image-segmentation

问题 I am trying to apply a bounding box for the discontiguous region in the sample below. I found something in Matlab help docs on regionprops but it did not explain anything about how to do it. I need the smallest Box that can contain all the blobs in the image. 回答1: By default, when introduced with a logical type input mask, regionprops automatically apply bwlabel to the mask and computes properties for each connected component of the input mask. In your case, this is not a desirable behavior,

问题 I am reading multiple conflicting Stackoverflow posts and I'm really confused on what the reality is. My question is the following. If I trained an FCN on 128x128x3 images, is it possible to feed an image of size 256x256x3 , or B) 128x128 , or C) neither since the inputs have to be the same during training and testing? Consider SO post #1. In this post, it suggests that the images have to be the same dimensions during input and output. This makes sense to me. SO post #2: In this post, it

问题 I'm doing an image segmentation with UNet-like CNN architecture by Pytorch 0.4.0.It mark foreground as 1 and background as 0 in the final segmentation result.I use a pre-trained VGG's feature extractor as my encoder, so I need to upsampling the encoder output many times.But the result shows a weird lattice parttern in the result like this: I suspect these different shape of black parttern were caused by the deconvolutional layers.It's said that deconv layer add (s-1) zeros between the input

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 5 months ago . I have many images consisting of circles(varying from 1 to 4) in each image. I am trying to get a clear circle images by filling the missed pixels along the circle path. I have tried Hough-transform, but its parameters are image specific: for each image I have to change the

问题 i have to extract each characters from a image here i am uploading the code it is segmenting the horizontal lines but not able to segment the each characters along with the horizontal line segmentation loop. some1 please help to correct the code this is the previous code: %%horizontal histogram H = sum(rotatedImage, 2); darkPixels = H < 100; % Threshold % label [labeledRegions, numberOfRegions] = bwlabel(darkPixels); fprintf('Number of regions = %d\n', numberOfRegions); % Find centroids

问题 I have an image with 6 colors each indicating a value. I had obtained an image as shown below. I need to smoothen the edges and then find out the area as well as the surface area of that region. The second image shows a black line drawn in the edges which indicates that I need to smoothen the edges in such a way. I had used segmentation to create a mask as shown in the third image, and then obtain a segmented image using the code following the image. I have used the following code for

问题 My code is the same as this tutorial. When I see the result image after using cv::watershed() , there is a object(upper-right) that I want to find out, but it's missing. There are indeed six marks in image after using cv::drawContours() . Is this normal because the inaccuracy of the watershed algorithm exist? Here is part of my code: Mat src = imread("result01.png"); Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat thresh; threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU); //

问题 The implementation of this functionality seems pretty straightforward in Python, as shown here: http://docs.opencv.org/trunk/doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.html Yet, when I tried to do exactly the same in C++, I get bad arguments error (for the grabcut function). How to put the mask image in the right format? I am a newbie at this, so I'd be very thankful if someone could help me understand better. Thank you! Here's what I have so far: Mat image; image= imread(file); Mat

问题 public class Grabcut extends Activity { ImageView iv; Bitmap bitmap; Canvas canvas; Scalar color = new Scalar(255, 0, 0, 255); Point tl, br; int counter; Bitmap bitmapResult, bitmapBackground; Mat dst = new Mat(); final String pathToImage = Environment.getExternalStorageDirectory()+"/gcut.png"; public static final String TAG = "Grabcut demo"; static { if (!OpenCVLoader.initDebug()) { // Handle initialization error } } @Override public void onCreate(Bundle savedInstanceState) { super.onCreate

问题 I'm working in image segmentation, testing a lot of different segmentation algorithms, in order to do a comparitive study. At the moment i've implemented the mean shift algorithm. I would like to count the objects segmented in the image. In this images there are two types of objects, with different colors. I have a the manually counting done by specialists so i would like to compare the results. The result image is: There is any way for me to automize this process? Can you please help me out!