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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What is the purpose of zero mean in image processing? [closed]

I know that through zero mean and unit variance we normalize the image. But why is it good to have zero mean? What is the purpose of doing this? I have no transformation to do. I start with an image ...
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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: ...
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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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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. ...
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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 ...
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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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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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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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How to match a curve/image to part of another curve/image?

I have a long curve and want to extract segments with specific feature, the feature is given by a template curve. I intend to use similarity measures like Fréchet distance or dynamic time warping to ...
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Measure for difference or similarity between two regions of the plane

Suppose I have a picture and let two people mark regions within it. How can I measure the (dis)similarity of the two marked regions? More mathematically, suppose I have a (filled) rectangle $A\subset \...
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Clustering points (belonging to polygonal chains) with unknown number of clusters

I have a set of polygonal chains $\{p_1, \dots, p_n\}$ that lie in a plane. $n$ is in the range of $100-200$. Each polygonal chain has around two to five points and represents a handwritten small ...
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Connection between grey scale and intensity in image matrices

Let A be a image matrix where each element in the matrix displays the integer value of the pixel intensity for a specific pixel. We also have a greyscale between two values. For example let A be a ...
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Transfer learning on images with higher dynamic range

Is it possible to fine-tune a CNN-based model previously trained on grey-scale images with 8 bits depth [0 ~ 2^8] to fit a 16 bits depth [0 ~ 2^16] images? if there is any research paper that confirm ...
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Why does torchvision.models.resnet18 not use softmax?

I see image-classification models from torchvision package don't have a softmax layer as final layer. For instance, the following snippet easily shows that the resnet18 output doesn't have a sum = 1, ...
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Why is self-attention used for image classification?

I'm wondering why you would use Self-Attention across an entire image for image classification. What are the advantages of Self-Attention compared to a pure MLP?
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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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Clustering into hours-of-the day, using snapshots of the environment

Let's say I have hourly measurements of 2 variables, taken from all hours of the day. I wish to classify these into (ideally) 24 bins, so that when I get a new snapshot, I might assign it to its ...
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Summary of all the results of all participants after 2015 in ImageNet (ILSVRC) challenge. Does such resource exist?

I have been reading about the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). And while I can find the paper by Russakovsky et al. from 2014(updated in 2015), which contains all the ...
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spherical embedding of image data

Embedding positive data images onto a spherical manifold using the spherical embedding method doesn't guarantee the generation of positive directional quantities as the embedding positions are ...
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What is the filter experiment?

I read a method to check which model does best in measuring uncertainty. Source: page 485 of Probabilistic Deep Learning: with Python, Keras and Tensorflow Probability The steps are as follows: ...
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Edge Detection Convolution Intuition

I was learning about convolution and how filtering helps us to detect an edge in an image;however I still cannot not understand how the convolution process in the image below does this. I understand ...
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Is the density of PixelCNN normalized?

PixelCNN++ constructs a model distribution $p(x)$ over images $x\in\mathbb{R}^{n\times n}$ as a product of conditional distributions over pixels $$p(x)=p(x_1,...,x_{n^2})=\prod_{i=1}^{n^2} p(x_i| x_1,....
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Would preprocessing an image with multiple filters speed up training?

I came across this post - https://stackoverflow.com/questions/23470229/why-do-i-must-use-sobel-operator/23478399#23478399, where they use a Sobel filter for improving handwritten digit classifiers. ...
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Comparing colors of digital and print (in cloth) images

I want to compare color histograms between a digital photo and its physically printed in cloth (like a tshirt) version. The process is simple: Printing a digital image (with good resolution) in a ...
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How Convolutional layer work exaclty in RGB image processing?

I'm studying convolutional layers and I'm pretty confused. Supposing that I give to my network (CNN) an RGB image, so an image with three channels. Since the image has 3 channels, then the kernels ...
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Neural Network Architecture for Image to Image Supervised Learning?

I have a task where I would like to create a supervised learning model where each training record of X,y is a pair of images. e.g. learning a transformation from an image to an image. Is there a ...
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