matlab-cvst

I would like to correct lens distortions on a series of images. All the images were captured with the camera fixed in place, and a checkerboard image from the same set up is also available. After detecting the corners of the distorted checkerboard image, I would like to compute the radial distortion coefficients so that I can correct the images. Similar to the estimateCameraParameters function. Ideally, I would like to use a method similar to Matlab camera calibration however this does not seem to work for cases where only a single calibration image is available (and the images were all

I currently have 2 seperate data sets that belong to 2 different skin diseases. I have drawn an abstract image differentiating the 2 diseases on MS Paint. Disease 1 tends to be rounder in shape than Disease 2 and there is a texture difference as well. Using texture filters and segmentation functions on Matlab, I am able to locate the disease region (and draw a border around it), for both Disease 1 and 2 . My question is how can I differentiate between the 2 diseases? Are there functions I can use or am I better off using some form of machine learning on the data sets. Any advice at all is

I calibrated a camera with checkerboard pattern using OpenCV and MATLAB. I got .489 and . 187 for Mean Re-projection errors in OpenCV and MATLAB respectively. From the looks of it, MATLAB is more precise. But my adviser feels both MATLAB and OpenCV use the same BOUGET's algorithm and should report same error (or close). Is it so ? Can someone explain the difference b/w MATLAB and OpenCV camera calibration methods ? Thanks! Your adviser is correct in that both MATLAB and OpenCV use essentially the same calibration algorithm. However, MATLAB uses the Levenberg-Marquardt non-linear least squares

Hello I have a depth image, I want to extract the person(human) silhouette from that. I used pixel thresholding like this: for i=1:240 for j=1:320 if b(i,j)>2400 || b(i,j)<1900 c(i,j)=5000; else c(i,j)=b(i,j); end end end but there is some part left. Is there any way to remove that? Original_image: Extracted_silhouette: Shai According to this thread depth map boundaries can be found based on the direction of estimated surface normals. To estimate the direction of the surface normals, you can [dzx dzy] = gradient( depth_map ); %// horizontal and vertical derivatives of depth map n = cat( 3, dzx

I've been trying to implement a neural network in Matlab that is capable of recognizing images based on their features. I am attempting to use the Bag of features/words approach to obtain a discrete vector of features that I can then feed into my neural network. I have been using this example as a guide - http://in.mathworks.com/help/vision/examples/image-category-classification-using-bag-of-features.html One line in the code (featureVector = encode(bag, img);) counts the word occurrences in an image. Could I use this "featurevector" matrix to train my neural network? And would I have to

stereoParameters takes two extrinsic parameters: RotationOfCamera2 and TranslationOfCamera2 . The problem is that the documentation is a not very detailed about what RotationOfCamera2 really means, it only says: Rotation of camera 2 relative to camera 1, specified as a 3-by-3 matrix. What is the coordinate system in this case ? A rotation matrix can be specified in any coordinate system. What does it exactly mean "the coordinate system of Camera 1" ? What are its x,y,z axes ? In other words, if I calculate the Essential Matrix, how can I get the corresponding RotationOfCamera2 and