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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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Stereo image classification [on hold]

I just been wondering how can I combine stereo vision and Convolutional neural networks for a classification problem
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AdaBoost gets “stuck”, fails to converge [on hold]

I'm attempting to implement Viola-Jones using AdaBoost in Python. During the AdaBoost step, I add each weak learner to my strong learner, check whether the strong learner's FPR is below a certain ...
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Generating vector image from a hand drawn picture. Machine Learning

I am new to machine learning! I need a way to generate vector image out of hand drawn sketch. I dont need to trace bitmap like it is usally done because it gives you exactly what you drawn. I need to ...
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Dimensions and implementation of the Convolution step in CNN

I am trying to write my own convolutional neural network from scratch (Python) and after reading several articles and watching tutorials (on CNN) there are still a couple of issues that I am unable to ...
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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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Statistical Reasoning of Noise Images on Random Pixel Generator

http://www.pixelmonkeys.org/#theory It is always explained that even billions of images are generated per second, it is almost impossible to see a natural image ( whether it is clear or distorted as ...
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Object Localisation without Classification

I have a data set of photos containing an object in each of them. I want to find out the coordinates of rectangle enclosing the object. Note that each photo contains exactly 1 object (for example, if ...
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1answer
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How do ConvNets self-organise to have a hierarchical segmentation of higher- and lower-level features?

As far as I know, each layer of a convolutional neural network used for image classification specializes in recognizing a different part of an image. At earlier stages in the network, more rudimentary ...
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Image Augmentation or incrementing dataset by flipping/mirroring?

My task is a regression task, where an input image results in another, transformed image. So far so good, works quite well. As my data set is fairly small, I want to take some actions. Here I wanted ...
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Which machine learning approach to use for data with very low variability and a small training set?

My goal is to write a program which recognizes the chess position in an image of a digital game. I'm not trying to process actual photos of a game in real life, just images like the own below This ...
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image caption generator

I see two models of image caption generator online: In the above model, the first LSTM cell of decoder takes the entire image as an input. In the above model, all the LSTM cells of the decoder take ...
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1answer
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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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How does smoothing an image gives it a different scale?

In some deep learning papers i read about multiscale inputs, so i wanted to read about scale of an image. What i got to know is that fundamentally scale is related to the distance of the object being ...
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1answer
25 views

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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Is there a way to write a script to delete completely unrelated images using ML?

When preparing a dataset for a classification problem I find myself spending a lot of time manually cleaning up and removing unrelated images. I download a large number of images from search engines ...
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how to calculate the difference between multiple distribution(or frequency list)

Here is the scenario: I have a dataset, which contains list of data points, each point has F features(i.e. float numbers) and a category(there are C categories). I want to compare the difference of ...
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Can I create training split as follows?

I currently have 10000 images for class A and 1000 images for class B. Instead of undersampling or oversampling, I would like to split the class A data into 10 fold and train with available class B ...
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Stack data channels on the input to a CNN?

There are a couple other questions that are similar, but I wanted to be specific. Both this and this talk about adding additional data to a CNN by plugging into the lower layers of the CNN, or using ...
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“Feature Scaling” for images for Conv. Nets

is there something like Feature Scaling for images (before I feed them to a convolutional net)? My guess is that it could be useful to modify all images in an equal way. Maybe so the brightest pixel ...
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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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4answers
135 views

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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1answer
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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
31 views

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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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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25 views

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
53 views

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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182 views

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
78 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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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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130 views

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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1answer
81 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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163 views

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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1answer
900 views

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
41 views

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 ...