All Questions
Tagged with image-segmentation computer-vision
19 questions
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9
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How to perform user assisted image segmentation using Gaussian Mixture Models?
I have a general idea of Gaussian Mixture Models. My understanding:
GMM is a way of clustering data points which, unlike K means clustering, soft assigns them under different distributions by ...
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22
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Detecting Object Removal in Images
The problem statement is as follows - Given an altered image (an image from which some object has been removed), generate a mask for the removed object.
For instance, say an original image contains ...
- 115
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98
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Instance segmentation using a discriminative loss?
I have been reading this paper and I was wondering if their discriminitive loss definition is correct for instance segmentation ?
From what I understand they map the image pixels into a higher ...
- 111
2
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1
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42
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What are some best practices for labeling data that exists in a continuum?
I am building computer vision models on data that exists in a continuum. For example, imagine I'm trying to do semantic segmentation on cars. Some of the labels are distinct, like "chipped paint&...
- 438
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71
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Is it a good idea to have a category and its subcategories in the training set of an object segmentation model?
I hope you are doing great!
I am currently training an object segmentation model (detectron2 : mask rcnn)
The objective is to detect materials like wood, plastic, glass etc...
wood is one of the ...
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86
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Does atlas-based imaging segmentation generally involve machine-learning [closed]
Segmentation is an important task in medical imaging analysis.
Many FDA approved medical device use "atlas-based" segmentation tasks. Newer device use "deep-learning based" ...
- 120
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415
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Mask for image padding in semantic segmentation
I'm using data augmentation for a semantic segmentation task, where some images are cropped or rotated. As a result, some padding is added to ensure that the image is always the same size. These ...
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2
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1
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370
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What does ADE20k (the scene segmentation benchmark) stand for?
ADE20k is a scene segmentation dataset created by MIT. It is a common benchmark for localization tasks in computer vision research. I cannot find anywhere what the name stands for!
This information ...
- 21
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2k
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Correct way of computing dice score for image segmentation?
In binary image segmentation, for given a set of images, it's true mask and predicted mask. How to compute dice score?, should I compute dice score for each image separately and then find mean across ...
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8
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3
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3k
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Overlap-tile strategy in U-Nets
I was reading the U-Nets paper and there is a mention of some "overlap-tile strategy" in it that I am not quite familiar with. Here is the paragraph from the paper where it has been ...
- 261
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752
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How to classify unbalanced classes in image segmentation?
I'm trying to implement image segmentation for the first time, but I have trouble understanding the data format. As I understand it, my network input is supposed to be an image, so a ...
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1
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1k
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Semantic Segmentation Multi-Class Single Channel Output Math
For semantic segmentation problems, I understand that it's a pixel-wise classification problem. At the last layer of the neural network, I would basically have a 1x1x1 convolution layer with a softmax ...
- 153
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1
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1k
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Understanding the weighted cross-entropy method of u-net
I am trying to implement the weight-cross entropy mentioned in unet
paper to counter the class-imbalances. I am not really able to understand how they are exactly implementing the weight-cross entropy....
- 123
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1
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319
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Semantic segmentation mask
What does the mask look like when doing semantic segmentation. I have 3 classes (background, liver, tumour).
Currently the input to my segmentation model looks like this
(32, 128, 128, 3) where 32 =...
- 3
4
votes
1
answer
493
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Training an Object Detection Model Using with Artificial Data from Video Games
I had an interesting idea of using artificial data gathered from screen shots of a high-resolution video game as a cheap substitute for labeled real data, which can be quite expensive or difficult to ...
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43
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what is the best approach in dealing with large dimension custom data for training and predicting deep learning models
i am trying to implement semantic segmentation for satellite images.My custom dataset has dimensions(height,width)in range (3000, 3000)what is the best approach for feeding(for training) and ...
- 23
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1
answer
1k
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Deep learning models for unsupervised semantic segmentation
I am working on semantic segmentation for satellite images using keras and python. It is my understanding that popular models like U-Net require mask images (labels). Are there any unsupervised deep ...
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1
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1
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876
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Introduction to Conditional random fields
I came across the application of a conditional random field (CRF) to the output from a convolutional neural network (CNN) for image segmentation. The additional CRF step seems to be a common ...
- 569
3
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4
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770
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Hints on this computer vision / machine learning problem
I've been working for a while on a pet problem. The task is to identify and segment out the dark lines and possibly the wiggly ones too.
I'm not looking for anyone to solve this problem for me...I'm ...
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