image-segmentation

I've read the documentation for calcHist() many times, but I think my inexperience with OpenCV and rusty programming skills are completely precluding me from understanding it. I'm looking to count pixels in one channel of an HSV image (Hue, or channel[0]) for segmentation purposes using 10 bins that closely approximate color according to something like (let's use this as an example, I stole the ranges off the web - fwiw, it seems erroneous to omit purple-red): Red: 0-19 & 330-360 Red-Yellow (RY): 20-49 Yellow: 50-69 YG: 70-84 Green: 85-170 GB: 171-191 Blue: 192-264 BP: 265-289 Purple: 290-329

I am doing a image segmentation task based on deep convolutional neural network. The network structure is from this paper , and the structure can be seen in the picture: FCN used in image segmentation . The network is designed based on Fully Convolutional Network and DCAN . The data set is the public benchmark dataset of Gland Segmentation Chalenge Contest in MICCAI 2015 (also named as Warwick-QU dataset.) I use this network to do the segmentation task. However in my result, I always noticed a fixed pattern of noise: the little white crossing in the predicted image: Prediction results with

I am attempting to do automatic image segmentation of the different regions of a 2D MR image based on pixel intensity values. The first step is implementing a Gaussian Mixture Model on the image's histogram. I need to plot the resulting gaussian obtained from the score_samples method onto the histogram. I have tried following the code in the answer to ( Understanding Gaussian Mixture Models ). However, the resulting gaussian fails to match the histogram at all. How do I get the gaussian to match the histogram? import numpy as np import cv2 import matplotlib.pyplot as plt from sklearn.mixture

I am dealing with a image classification problem. Before classification, images should be segmented. I tried several methods. My question is "how can i test accuracy of segmentation ?". I plan to compare final binary image with correct binary image based on pixel differences in order to get a success rate. Is there a more efficient way to compare edges of two binary image, instead of this? Measuring the quality of image segmentation is a topic well studied in the computer vision community. You can see this method that is suitable for binary segmentations. There is also this method for multiple

I am trying to segment cars from image that consist of only one car and an easy background like but what I get from my implementation is this and respectively but it works very easily on almost already segmented images like. giving results like The Code I am using is import cv2 import numpy as np THRESH_TYPE=cv2.THRESH_BINARY_INV def show(name,obj): cv2.imshow(name,obj) cv2.moveWindow(name, 100, 100) cv2.waitKey(0) cv2.destroyAllWindows() def process_end(new): drawing = np.zeros(o.shape,np.uint8) # Image to draw the contours contours,hierarchy =cv2.findContours(new,cv2.RETR_TREE,cv2.CHAIN

I want to use the GrabCut algorithm implemented on OpenCV. As shown in the documentation this is the function signature: void grabCut( InputArray img, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, // * InputOutputArray fgdModel, // * int iterCount, int mode=GC_EVAL) The mode param, indicates how to initialize the algorithm, either with the rect (a rectangle bounding box) or with the mask (a matrix whose values correspond to user paintings of the foreground/background regions. I already have the color models for both the FG and the BG, so ideally I shouldn’t need to provide a