fj_abbasi's profile - activity

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2017-09-12 02:36:30 -0600	commented question	Image Segmentation @Ziri k-means clustters the road and ground pixels into same category as both regions are quite the same (here we have d
2017-09-09 07:40:08 -0600	received badge	● Student (source)
2017-09-09 07:39:20 -0600	commented question	Image Segmentation @sturkmen i have edited the question.
2017-09-09 07:36:24 -0600	edited question	Image Segmentation Image Segmentation I m working on road detection, I am following an approach as mention in this paper, which is based o
2017-09-09 07:36:19 -0600	edited question	Image Segmentation filtering undesired pixels I m working on road detection, I am following an approach as mention in this paper, which is
2017-09-09 07:35:08 -0600	received badge	● Editor (source)
2017-09-09 07:35:08 -0600	edited question	Image Segmentation filtering undesired pixels I m working on road detection, I am following an approach as mention in this paper, which is
2017-09-08 09:00:51 -0600	asked a question	Image Segmentation filtering undesired pixels I m working on road detection, After following an approach I have results as shown in the im
2017-09-07 02:40:43 -0600	received badge	● Enthusiast
2017-09-06 10:32:53 -0600	commented answer	grouping/joining collinear segments to fit a line I have to group the pixel belonging to each road in the image and I have no idea how to group them.
2017-09-06 06:33:08 -0600	commented answer	grouping/joining collinear segments These are the results which i can achieve by playing around with HoughLineP parameters but i need more accuracy (line f
2017-09-06 06:30:15 -0600	commented answer	grouping/joining collinear segments This is the stage where I am already as shown int the image below, by playing around with HoughLineP parameter I can ach
2017-09-06 04:55:49 -0600	commented question	grouping/joining collinear segments I guess it' will be difficult t to learn an ideal threshold as images are from different regions, though i have NIR as
2017-09-06 04:55:14 -0600	commented question	grouping/joining collinear segments I guess it' will be difficult t to learn an ideal threshold as images are from different regions, though i have NIR as
2017-09-06 04:08:55 -0600	commented question	grouping/joining collinear segments You mean filtering out pixel values of roads? In that case i guess it may give good result in one particular image but I
2017-09-06 03:23:59 -0600	asked a question	grouping/joining collinear segments grouping/joining collinear segments I am working on road extraction from the image. As a result of HoughLineP applied on
2017-09-05 08:40:13 -0600	marked best answer	Dilation not working I have a distance transformed image and I have to return an image in which each pixel is assigned a highest value in the neighbourhood using a 3x3 grid. I am using morphological Dilation for that, but dilation doesn't seem to be working. It returns the same image. # load the image, convert it to grayscale, and blur it slightly image = cv2.imread(r'C:\Users\x\Desktop\sampleImg\ecu.tif') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (3, 3), 0) #apply Canny edge detection canny_img = cv2.Canny(blurred, 150, 200) #apply Distance Transform invert_canny= 255- canny_img dist_trans= cv2.distanceTransform(invert_canny, cv2.DIST_L2, 3) #normalize to visualize dist-transformed img cv2.normalize(dist_trans, dist_trans, 0.0, 1.0, cv2.NORM_MINMAX) # apply dilation kernel=np.ones((3,3), np.uint8) dilate=cv2.dilate(dist_trans,kernel, iterations=1)
2017-09-05 08:40:13 -0600	received badge	● Scholar (source)
2017-08-30 04:31:13 -0600	asked a question	Dilation not working Dilation not working I have a distance transformed image and I have to return an image in which each pixel is assigned a