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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How to calculate the transalation and/or rotation of two images using fourier transaform?

I need find the translation and/or rotation of an image and himself translated and/or rotated (x0, y0) px and/or J degrees. Given the two images I need to find N.
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1 answer
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Is there any well-founded way of calculating the euclidean distance between two images?

I need to determine the distance between two images. Supposing that we are dealing with images of the same size, I think that we can reduce this problem to the square root of the sum of the square of ...
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Standard deviation of symmetric data

Within my field a recent study suggested to use the symmetric properties of certain image datasets to improve signal to noise ratio (SNR). I will spare you the details, but in the end one can get a ...
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Computing the correlation index when there are few values available

I'm investigating the effect of image quality on system performance. That is, I have an image processing pipeline that receives an image as an input and delivers a function. Now I wanna statistically ...
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1 vote
1 answer
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Best reconstruction loss for RGB images?

Which loss works the best for pixel-wise RGB image (3, width, height)reconstruction loss? It seems there are several options Regression way. The input image has ...
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How to explain the high accuracy and F1 score on the test set with a huge binary crossentropy loss?

I'll provide a little of introduction based on my example. I have a small collection of RGB (but 'gray-looking') brain MRI photos, divided into 2 classes: healthy and tumor. My data split looks like ...
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Image preprocessing guidance for a Multi-class image classification problem

Scenario Multi-class image classification of mechanical parts. The Train set contains images of parts on a white background. The Test set contains images of parts from the workshop. Parts are ...
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Pix2Pix facede dataset, prevent "gray" in dataset to be predicted

I'm trying to build from scratch the pix2pix architecture, the one on this paper. As they did, I'm using the facade dataset, and this is one of their result: I'm particularly interested in the last ...
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Better noise distribution in GANs?

I am just wondering if there is something analogous to the Kaiming He Initialization in GANs for better training (better convergence, training time, etc.)? For example, can the generative model use ...
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Centering in normalized cross correlation for template matching

Context I'm following Lewis (1995) exposition on normalized cross correlation for template matching (Section 2). The cross-correlation of the image and the feature at $u,v$ is denoted by $c(u,v)$ and ...
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Predict the missing pixel value by CNN

The data is images which resize to 256*256. And for per 8x8 area, we remove 4 pixels from the 8x8 block. Then iter this process to whole image. So the task is how to use per block pixels value(60 ...
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Why $P$ and $Q$ don't exist on the same coordinate, they need to be reconciled (processed) to exist on the exact same cells in order to calculate RMSE

I have a question about the root mean square error and Wasserstein distance on the paper https://arxiv.org/abs/2111.08736?context=stat. Consider two discrete probability distributions $P=\{P_i\}_{i=1}...
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Significance test for an image centroid

I have an n x n matrix. Each cell contains a value. The matrix is essentially a heatmap. The null hypothesis is that the greatest values would be at the horizontal and vertical midlines of the matrix. ...
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How to develop Multistage classification model using deep learning

I am little confused while doing multistage classification using deep learning model. I have data as below: ...
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1 vote
1 answer
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Where can I find pre-trained fully convolutional neural networks? [closed]

I know that fully convolutional neural networks can be used for classifying images of arbitrary sizes. I would like to use some pre-trained fully-convolution neural networks for extracting features in ...
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Image generation with multiple images as inputs

I am fairly new to machine learning. I am trying to generate a new image from other images of the same shape. An example of the image I'm trying to generate is an Hi-C data matrix: https://encrypted-...
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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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Autoencoder for Image Classification/Clustering

I'm trying to construct a convolutional autoencoder that will learn features of an image dataset in the low-dimensional latent space. My hope is to use the latent vectors to cluster the images into ...
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Normalizing features for CNNs and out of distribution

As I was reading this question on another thread: Why normalize images by subtracting dataset's image mean, instead of the current image mean in deep learning? I realize that either one point is ...
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Testing for constant mean (stationarity)

I have a grayscale digital image with noise and I would like to test for uniformity in small neighborhoods. One hypothesis says that the mean is independent of the spatial coordinates, against the ...
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How to perform image classification in a dataset of images with heterogeneous sizes?

I have a dataset of images with very different sizes (ranging from 100X100 pixels to 5000X1000 pixels) and aspect ratios. I want to use neural networks for dealing with this problem. Is there any ...
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What are some of the commonly used image processing techniques for multiclass image classification?

I'm working on multiclass skin disease image classification(caused by bacteria and fungus). Some of the sample images are shown below. Images contain different background as shown in image_1 and ...
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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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Strategy for Train/Test-Split on Video Sequences

My dataset consists of 15 video sequences, each sequence showing a different movement. I want to train a CNN to detect poses (e.g. standing, sitting, ...) on single frames of this dataset but struggle ...
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Structure from motion approach to estimate target frame from nearby frames

My question comes from the paper: https://arxiv.org/pdf/1704.07813.pdf , which is an unsupervised learning approach for depth estimation. Suppose I have a sequence of images $I_{t-1}, I_t, I_{t+1}$. I ...
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Finding a dataset for a computer vision project related to medical imaging (related to cancer/tumor) [closed]

I am trying to find a dataset of medical images related to tumor/cancer, there should be images different stages of the cancer and also preferably the details about the patient, their medical history, ...
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Image-recognition model makes good predictions only with training examples

Im trying to use a kaggle dataset to train a model that recognizes american fingerspelling language from an image. The problem is that, built the model, if i record the screen with the examples ...
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Really poor result using transfer learning on medical images vs on bees/ants dataset

I am using an Inception V3 model pre-trained on ImageNet and when I train it on Bees and Ants dataset from PyTorch training, I get this result: Dataset statistics: ...
3 votes
2 answers
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About basic understanding of attention mechanism and model weights

In the image domain, for a given image, suppose that we want to understand if there is a bird on that image (0 and 1 label = no bird and bird), attention mechanism helps to pay more attention to the ...
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No overfitting but bad prediction

I classified some medical images. And distribution of the dataset is : 494 Train Anormal 469 Train Normal 37 Test Normal 64 Test Anormal 84 Val Anormal 37 Val Normal ... My training result is (by ViT):...
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What should I do with Images that has no objects?

I have a dataset that contains images that has cancerous nodules. I want to use object detection models to detect these nodules from the image by using an object detection model. Now my dataset has ...
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1 answer
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How to get the probability a prediction is correct from a binary classifier

I have an image binary classifier that where class a = 0 and class b = 1 When I receive a prediction of a single image, is working out the probability that the prediction is correct as simple as: a: ...
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Classifying the relationship between two objects in an image

Let's say I have a photo of a group of people, and some of the people are pointing at each other. I'd like to train a machine learning model to detect two things: Which people are pointing For each ...
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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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How to quantify nature-likeness/criticality/complexity of image based on its image-statistics in code? openCV examples?

I'm trying to come up with an image statistic that quantifies the criticality of an image. Criticality is a scale that is highest when an image finds itself on a sweet spot between randomness (e.g. ...
1 vote
1 answer
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Question: Optimal D notation in Generative Adversarial Network (GANs)

I am completely new to Computer Vision and how Deep Neural Networks work on images in general. In particular, I have questions on the Discriminator component of Adversarial Generative Network (GANs). ...
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How to perform data augmentation with traditional machine learning algorithms?

I am currently working on a multi-class image classification project, in which I have to use traditional machine learning and feature extraction methods (no convolutional neural networks). I know data ...
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Reducing multiple images into one for visual product recommendation?

I am trying to create an efficient visual recommendation system for real estate but I am running into difficulties regarding image processing. Indeed, the 'products' in this instance are defined by ...
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How to check statistically significant relationship between images and the values stored in the CSV file?

I am trying to perform a regression task with CNN. I am using some images as the predictors, and the independent variable is coming from a CSV file. The images are sets of satellite images and the csv ...
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Quantifying spatial correlations for independent images

I have a series of maps (100x100 pixels each) and for each map, each pixel is labeled according to what is located there (currently values are in RGB, where each color denotes a different substrate). ...
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Small vs large image classification

How should a well-performing 50x50 CNN be adjusted for 500x500 images? Deeper, wider, larger kernels, more stride and pooling? Any ablation studies? Does the verdict change if there are 100 times ...
2 votes
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How to choose the number of latent dimensions in VAE?

I have trained a VAE that can generate photos of human faces. I have isolated the dimension that correlates most to smiling and now I only want the VAE to generate smiling faces. May I know is it a ...
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What is a good approach to increase the depth of Nifti file format or Dicom file series?

I have a CT scan dataset of skull fracture consisting of multiple fractures and normal cases, the CT scans are in Dicom format. I want to do multi-class classification. But not every Dicom image ...
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1 answer
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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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Should I use every Dicom slice of a Dicom series?

I have a skull fracture CT scan dataset, consisting of fracture or normal cases. My question is: Let's say patient-1 has a skull fracture, and his CT scan has 300 Dicom slices. Now should I label ...
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Help interpreting image prediction

Could someone tell me why my predicted result ("Predicted Heatmap") has "ghost layers" and gray background? What can I do to improve my model? **What I've done to the images ** ...
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Mixup VS CutMix Data Augumentation Method

I am looking for arguments on which Data augmentation (Mixup VS CutMix) method would be preferable for Image data and Time-series classification data. As for as I know, both have the following ...
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Successor to Densenet, and Why Would My Resnet Model Peform Better Than My Densenet Model?

Currently, I'm modifying PoseNet, for use with camera localizaiton, with Resnet and Densenet. I modified both to have 2048 output classes, which feeds into dense layers to get the camera pose(like the ...
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Fisherfaces/Linear Discriminat Analysis - What are those faces supposed to be?

I see a lot of weird blue/gray/green faces when I search for "firsherfaces" at Google. I see faces like this and my question is simple: What are those faces supposed to be? Are they some ...
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Is there any standard way of measuring the intra- and inter- class variance in image datasets?

I'm dealing with an image classification problem and I have a feeling that we have a small inter-class variance and a proportionally big intra-class variance. However, it would be interesting to use ...
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