image-segmentation

I'm using opencv in Java to try to detect circles (iris, and pupil) in images with eyes, but I didn't get the expected results. Here is my code // convert source image to gray org.opencv.imgproc.Imgproc.cvtColor(mRgba, imgCny, Imgproc.COLOR_BGR2GRAY); //fliter org.opencv.imgproc.Imgproc.blur(imgCny, imgCny, new Size(3, 3)); //apply canny org.opencv.imgproc.Imgproc.Canny(imgCny, imgCny, 10, 30); //apply Hough circle Mat circles = new Mat(); Point pt; org.opencv.imgproc.Imgproc.HoughCircles(imgCny, circles, Imgproc.CV_HOUGH_GRADIENT, imgCny.rows() / 4, 2, 200, 100, 0, 0); //draw the found

I've never used OpenCV before, but I'm trying to write my neural network system to recognize text and I need some tool for text extraction/ segmentation. How can I use java OpenCV to preprocess and segmentate an image containing text. I don't need to recognize the text, I just need to get each letter in a separate image. Something like this : Try this code .No need of OpenCV import java.awt.image.BufferedImage; import java.util.ArrayList; import java.util.List; import org.neuroph.imgrec.ImageUtilities; public class CharExtractor { private int cropTopY = 0;//up locked coordinate private int

I'm using K-means clustering in color-based image segmentation. I have a 2D image which has 3 colors, black, white, and green. Here is the image, I want K-means to produce 3 clusters, one represents the green color region, the second one represents the white region, and the last one represents the black region. Here is the code I used, %Clustering color regions in an image. %Step 1: read the image using imread, and show it using imshow. img = (imread('img.jpg')); figure, imshow(img), title('X axis rock cut'); %figure is for creating a figure window. text(size(img,2),size(img,1)+15,...

I am trying to implement some segmentation tool for my images, and I am trying to use conditional random field (CRF) based method. For example, in this paper . The standard CRF energy function includes two parts, i.e., a unary potential and a pairwise potential where L are the class labels and X are the observations (image pixels). I have got some training image with labels of the objects in the image. For example, I have got the ground truth segmentation of the objects in the image with labels. If I want to use texture of these objects as the feature, I am wondering how to implement and do

I'm making a GUI in Matlab that scrolls through and displays ~600 medical images. I have an axes on which the images are displayed, and a scrollbar that presently goes through images one at a time when the end arrows are pressed. I'm trying to figure out how to incorporate the WindowScrollWheelFcn so I can use the scroll on the mouse to go through the images faster. This is my code: function ct_slider_Callback(hObject, eventdata, handles) set(gcf, 'WindowScrollWheelFcn', @wheel); set(gcf, 'CurrentAxes', handles.ct_image_axes); handles.currentSlice = round(get(handles.ct_slider, 'Value'));

I've heard that if you need to do a color segmentation on your software (create a binary image from a colored image by setting pixels to 1 if they meet certain threshold rules like R<100, G>100, 10< B < 123) it is better to first convert your image to HSV. Is this really true? And why? The big reason is that it separates color information (chroma) from intensity or lighting (luma). Because value is separated, you can construct a histogram or thresholding rules using only saturation and hue. This in theory will work regardless of lighting changes in the value channel. In practice it is just a

I found this dog and cat image dataset: The Oxford-IIIT Pet Dataset . Each image has a pixel level foreground-background segmentation (trimap) image. Searching the internet, I saw that trimap is an image with three colors (one for the background, one for the foreground and one for the not-classified region), but here the image is all black. Is it a mistake or is it correct? But above all I want to know if and how you can use it to get, given a normal image, a new image with the cat or dog on a black background. Thanks. The trimaps look black because they only contain pixels values ranging from