Questions tagged [image-segmentation]

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Sort a collection of images by similarity

I have a large list of product images which I'd like to sort by visual similarity and create a nice gallery out of it. What would be the best way to approach it?
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How to find binary image's object size like objects height,width,midpoint using python?

if you see the attached two pictures,then in "the mask" section you can see 2 binary images where white background are 0 and black marked area of object is full of values with 1 now given images like ...
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In image segmentation, is Dice score usually reported as an average between classes?

Dice Similarity or Dice Score is a common evaluation metric for segmentation projects with high class-imbalance. It measures the overlap agreement between discrete classes from two images, ranging ...
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Should training set images change during epochs?

I'm training a convolutional neural networks for image segmentation. In training data preprocessing i'm applying some data augmentation to change luminosity of images. I'm using tensorflow ...
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An CNN seems like capturing specific range of input data. (Image Segmentation)

I'm trying to build a model to segment brain tumors. I trained a model, and the validation dice coefficient is disappointing(0.6). When i saw the predicted images with the ground truths, it seems ...
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What is the difference between Chan & Vese and total variation segmentation methods?

What is the difference between Chan & Vese and total variation segmentation methods, which is better between the two and how to explain that?
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Finding function for histogram to investigate on minimum, maximum and inflection points

To investigate on the statistics of an image, I want to find out how to get all 1.) minima, 2.) maxima and 3.) inflection points for the histogram of a specific image. I know how to extract the ...
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59 views

What is the purpose of the last 1x1 convolution layer in segmentation networks providing a linear transformation of the features?

Semantic segmentation networks make use of a final 1x1 convolution layer at the very end of their network which brings the feature maps equal to the number of classes in the dataset. Since this 1x1 ...
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24 views

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 =...
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MSE loss going Infinite [duplicate]

I am using DeepLabv3+ model to perform regression and predict the value of one of the channels. Details of the model used OS = 8, Backbone = None, loss='mean_squared_error', optimizer = adam(0.001, ...
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If features only vary locally in the input, does it make sense to use a locally connected input layer or a FC layer instead of a convolutional layer?

For example if you have a segmentation or regression problem, but the features that you are interested in are always in the same or similar place in the input image or time series (across different ...
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Why does Dice loss neglect to predict a random subset of classes?

I implemented Dice loss for a semantic segmentation problem (with a severe class imbalance in my dataset) as follows: ...
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125 views

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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112 views

Understanding Accuracy, Recall and IoU

Working on an image segmenetation problem, I've tackled the following scenario repeated on different images: High Recall and Accuracy (around 99%) Low IoU (around 60%) How is that possible? Recall ...
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Increasing Mean Intersection Over Union with Increasing Validation Loss - Semantic Segmentation

Firstly I'm new to Cross Validated so I apologize if this is structured incorrectly or I didn't find some related post or missed out something. I'm training deep networks for semantic segmentation ...
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Are Fully Convolutional Neural Network (FCN) just normal ConvNets?

I was reading the paper Fully Convolutional Networks for Semantic Segmentation and on section 3 they introduce the notation for what they call a Fully Convolutional Neural Network (FCN). Are they just ...