying to extract HOG features from an image in MATLAB R2013a. This is the code I copied from MATLAB documentation website: input='1.png'; Z=imread(input); %Z=rgb2gray(Z); [featureVector, hogVisualization] = extractHOGFeatures(Z); The error I get is: Undefined function 'extractHOGFeatures' for input arguments of type 'uint8'. Error in hogfeatureextractor (line 6) [featureVector, hogVisualization] = extractHOGFeatures(Z); I think this is due to the computer vision toolbox not installed

问题 I am trying to use a KLT tracker for human tracking in a CCTV footage. The people are very close to the CCTV. I noticed that some time people change the orientation of the heads and also the frame rate is slightly slow. I have read from Rodrigues et al. paper Section 3.4 that the: "This simple procedure (KLT tracking procedure) is extremely robust and can establish matches between head detections where the head HAS NOT BEEN continuously detected continuously detected due to pose variation or

问题 I was following a tutorial in matlab documentation at http://www.mathworks.com/help/vision/examples/automatically-detect-and-recognize-text-in-natural-images.html There they use a function helperGrowEdges but I it is not being recognized right now. I verified that I have Computer Vision System Toolbox. I am unable to figure how to implement it. It says it 'Grow the edges outward by using image gradients around edge locations', but I am unable to figure out how to implement is well. 回答1: As

问题 I'm confused with matlab "extractHOGFeature" function. I have an depth image (370x89). I want to calculate HOG on single point in an image. When I give the point[i,j] sometime it returns empty vector(0x36) and when I swap the points[i,j] to [j,i] the function return a non empty vector(1x36). Additionally I ran the function in the loop for every pixel[i,j],[j,i] and whole image separately and count the non-empty HOG vector and surprisingly I got different numbers. Let me explain in the example

问题 I have to reconstruct an object which will be placed around 1 meter to 1.5 meters away from the baseline of my stereo setup. The image captured by both cameras have high resolution (10 MP) The accuracy with which I have to detect it's position is +/- 0.5mm, in all the three co-ordinate axes. (If you require more details, please let me know) For these, what should the optimal specifications of my checkerboard (for calibration) be? I only know that it should be an asymmetric board. It should be