image-segmentation

I'm using the function regionprops to detect the number of trees on a image taked by drone. First I removed the ground using Blue NDVI: Image with threshold: Then I used the function regionprops to detect the number of trees on image: But there are a problem on region 15, because all trees on that region are connected and it detects as one tree. I tried to separate the trees on that region using Watershed Segmentation , but its not working: Am I doing this the wrong way? Is there a better method to separate the trees? If anyone can help me with this problem I will appreciate. Here is the

my target is to remove any non text area from a scanned business card image but i don't know the steps to perform that using OpenCV , i have followed this steps but don't know this is the right one or not also i don't notice any change in the image (non text areas remains) any idea will be very helpful,thanks. 1) convert the image to grayscale 2) binaries the image 3) invert the colors (cv::bitwise_not) in order to have a white pixels text 4) Erode the image(cv::erode) 5) detect edges using canny 6) detect text lines using hough transform (not yet) code: cv::Mat greyMat = [self.imageView.image

Is semantic segmentation just a Pleonasm or is there a difference between "semantic segmentation" and "segmentation"? Is there a difference to "scene labeling" or "scene parsing"? What is the difference between pixel-level and pixelwise segmentation? (Side-question: When you have this kind of pixel-wise annotation, do you get object detection for free or is there still something to do?) Please give a source for your definitions. Sources which use "semantic segmentation" Jonathan Long, Evan Shelhamer, Trevor Darrell: Fully Convolutional Networks for Semantic Segmentation . CVPR, 2015 and PAMI,

Let us consider an image Y of size 512x512. The code below serves to segment the image Y into blocks where each block take the size 8x8. Matlab Code: for m = 1:64 for n = 1:64 subX = Y(8*(m-1)+1:8*m,8*(n-1)+1:8*n); end end What i need in this question is to resolve my two problems below: 1) to segment the image X into 8 x 8 number of blocks (not the size is 8x8 but the number of blocks must be 8x8). In this case the image will become segmented into 64 blocks where each block being contain 512/64 pixels =8 pixels. 2) it is the same concept of 1), but in this case, i want to segment the image

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 function for this? At the moment I am using tf.nn.sparse_softmax_cross_entropy_with_logits . I'm not 100%

I have to apply dct2 to small windows of my image, preferably by using an overlapping window. I have found out that there are two functions in Matlab that can make this possible blockproc and im2col . I am having trouble understanding either and would appreciate some clarification. blockproc can be used to implement my function on a sliding window using the BorderSize and TrimBorder arguments. B = blockproc(A,[64,64],fun,'BorderSize',[5,5], 'TrimBorder', 'false'); I realize that this creates a block of [64 + 2*5, 64 + 2*5] and applies the function @fun on each block. But since I cannot go into

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 would have to convert this image to a black and white image (thresholding). After that, I would have to find a center point and draw a rectangle in the white pixels. Then, I have to turn the rectangle 360 degrees and locate the position of the rectangle (and so the dimensions). I am somewhat inexperienced, and I would