Questions tagged [image-processing]

A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhancement (in particular, pattern recognition and projection).

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9 views

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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image with 4-channels [closed]

i have images with 4-channels that i created by stacking RGB and thermal data. as follow: ...
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22 views

Autoencoder failed to find anomaaly

We're using the following architecture for anomaly-finding- ...
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What could be a good metric for rotation/translation-invariant image similarities?

An acquaintance posted an interesting image processing question. In the image below, we can tell that (a) and (c) are similar, whereas (b) is different. If we use (a) as a template and want to have a ...
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32 views

Which statistical test when you have two reviewers (for qualitative scores)?

Two reviewers (radiologists) assessed image performance (resolution, signal/noise, contrast...) based on a 5-point scale : 1, markedly worse; 2 worse; 3 equivalent; 4 better; markedly better) of 23 ...
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Is validation performance sufficient for hyperparameters choice on a small dataset for images multi-classificaiton problem

Problem: multiclass (3-6 classes) images classification (DeepLearning). Dataset size <2000 samples. One class is rare <50 samples. We've conducted several sets of stratified cross-validations ...
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“Conorm” image preprocessing

For a personal project, I am trying to reproduce the MCDNN of the paper Multi-column Deep Neural Networks for Image Classification by Ciresan, Meier, and Schmidhuber on CIFAR-10. The different ...
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Bilinear Interpolation Algorithm for up-sampling 2D images

In keras it is possible to use UpSampling2D layer to up-sample an image. You can use Bilinear Interpolation. Given an image ${h\times w}$ it is possible to increase its size in ${h*k\times w*l}$, ...
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Patch wise feature vector comparison

I have a image of size of 64*64. I am trying to compute HOG features for the image. I have skimage for my implementation, with the following parameters: ...
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Feature Importance for each instance (point) for an image classification system by unsing CNN

I have developed a simple binary classification system (true, false) by using convolutional neural networks in keras, My input image are color images with the shape of 100,100,3. I am just wondering ...
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20 views

repeated measures ANOVA or one-way ANOVA for a radiology study? [duplicate]

I am conducting a study on different image reconstruction algorithms for computed tomography (CT). My aim is to investigate which of the image reconstruction algorithms that have the highest signal-to-...
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Normalization of 3D medical images

I am trying to normalize a medical (MRI) 3D image of the brain with shape DxHxW. My question is, how should I normalize this image since there is correlation between each slices? Is there a way to do ...
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Creating an output image using Convolutional Neural Networks

I am currently working on undergraduate research to determine hotspots for hand-surface contact. Ideally, I would like to give the model a depth image as input: Example of a synthetic depth image and ...
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Padding images of size less than 5 by 5 pixels for CNN

I am working with multispectral Sentinel 2 images in 20m resolution meant as 3x3 pixel image is 60x60 m in extent in real. Most relevant information is in values of intensities of nine different bands....
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Cell colony survival mapping in a particular spatial pattern

I am attempting to spatially map the cell survival in a given scanned image of a cell flask. Quick background: the cells have received a high dose of irradiation (protons/X-rays) delivered through a ...
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30 views

Which one-sample statistical test for ordinal variables?

I have one group of <25 subjects and multiple evaluation scores: New images of every patients has been rated by a radiologist compared to old images (1 to 5 with 1 Markedly worse, 3 similar and 5 ...
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distribution of image data?

(I don't much about deep learning, but have been playing with a few things and have some questions.) I took the (pretrained on imagenet) resnet18 model from pytorch, removed the last fully-connected ...
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How to treat (label and process) border cases in machine learning?

In every computer vision project I struggle with labeling guidelines for border cases. Benchmark datasets don't have this problem, because they are 'cleaned', but in real life unsure cases often ...
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8 views

How to calculate labels and scores for a feature detector without classification or segmentation?

I am using SIFT feature detector to detect features from ground truth image and test image. I am using the location and scale of ground truth feature to define the predicted features on test image. I ...
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Optimizing a model for three different metrics that have different ranges

I have a multiple object tracker that I apply on a specific object in an image series. The tracker has several parameters that can be adjusted which affects the performance of the tracking. I am ...
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Measurement for how quickly function output decorrelates on changes in the input

So I have a deterministic function that takes a bit-string as input and creates a certain image as output. This image contains some patterns that are directly dependent on the input bits. When taking ...
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Solutions to classify images into good and bad for machine learning use

Our factory produces jumbo rolls (not the ones in the picture) of material and we are installing line scanners that are generating 20 cm long pictures of material from the jumbo. The material can ...
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Tumour cells and ResNet

A patient is represented by a set of images (1000 at maximum) of his tissues of either healthy or tumour cells. A patient is classified as 1 if at least one of the images shows tumour cells. These ...
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Why binary cross entropy is not good for detecting edges?

Why using binary cross-entropy is not doing well in detecting edges on semantic image segmentation? What loss function is a better alternative in this regard?
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What happens if the weights are plot?

Binary Regression for Images I'm not very good at coding, so couldn't really test much this hypothesis. But if weights are a sort of "measure of importance" of a particular pixel, then if ...
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How to choose detection probability of Joint Probability Data Association Filter (JPDAF)?

I want to know about the optimal value or optimal method for choosing or calculating detection probability $P_D$ of JPDAF. I am working on image processing. Kindly guide me. I have searched and found ...
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Calculate standard deviation for grayscale imagenet pixel values with rotation matrix and regular imagenet standard deviation

I want to train some models to work with grayscale images, which e.g. is useful for microscope applications. Therefore I want to train my model on graysale imagenet, using the pytorch grayscale ...
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73 views

Should I use repeated measures ANOVA or one-way ANOVA?

I am conducting a study on different image reconstruction algorithms for computed tomography (CT). My aim is to investigate which of the image reconstruction algorithms that have the highest signal-to-...
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1answer
21 views

Importance for Color in X ray imaging for Detection of Pneumonia

I want to apply Deep learning architectures for detection of Pneumonia on chest X-rays, Should I directly apply CNN on RGB images or I should convert RGB into grayscale image and then apply CNN. ...
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Regression-based or otherwise method to predict distance according to pixel color

What methods could I use to predict distance of a pixel to another pixel in the image according to the first pixel's color? Basically I want to develop a simple regressor that will tell me "this ...
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Image Classification Modeling: How to determine particle size from mobile phone image?

I am looking for modeling advise: Given a picture of particles that can be coarse or fine (think sea salt vs. table salt or fine sand vs soil), I'd like to predict the particle size. Two pictures of ...
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SIFT: why Gaussian blur is performed iteratively?

SIFT is the feature detector I am trying to implement for self-study purposes. But my question concerns the Gaussian blurring done as part of detecting the keypoints. Gaussian pyramid is constructed. ...
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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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Quick technique for comparing images better than MSE

I have been using Structural Similarity Index (through tensorflow) for comparing images, however it takes too long. I was wondering if there is an alternative technique that doesn't take so much time. ...
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About a result of ms ssim

The above formula is from https://ieeexplore.ieee.org/document/1292216, the paper of ms-ssim. I calculated ms-ssim between two dummy images with this code: ...
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neural network for circle detection

I'm new to Neural Networks and I would appreciate any advice. I'm working with the raw data which can be visualized in the form of the image (32 pixels in the x direction and 128 pixels in the y ...
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3d object rotating from one given image

There is a game Stronghold 2001 with sprite graphics. Movable units in the game has different animations that consist of several frames. Each frame is usually represented by 8 sprites from different ...
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How to initialize k-means

I am working on image processing. I have to apply k-means upon them. But I am confused with the initialization of k-means that either I should use just first frame or all the frames to initialize it. ...
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How might disparity in image format/quality between binary classifications affect training of CNN?

I have an image dataset containing two classes. One of the classes has many images and they are all JPG images with the following format: ...
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Two image channels, one quantitative, one “relative”. Best architecture/preprocessing?

I'm performing semantic segmentation on a multi-channel image with a residual U-NET. I'm getting DICE scores that are ok for this task but I want to do even better. The problem is that the first ...
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Gaussian mixture model for image labelling task

I'm trying to solve an image labelling task by using Gaussian Mixture Models. The total number of classes in my dataset is 9, each representing a different variety of vegetable (Class1, Class2, Class3)...
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37 views

Machine learning algorithm to work with body keypoints from image

I'm working on a project that will predict whether a person approaches and intends to interact. There will be a camera and a pose estimation model that will analyze live frames and save the body parts ...
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standard deviation of two constant noised signals related through interpolation

Let us say say we have a noised constant signal and want to evaluate the standard deviation (std) of the noise. We calculate the std of the said noised signal and call it σ1. Now we process the signal,...
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What is a feature vector and how do they differ from feature maps

I'm reading a paper which reviews image upscaling techniques. A model named SRCNN is described as: First layer: creates feature maps from low-resolution input images Second layer: converts these ...
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Difference between Keras' DenseNet201, DenseNet169, and DenseNet121

I have been trying to use DenseNet architecture for the CIFAR-10 dataset. For the first trial I used DenseNet201, and got around 79% validation accuracy, which is pretty less than what I expected. So ...
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Encoding prior knowledge in image classification (A bayesian approach ?)

I have come across bayesian linear regression and bayesian logistic regression. These approaches sample from the posterior of the unknown parameters conditioned on the data using MCMC methods. The ...
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6 views

Difference in features generated by same filters for color and grayscale images?

Would there be ay difference between the features generated by CNNs if they are fed with same image in color and grayscale format. If I am performing classification with same network for let's say ...
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47 views

Using visual representation of time series of unequal length

I would like to apply methods like Gramian angular field, recurrence plots and Markov transition fields to a time series classification (TSC) problem where the time series themselves are all of ...
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Cross-validation necessary when using Random Forest?

I am new to this forum, but I am stuck with a couple questions related to image classification, and seeing the kind and highly useful messages that people provide, I had the hope that someone could ...
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Is there a “good” way to evaluate segmentation in weakly labeled image data?

I have an image dataset for anomaly detection, which has weakly labeled ground truth images for the anomalies. Therefore, if there is a defect in an image, the ground truth would have a relatively big ...

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