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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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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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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Neural Networks: How to set the weights for weighted sampling for semantic segmentation?
I'm currently trying to do semantic segmentation with a deep learning model on images. The dataset is highly imbalanced and i would like to try weighted sampling. I'm using pytorch and a dataloader ...
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Does chip size affect accuracy while training the image in semantic segmentation using UNet?
I am working on a UNet model with backbone set to ResNet34.
My data is high resolution (7.5 centimeter) satellite imagery. Currently I am processing my data on my laptop, hence I cannot create a ...
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Why is mAP (mean Average Precision) used for instance segmentation tasks?
I understand why this metric is good for object detection tasks but for instance segmentation tasks it does not give any clue about the quality of the predicted masks.
Shouldn't it be combined in ...
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Is it cheating to stratified split the whole dataset based on a previous test set result?
I trained a model using a small mri dataset(57 patients). The model's performance was so low(Train set 0.7, Val set 0.7, Test set 0.45).
I found the model segments tumor in upper part of brain well, ...
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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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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 ...
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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....
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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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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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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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why encoder/decoder is better than stack of conv layers in segmentation task? [closed]
I have read through several articles that said stacking of conv layers consume lots of computer resources(I guess only run time not memory right?) https://www.jeremyjordan.me/semantic-segmentation/. ...
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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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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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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 ...
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Is the capacity of a multitask U-Net with two-decoders the same of a standard U-Net with doubled capacity in the decoder?
I implemented a U-Net with an additional decoder (one encoder, then it splits into two decoders). The first decoder predicts the normal segmentation label and the second decoder predicts the distance ...
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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 ...
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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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How can I isolate objects from a 3D image volume using normally distributed geometric features
I have '.tif' image stacks that I am analyzing as volumes. For every object, I can get the Volume, surface area:volume, 'sphericity' and Euler number. For every one of those features, the objects I am ...
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Multiclass Segmentation Using U-Net: My training loss is not decreasing after certain epoch (accuracy not increasing) [duplicate]
So the problem is to perform a multiclass segmentation (255 classes of crops), and I am using a U-Net model for that. The input images are grayscale and the images of dimensions (128,128,1) are ...
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Image classification using Semantic Segmented Images
Can we use the semantic segmented images directly to perform image classification using CNN model?
Updating the question:
I am trying to classify images as below:
a. Input : Images taken from camera ...
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How we determine the ground truth box of the object in each frame in Matlab?
When we track one object in a video sequence using a tracking object method, the estimated bounding box is given by the method for every frame of the video. But how we determine the ground truth box ...
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Future of statistical methods in image segmentation? [closed]
I was looking for a purely statistical method for image segmentation and found many, e.g. Hidden Markov Random Fields with EM algorithm. But it seems to me that these methods are nowadays completely ...
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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 ...
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Multi Label Semantic Segmentation - One class is way behind
I'm trying to build a Multi-Label Semantic Segmentation model, but while training, when I'm looking at the validation set, I can see that one label is far far behind, and in the end, he is not getting ...
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To segment or not to segment, this is the question?
I am starting a project, in which I plan to run a neural-network regression using images. These are simple images of particles in a field with low contrast. The shape of the particles changes in ...
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Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small data-set like few hundreds
Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small dataset like few hundreds.
While for the image classification task, it is not ...
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BatchNorm after ReLU
I am currently experimenting with different settings for a U-Net (https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) based image segmentation and I was unable to find out if it makes any ...
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Adequacy of cross-validation in a image classification problem
The problem I am facing is remote sensing image classification. My initial idea was to perform feature selection, classifier selection and hyperparameter tuning inside cross-validation inner loop, ...
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Combining semantic segmentation with image classification (FCN + CNN)
I am currently working on a project that involves classifying each image as Good/Bad/Failed. We have a working convolutional neural network approach that works decent.
I also have trained a Fully ...
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is K-Means clustering suited to real time applications?
I want to segment a sequence of RGB images (basically it's a video) based on their colors in real time. KMeans is an easy and intuitive algorithm to use in this case, but it's execution time is very ...
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Fully Convolutional Neural Network Exploding Logits and Loss
I am trying to train a fully convolutional neural network for 3D medical image segmentation, I have started from the architecture of this paper with the differences being that I have images of varying ...
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Using pretrained segmantation network for unseen motives
For a research project, I need to do a segmentation on images. Since the motivation is nothing any of the big networks was ever trained on, I would ask if it still makes sense to use pretrained ...
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Can U-Net be used for counting objects?
If I understand the U-Net paper correctly, the NN output is segmentation of known objects on the image from the background. In other words, the network will try to mark all the pixels which are part ...
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Evaluate image segmentation with the absence of ground truth
Motivation:
- Evaluate the computerised image segmentation against manual segmentation;
- Evaluate the difference between difference manual segmentation.
Background:
Given a raw medical image (...
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Loss function for semantic segmentation?
Apologizes for misuse of technical terms. I am working on a project of semantic segmentation via convolutional neural networks (CNNs) ; trying to implement an architecture of type Encoder-Decoder, ...
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Utilizing Graph Structure when using Mean Shift Clustering
Imagine we are clustering pixels in an image. If we use Mean Shift Clustering, at least in my understanding, we will embed each pixel into some dimensional space (intensity, rgb, texture, etc) and ...
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Foot Image Segmentation [closed]
I am working on segmenting plantar foot image. i am finding it difficult to find the edges of the foot without the details inside. Please take a look at the canny edge detected. It is full of noise ...
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What are the difference between Dice, Jaccard, and overlap coefficients? [closed]
I come across three different statistical measures to compare two sets, in particular to segmentation on images (e.g., comparing the similarity between the ground truth and the segmented result).
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Image Segmentation with a challenging background
[cross-posted from datascience, as no answers received]
I'm working on an animal classification problem, with the data extracted from a video feed. The recording was made in a pen, so the problem is ...
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What are good / simple techniques available for segmenting non-cursive handwriting images?
I need to process English hand-written form fields. So the hand-writings are expected to be mostly non-cursive but the letters may occasionally overlap with each other slightly, with some punctuation ...
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CRF or MRF energy functions for image segmentation
I am currently working on image segmentation for the purposes of computer vision. I have read many papers and a few books dealing with MRFs and CRFs for computer vision. All of them define an energy ...
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Cost function for image segmentation
I am currently working on image segmentation based on superpixels. My input is a data matrix that contains stixels (rectangular superpixels that span an entire column). In the matrix I have stixel ID, ...
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Identify region of interest in image
I'm currently trying to work on the challenge https://www.kaggle.com/c/noaa-right-whale-recognition; I've done basic image recognition work before (Identifying plankton), but this particular challenge ...
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Interpretation of cross validation results when comparing models
I'm trying to solve a bio-medical image segmentation problem using a binary classifier and then a spatial smoothing (assuming continuous regions). I have:
Training set of 10 3D scans, a total of ~30 ...
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