image-segmentation

问题 I am working on tensorflow2 and I am trying to implement Max unpool with indices to implement SegNet. When I run it I get the following problem. I am defining the def MaxUnpool2D and then calling it in the model. I suppose that the problem is given by the fact that updates and mask have got shape (None, H,W,ch). def MaxUnpooling2D(updates, mask): size = 2 mask = tf.cast(mask, 'int32') input_shape = tf.shape(updates, out_type='int32') # calculation new shape output_shape = ( input_shape[0],

问题 I'm interested in fine-tuning a Mask-RCNN model that I'm using for instance segmentation. Currently I have trained the model for 6 epochs and the various Mask-RCNN losses are as follows: The reason I'm stopping is that the COCO evaluation metrics seem to have dipped in the last epoch: I know this is a far reaching question, but I'm looking to gain some intuition of how to understand which parameters are going to be the most impactful in improving the evaluation metrics. I understand there are

问题 Input is a uint16 grayscale .tif-image, 512 x 512 pixels. As the title to this question implies, I would like to calculate the average pixel intensity of blobs identified by the blob_log method (see: http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.blob_log ) but am unsure how to access the pixel values of each individual blob. Average intensity values must be returned in uint16 range (0 to 65535). Below is what I have so far. Apologies in advance if I haven't been

问题 I'm having following problem: I have black/white images, which I have to colorize: Every white "blob" in the image represents an instance of an object and I want to color every object with a new color, but for every image i want to use the same color scheme: For example: 1st image: 3 Objects -> used colors: red, green, yellow 2nd image: 2 Objects -> used colors: red, green 3rd image: 5 objects -> used colors: red, green, yellow, pink, orange I've colored a couple of images by hand to show

问题 I'm working with the image segmentation. I need to compute image gradient in the point, so I was trying to understand by myself (because I know how to calculate gradient when having an ordinary equation) but I failed. I was googling... and googling but to find wright answer I couldn't. Can anyone say or give some information how to compute image gradient in the point step by step? 回答1: The gradient at each pixel of an image I(x, y) is simply the 2D vector (dI/dx,dI/dy)(x, y) . Approximate dI

问题 What is My Issue? I have generated depth maps from monocular images using DenseDepth. Some of my results are below. I need help masking the darkest shades/ the darkest value of greys in a given range in the depth map. I would like to be able to give two hex values ie. #6E6E6E and #000000 and for the mask to select all the values in-between and then generate a black and white mask. What Have I Tried? After some research I came across this here: https://medium.com/@offsouza/segmentando-objetos

问题 What is My Issue? I have generated depth maps from monocular images using DenseDepth. Some of my results are below. I need help masking the darkest shades/ the darkest value of greys in a given range in the depth map. I would like to be able to give two hex values ie. #6E6E6E and #000000 and for the mask to select all the values in-between and then generate a black and white mask. What Have I Tried? After some research I came across this here: https://medium.com/@offsouza/segmentando-objetos

问题 This might be a bit too "general" question, but how do I perform GRAYSCALE image segmentation and keep the largest contour? I am trying to remove background noise (i.e. labels) from breast mammograms, but I am not successful. Here is the original image: First, I applied AGCWD algorithm (based on paper "Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution") in order to get better contrast of the image pixels, like so: Afterwards, I tried executing

问题 I am using OpenCV in Python to be able to identify only the Leaf presented on the image. I already be able to segment my image, and now I am currently stuck at "how to crop the largest component after I have detected all of them. Below is the codes, please have a look. Using scipy.ndimage, I was unable to advance after find the components: def undesired_objects ( image ): components, n = ndimage.label( image ) components = skimage.morphology.remove_small_objects( components, min_size = 50 )

问题 I am using OpenCV in Python to be able to identify only the Leaf presented on the image. I already be able to segment my image, and now I am currently stuck at "how to crop the largest component after I have detected all of them. Below is the codes, please have a look. Using scipy.ndimage, I was unable to advance after find the components: def undesired_objects ( image ): components, n = ndimage.label( image ) components = skimage.morphology.remove_small_objects( components, min_size = 50 )