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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 are the shortcomings of calculating the loss in pixel space vs. feature space

While training (Variational)-Autoencoder networks, I came along the paper by Higgins et al. "DARLA" where she stated: The shortcomings of calculating the log-likelihood term [...] on a per-pixel ...
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Why is my Semantic Segmentation DL network decreasing in accuracy?

In order to familiarize myself with semantic segmentation and convolutional neural networks I am going through this tutorial by MathWorks: Semantic Segmentation Using Deep Learning I did not use the ...
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Measure of smoothness

I have an image that has artefacts which I am using a specific process to remove. I want to show that the new image is improved by that process. To compare the two images I am using data from a ...
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How to use naive Bayes when you have 1000's of features? [closed]

I am working on fruit classification project. After I feed my images with feature extractor methods I get a lot of features like about 2300 columns. Also, I am supposed to implement naive Bayes from ...
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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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1answer
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How can I mix image and data into a CNN

I've recently been testing around tensorflow and keras and I've been doing a project to classify images. So far it's been working but now I want to use real data mixed with the image in order to solve ...
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Metric for evaluating predicted bounding boxes from semantic segmentation on an object level outside of training

Context For simplicity let us pretend we are performing semantic segmentation on a series of one pixel high images of width w with three channels (r, g, b) with n label classes. In other words, a ...
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1answer
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Clustering / Grouping on image's pixels

I have an image, and im building a model to recognize a pattern in that image and classify it. There is however a lot of noise in the rest of the image, but the actual pattern to classify will always ...
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Encoder Decoder networks with varying image sizes

Encoder Decoder Network - Computerphile : At the very beginning of this video, Michael Pound goes on to say: So it (encoder decoder network) makes no assumptions about the size of the input the ...
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1answer
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Are eigenfaces same as eigenvectors?

I'm trying to understand the difference between eigenvectors and eigenfaces, are they different names for same concepts? I ask this because I got confused when I am trying to compute eigenvectors for ...
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Training accuracy of the model keeps decreasing with each epoch even though loss is decreasing

I am training a model to recognise characters from images with 8 conv layers. I am having problem that the train accuracy decreases by large value in each epoch, the first few have like .80-.90, even ...
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MNIST digit recognition: what is the best we can get with a fully connected NN only? (no CNN)

To fully understand how it works internally, I'm re-writing a neural network from scratch in Python + numpy only. (As it's for learning purposes, performance is not an issue). Before moving to ...
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leave-one-out cross validation on images that have a discrete labels

How should I do leave-one-out cross validation on images that have a discrete labels (either Python or R)? Most of the examples I see are quite different (they are not images).
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1answer
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Why map the pixel grayscale [0, 1] to [0.01, 0.99] before feeding to the neural network? (MNIST digit recognition)

In this introduction to neural networks (I enjoy it because it builds a digit-recognition neural network from scratch with just numpy, without any high-level NN library like pytorch or tensorflow; ...
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U-Net image size for training

I have a small question regarding the size of images used for training the U-Net. I have thus far been able to train a U-Net reasonably well using 656x656 images and now wanted to use sections of ...
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Recognition the same object from different views

I have 33 classes (33 different objects). I need to recognize the object from any view of the object. Like a packet of potato chips, the packet has different appearance from different view (as shown ...
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1answer
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Using step function as activation function in the final layer

I am using variational autoencoders as machine learning algorithm. My input data are images/matrices that represent user interface layouts or how the HTML page will be divided. I am thinking to ...
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Is YOLO a good algorithm for defect detection on images?

I wish to train an algorithm to detect defects on images of labels. These may be such things as scratches, tears and voids. I would like to try to train a YOLO algorithm to do this, but it is very ...
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How does this log-likelihood measure similarity between histograms?

I am unfamiliar with these kind of statistics, and although I've read a number of papers on various sites, including some of the links provide in other questions on this topic on CrossValidated, I am ...
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Feature recognition with partial images on CNN

If I train a CNN to learn to recognise features on complete images, would that same network be able to recognise features on a partial image of the same type? The motivation for asking this question ...
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How to calculate the image dataset's mean and std for deep learning?

Which is correct? Suppose that the image's size is (channels, height, width). ...
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1answer
52 views

Variational Inference: Ising Model

I am self learning Variational Inference. Currently I am reading the chapter 21 book from Murphy 1 and trying to understand the Ising model (21.3.2). The Ising model here is used as denoising ...
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How to calculate image similarity between 2 images using DTV?

I try to implement Differential total variation (DTV) as described in (Wu, Y. et al 2017) or (Li, Y. et al 2015). (Wu, Y. et al 2017) describe DTV as following My DTV Matlab function is ...
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Unimodal in machine learning with face recognition

Having trouble with the concept of machine-learning, when it comes to face-recognition, obviously, at least from what I've read, a multimodal distribution is preferred instead of a unimodal. I can't ...
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Clustering cells within the same class based on image similarity

Given a dataset of cell images within same class (same type of cell already classified), I've been tasked with clustering them based on some "similarity" metric. The idea being that it will help a ...
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Why truncated SVD can denoise images

There are a lot of empirical results about that truncated SVD (TSVD) can help denoise the noises of images, but I wonder what is the theoretical support behind that? We know that TSVD is the best low-...
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2answers
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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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Image reconstruction with deep learning preserving speckle noise

My goal is to reconstruct images that have a lot of speckle noise on them, using a deep fully convolutional neural network. So far I have tried using the obvious choices of L1 and L2 losses for the ...
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Siamese Networks Pytorch

I have 2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ....
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1answer
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How to automatically cluster a U-Matrix?

After training a self-organising map, one can calculate the U-Matrix. There are some tools to manually visualize it and identify clusters, but I'm wondering if there is any algorithm to do this ...
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Optimizing popular convolutional networks for grayscale

Common questions in the stack community are variants of " how do I use a pretrained alexnet for grayscale images?", or "How can I do transfer learning from a pretrained network on grayscale images?" ...
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Seeking the terminology of a particular type of object localization

Much like this paper on cell detection, I have a vision task in which I'd like to output the pixel coordinates of object centers. The number of objects can vary. Effectively I'd like to learn a ...
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How to recognise texture areas on images

Any ideas, how to detect marked areas using computer vision and machine learning algorithms?
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1answer
69 views

Interpolating between consecutive weather radar images

I have a series of rainfall intensity images from a weather radar taken every 10 minutes. My goal is to generate intermediate frames in order to create a slow motion video. I've tried using the ...
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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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Difference between Mean/average accuracy and Overall accuracy

I just got confusion while reading the paper "Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance". They mentioned that they repeated each experiment 10 times and ...
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1answer
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Quantitative evaluations for image classification

Hello I am working on the classification of different weed categories. I want to know what quantitative evaluation I can do other than find the accuracy?
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Developing algortihm/model to identify thin linear features in aerial imagery [closed]

I am exploring the possibility of identifying fencelines from NAIP aerial imagery (GSD = 0.6m). I have tried some basic processing in OpenCV using canny edge detection that was detailed in a question ...
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Is it a good idea to train a Neural Network on continiously randomly generated training data? [duplicate]

Hello everyone I'm building a license plate detection model in Tensorflow. I built a function that chooses a license plate at random from a collection of ~5000 plates and puts it in a random place in ...
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1answer
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Find similar images in a dataset without labels [closed]

I have a set of grayscale images, some of them are transformed of the other images. For example in 10 images, image 2 is the same as image 8 but rotated, and image 4 is the same as image 7 but ...
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Multi-class image prediction post-processing

I am using an FCN to do multi-class pixel-wise image segmentation and my ouput is a 4D image (4 classes) matrix. Each dimension of the matrix, which represents the 4 classes, is a matrix of ...
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1answer
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Dimensionality Reduction on VGG Image Vector

I have a random forest model which I am using to make retail demand predictions. I am looking at trying to leverage product image data to improve the predictions and have put the images through VGG-...
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How to interpret semivariogram parameters from two different raster images?

I know the definitions of the components of a semivariogram. However, I would want to know how they could be interpreted when applied to actual scenarios. For instance, I have two EVI (enhanced ...
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1answer
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Structure Recognition within images by Machine Learning with scikit-learn

I want to solve the following problem: Quantify the share (numbers of pixels) of soil, leaves and fruit (ears) within the given image. For soil, this can easily be solved by looking at one of the ...
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Can few-shot semantic segmentation help improve accuracy for the minority classes?

Few shot learning (Or one shot learning) for the image classification problem can be used when there are few samples per class in the dataset (One method is siamese networks). Few shot semantic ...
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1answer
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Leveraging Images in Random Forest Predictive Model

I am using a random forest to make numerical predictions for the performance of products using structured variables, and am looking to leverage images to improve my predictions. One idea I have is to ...
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Why does image rotation prevent overfitting in few-shot learning?

From this paper in section 4.1: To reduce the risk of overfitting, we performed data augmentation by randomly translating and rotating character images. We also created new classes through 90◦, ...
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Convolution in Image Processing

How does Convolution in Linear Shift-Invariant systems ( Digital Signal Processing ) relate to Convolution in Image Processing. Mathematical details would be appreciated!
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Mean centering and normalization along every dimension or over whole dataset

I'm working a side project which involves using a pre-trained CNN and I came across a piece of code that made me question some of my recently gained knowledge around mean centering and normalization. ...
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What's a good approach of classifying video frames?

I am working on a project where I want to classify what's currently on a frame. For example, given a video of a TV recording, you would have classes as: Ads, Show Opening, Show. I have multiple ...