Questions tagged [image-segmentation]

Image segmentation arises in computer vision and digital signal processing. The goal of image segmentation is to partition a digital image into pieces, where each piece corresponds to some semantically important concept. Usually, this means that each pixel is assigned to one of the concepts. An simple example is dividing a picture of a person into the subject (the person in the foreground) and the background (whatever is behind and around the subject).

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Data split when using pseudo-labeling semi-supervised learning method

I'm trying to train a 3D segmentation model. The dataset I own consists of small number of labeled samples(~21) and a lot of unlabeled samples(~200). I'm using a simple semi-supervised method, where I'...
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Probability of sampling >50% "bright" pixels from an image

Suppose an image consists of 300,000 pixels. 51% of these pixels are bright and 49% are dark. We are trying to determine if the image is bright or dark through sampling (in this example it would be ...
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Why most works on Cityscapes don't use weighted cross-entropy?

Weight Cross-Entroy (WCE) helps to handle an imbalanced dataset, and Cityscapes is quite imbalanced as seen below: If we check the best benchmarks on this dataset, most of the works use bare CE as a ...
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Can I train Mask R-CNN with very small images and detect objects in much bigger images?

I have lots of images depicting one object each, and the objects are labelled with one of 10 classes (image size 40x20). Is it possible to train a Mask-RCNN with those small images and then detect ...
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Dice Coefficient vs *Negative* Dice Coefficient..?

While reading this paper I've noticed that they use what they call negative dice coefficient. I know that in general the dice coefficient is a metric commonly used in image segmentation tasks when we ...
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Which network to segment a rectangle on the ceiling of a room (enclosed by joists), and taking advantage of prior knowledge

I am wondering how you guys would approach this problem. Given an image from a camera pointing towards the ceiling of a room (some joists are present), I want to segment the biggest rectangular area ...
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When applying transfer learning for semantic segmentation, do you need to apply the same preprocessing to the new images?

I want to use transfer learning. It is not a popular model for semantic segmentation(Unet, etc). So, do I have to apply the same preprocessing to the new dataset? Does this include the masks? The ...
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How to compute ROC AUC for a method that uses two models?

To compare my method with others I'm trying to compute its AUC but I got a bit confused on how to do this for my case. My method uses a model that classifies an image as class A or B, after that if ...
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How to find correlation between segmentation outline attributes and dichotomous data

Background: So I have a dataset of a pair of doctors' diagnosis on N=10,000 patients' eyes for ocular disease. The data is indexed by the 10,000 patients and the columns are: 1 column w/ PNG images ...
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Kernel_size for rgb images in cnn?

I came across a cnn code of rgb images where kernel_size was mentioned only 3 not 3,3,3. So does 3 means 3,3,3. and for greyscale images kernel size was mentioned 3,3 so for grey scale images 3,3 I ...
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Can image tiling be used outside of remote sensing?

I am using computer vision (semantic segmentation)to digitise floor plans. I have read some papers which divide images in patches before the training.Once the training is finished, the patches are ...
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Can Mask-RCNN be used for semantic segmentation?

I am working on a computer vision project. I have learnt about semantic and instance segmentation and the algorithms: Unet, Mask-RCNN, etc. So far, after reading documentation I have associated Mask-...
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Meaning of phrase "standard deviation of 1.5 samples"

In this paper (page 4, 3.3) is stated that "we use a circular-symmetric gaussian weighting function with standard deviation of 1.5 samples". As english is not my primary language, i do not ...
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How to build a custom instance segmentation from scratch

Where I could find information for building a network of instance segmentation from scratch(e.g., using PyTorch). I would like to build a custom image classifier + instance segmentation part, using a ...
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Is there an official procedure to compute mIoU (mean intersection over union)?

Although it sounds silly, I'm not finding an official source to compute mean intersection over union (mIoU). I'm realizing a semantic segmentation task, and I want to compute the mIoU over a dataset. ...
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Two basic questions about icp (iterative closest point) algorithm

I am trying to learn shape analysis and a part is learning icp. I have many confusions but for now I have two basic questions: Does the point clouds need to have the same number of points for icp? ...
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Maximum likelihood expectation maximization (for image segmentation)

I am trying to wrap my head around the concept of using Maximum likelihood expectation maximization for image segmentation. I understand the concept of maximum likelihood as a way of finding the most ...
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Bayesian hierarchical model inference problem image segmentation

it might be really confusing question. I am working on my thesis and I am stuck at a problem. It's a problem in image segmentation and finding parameters of border lines of continuous region in an ...
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Proper way to calculate mean Dice coefficient on a dataset

I would like to ask a question about the proper way to calculate the Dice coefficient for an image dataset. We know that the Dice coefficient is calculated via the following equation: $$Dice = \frac{...
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Proper approach for image recognition of ~1000 symbols

We have a dataset of black symbols in grey squares (like attached below). The symbols are various letters (arabic, greek) as well as numbers in many distinct fonts; altogether ~1000 different images. ...
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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" ...
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Spatial Verification Techniques for Image Prediction

I am working on an image prediction problem, where we use a U-Net to predict a real-valued image. I've found that conventional metrics like MSE, r^2, MAE, etc just don't really cut it. What are some ...
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Keras' sparse_categorical_crossentropy loss

I'm currently training an U-Net which should segment a 3D space. The input is a 128x128x128 voxel grid. The labels are 5 different 128x128x128 masks. One of the mask is actually for the value 0. So ...
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How to properly report results for a medical image segmentation task?

Let’s consider a 2-class / binary segmentation problem where c=0 for background (healthy tissues) and c=1 for foreground (...
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How can I calculate loss if the size of the true image is different from the output in mask rcnn?

Refer to the mask rcnn paper When the feature map passes through the roi align layer, it has a fixed 14x14. The output after the upsampling layer is increased to 28x28. In other words, the size of the ...
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Estimating the mean and variance for a set of probabilities (bounded by 0 and 1) based on Image Segmentation results

Data My data is from a set of images wherein I am computing the Sensitivity of an image segmentation algorithm. Sensitivity is computed as: $$Sensitivity=\frac{TP_{pixels}}{TP_{pixels}+FN_{pixels}}$$ ...
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What loss function should I use in a variational autoencoder model that is supposed to generate binary images but creates only blank images?

Short version: What loss function should I use in a variational autoencoder model that is supposed to generate binary images but creates only blank images? How can I penalize the network for producing ...
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How do I decide the steps per epoch?

I am training a segmentation model where training size is 4000 (768x768x3) images with a batch size of 4 images (because the GPU gives memory error above this). My question or doubt is that when I am ...
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Correct or most common term for altering a loss function to ignore unlabelled pixels?

In my experience it is quite commonplace to alter the loss function used when training a neural network for segmentation to ignore the contribution to the loss of unlabelled pixels. There are a few ...
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About ROC curve in segmentation model

I know how to draw ROC curves about classification model for a one class. And I know how to plot ROC curves about classification model for many classes. But is there a way to plot ROC curves for a ...
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Am I doing correct unit test before whole batch training?

I read somewhere that unit tests are important before jumping onto training for the whole batch. And for that reason, if one sample overfits on the model, can we decisively say that the training will ...
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Object detection model doesn't perform well on camera frames

I am working on my thesis project, and I am a bit stuck on a problem. I am using this pretrained object detection model on a security camera, to find the b-box of objects.: http://download.tensorflow....
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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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Fluctuating loss curve/ steady dice score. Why? And How to improve? [duplicate]

I am training 3D data with multi-class 3D target ground truths(9 tissue labels) for segmentation. Using dice Loss and focal dice loss as loss criterion. Updating optimizer every second batch (...
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Do horizontal and vertical dead pixels on some of the bands of a hyper-spectral image affect the accuracy of the model?

I hope this is the right place to ask this question. It should be here according to questions I saw. I am working of PRISMA Earth Observation images which consist of tens of spectral bands. My final ...
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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 ...
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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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MobileNetV2 in Keras: Adjusting depth

I am trying to reconstruct specific U-Net architecture with the MobileNetV2 backbone using Keras. It seems for MobilenetV1, there is a way to adjust the depth using the ...
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Reusing Weights in Transposed Convolution

As far as I know it's possible to reuse the weights of a convolution in a transposed convolution to upsample an image. However when reusing the weights, the resulting restored images aren't even close ...
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Are F1 score and Dice coefficient computed in same way or different way in image segmentation (two class segmentation)?

On page 8 of the paper An automatic nuclei segmentationmethod based on deep convolutional neuralnetworks for histopathology images, the authors show performance of their deep model on test sets. They ...
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Tversky Loss function for RGB masks

I have a very imbalanced dataset for my semantic segmentation problem (monitoring deforestation using setellite images) and I found Tversky Loss to be much better than categorical crossentropy (due to ...
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How to initialize an encoder-decoder type of neural network that is used in image segmentation?

In this example: https://github.com/qubvel/segmentation_models.pytorch/blob/dcd19d676bdfbf73fc140d5b98d780f449b0a2f8/segmentation_models_pytorch/base/initialization.py It only initializes the decoder ...
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Using FCNN for multi-class semantic segmentation trained on single class labeled image data

I am working on project where main task is semantic segmentation of land cover and another objects in Sentinel 2 multi-spectral images. Currently I posses dataset ...
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How to choose the best segmentation model using the area under the precision recall curve, IOU and Dice metrics?

I am using several U-Net variants for a brain tumor segmentation task. I get the following values for the performance measures including Dice, IOU, Area under receiver-operating characteristic (AUC) ...
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How to measure the performance of Mask RCNN model. Given that there are two tasks , one object detection and another image segmentation

Mask RCNN is an instance image segmentation technique. It is based on Faster RCNN for object detection and an additional mask operation is performed by another CNN.
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Best software for image segmentation for time-series images?

I am currently measuring fluorescence for cells using time lapse images. For each sample, I have 50-100 cells. I currently manually select the ROIs (individual cells) using HCImage and measure the ...
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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 ...
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NAN loss while training a image segmentation model with non-object images

I am currently working on a multi-class image segmentation application. A fraction of dataset contains images whose corresponding ground-truth images do not contain any object (completely black ...
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How to fit a line between areas of high density? (find valley between hills in 3D)

My data are a number of points in 2 dimensions. There are areas that are more dense. I run a kernel density estimation with a Gaussian filter that gives me a result similar to the picture (of course, ...
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Resume training with 'best model parameters' keras

I have been using the Keras callback EarlyStopping to stop my model once the validation error has stopped decreasing. There's an option ...
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