image-segmentation

问题 I've been trying to clear images for OCR: (the lines) I need to remove these lines to sometimes further process the image and I'm getting pretty close but a lot of the time the threshold takes away too much from the text: copy = img.copy() blur = cv2.GaussianBlur(copy, (9,9), 0) thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9)) dilate = cv2.dilate(thresh, kernel, iterations=2) cnts =

问题 I've been trying to clear images for OCR: (the lines) I need to remove these lines to sometimes further process the image and I'm getting pretty close but a lot of the time the threshold takes away too much from the text: copy = img.copy() blur = cv2.GaussianBlur(copy, (9,9), 0) thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9)) dilate = cv2.dilate(thresh, kernel, iterations=2) cnts =

问题 I've been trying to clear images for OCR: (the lines) I need to remove these lines to sometimes further process the image and I'm getting pretty close but a lot of the time the threshold takes away too much from the text: copy = img.copy() blur = cv2.GaussianBlur(copy, (9,9), 0) thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9)) dilate = cv2.dilate(thresh, kernel, iterations=2) cnts =

问题 I want to identify the branching points of the lightning in this image: http://i.stack.imgur.com/PXujf.jpg What I did first was threshold the image such that I get the lighning part of the image and discard the backround. This is the result http://i.stack.imgur.com/IYNTi.jpg I used the threshold function in openCV and the resulting image is pretty much bad as the quality is lost, the branches are no longer visible. Ok, basically I have 2 problems: How can i properly segment the image such

问题 I want to identify the branching points of the lightning in this image: http://i.stack.imgur.com/PXujf.jpg What I did first was threshold the image such that I get the lighning part of the image and discard the backround. This is the result http://i.stack.imgur.com/IYNTi.jpg I used the threshold function in openCV and the resulting image is pretty much bad as the quality is lost, the branches are no longer visible. Ok, basically I have 2 problems: How can i properly segment the image such

问题 I'm trying to make a network that identifies missing atoms from an image, and is then able to count them. So far, I have created a CNN that is able to output an image like below that solely highlights such atoms 1, and I have found an OpenCV library tat I think would be able to count all the individual white spots (https://www.geeksforgeeks.org/white-and-black-dot-detection-using-opencv-python/). However I would like to be able to count the missing atoms for a given structre: eg I've circled

问题 I am using bounding box marking tools like BBox and YOLO marker for object localisation. I wanted to know is there any equivalent marking tools available for image segmentation tasks. How people in academia and research are preparing data sets for these image segmentation tasks. Recent Kaggle competition severstal-steel-defect-detection has pixel level segmentation information. Which tool they used to prepare this data? 回答1: Generally speaking it is a pretty complex but a common task, so you

问题 I'm trying to train a Unet model in Tensorflow 2.0 which takes as input an image and a segmentation mask, but I'm getting a ValueError : as_list() is not defined on an unknown TensorShape . The stack trace shows the problem occurs during _get_input_from_iterator(inputs) : /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in _prepare_feed_values(model, inputs, mode) 110 for inputs will always be wrapped in lists. 111 """ --> 112 inputs, targets,

问题 I have successfully trained a u-net for the specific task of cell segmentation using (256, 256, 1) grayscale input and (256, 256, 1) binary label. I used zhixuhao's unet implemention in Keras (git rep. here).What I am trying to do now is to train the same model using multiple grayscale layer as input. To make things easier, let's assume I want to use 2 grayscale image im1 and im2 , each of size (256, 256, 1) . Label Y is the same for im1 and im2 . I want to feed the model an input of size

问题 I have done a OCR application for handwritten normal characters .For the segmentation of characters I have used histogram profile method . That successfully works for normal English characters. I have used horizontal projection for line segmentation and vertical projection for character segmentation. To segment lines of cursive hand written article I can use horizontal projection as previous. But I can't use same methodology for cursive English character segmentation since they are merged