image-segmentation

问题 I have a image that includes object and some unwanted region (small dots). I want to remove it. Hence, I use some morphological operator example 'close' to remove. But it is not perfect. Do you have other way to remove more clear? You can download example image at raw image This is my code load Image.mat %load Img value Img= bwmorph(Img,'close'); imshow(Img); 回答1: You might prefer a faster and vectorized approach using bsxfun along with the information obtained from bwlabel itself. Note:

问题 I am running the unet from https://github.com/zhixuhao/unet but when I run the unet the predicted images are all grey. I get an error saying low contrast image for my test data, any one had or resolved this problem? I am training with 50 ultrasound images and get around 2000/3000 after augmentation, on 5 epochs with 300 steps per epoch and batch size of 2. Many thanks in advance Helena 回答1: After you made sure that your data pipeline is correct. There are a few things to consider here, I hope

问题 I need to cut this image into three parts using PIL and pick the middle part. How do I do it? http://thedilbertstore.com/images/periodic_content/dilbert/dt110507dhct.jpg 回答1: If the boxes are not known on before hand I would run a simple edge finding filter over the image (both x and y directions) to find the boundaries of the box. A simple approach would be: Run horizontal edge filter over image. You now have an image where each pixel describes the changes in intensity left and right of that

问题 I am working with openCV on Android platform. With the tremendous help from this community and techies, I am able to successfully detect a sheet out of the image. These are the step I used. 1.Imgproc.cvtColor() 2.Imgproc.Canny() 3.Imgproc.GausianBlur() 4.Imgproc.findContours() 5.Imgproc.approxPolyDP() 6.findLargestRectangle() 7.find the vertices of the rectangle 8.find the vertices of the rectangle top-left anticlockwise order using center of mass approach 9.find the height and width of the

问题 I was wondering how to handle not labeled parts of an image in image segmentation using TensorFlow. For example, my input is an image of height * width * channels. The labels are too of the size height * width, with one label for every pixel. Some parts of the image are annotated, other parts are not. I would wish that those parts have no influence on the gradient computation whatsoever. Furthermore, I am not interested in the network predicting this “void” label. Is there a label or a

问题 I have the image includes circular, elipsoidal, square objects and somethings like these. I want to get only circual objects. I applyed a filter by using Solidity and Enccentricity levels of objets but I could not remove square objects. Square objects which have not sharp corners have nearly same Solidity and Enccentricity level with circular objects. My question is that is there any other parameter or way to detect square objects? 回答1: You can compare the area of the mask to its perimeter

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 4 years ago . I have this image of a battery: I would like to determine the dimensions of the battery (in pixels). The problem I have is that the battery is rotated by an unknown angle. How can I detect the dimensions of this rotated battery? I was thinking of these algorithmic steps: First, I

问题 I have a canny edge image I want to remove all line except the lines that look like a semi-circle/ellipse or a 'C'. Tried Hough Circle transforms, it detects all curves.Don't need that. 回答1: A simple approach would be: Find connected components Find the minimum oriented bounding box Compute the aspect ratio of the box, and check if it's too much elongated . On your image, I marked in red almost straight lines, and in green the curved lines. You can play with the threshold on the aspect ratio:

问题 I have created a net for image segmentation, in particular brain tumors. The jupyter notebook with the code is here. When I train the CNN with minibatch size of 1, I get a fairly good result: But when I change the size to something larger ( 2 or more) the results are terrible: Tensorboard shows the diference in the loss. Clearly the the net with batchsize 2 is not minimizing the loss (blue) Any ideas on why this could be the case? 回答1: I found the problem. I checked my graph with tensorboard

问题 I want to know the possible measures that can be used to measure the accuracy of my image segmentation algorithm I know the following methods: sensitivity specificity Dice index Jaccard index But I am working with large data set that needs to be tested, So I think measuring accuracy with each pixel value will not be very suitable. So, I am looking for other more convenient measures. P.S: I have ground truth data 回答1: The question of "accuracy" in image segmentation is ill-defined, resulting