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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 is sigma function in the YOLO object detector? [on hold]

I have gone through the YOLO9000 paper, in that they have mentioned that network predicts 5 coordinates of the bounding box, and from that we find the exact centre coordinates and the width and height....
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Detecting trend in panel data, smoothing techniques and outlier detection

I'm conducting an analysis on a Landsat scene to detect trends for change detection phenomena (forest disturbances) over a time series of 20 years. I identified on the image the pixels that are ...
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What are the important methods that evolved in computing optical flow?

I have gone through various approaches to find optical flow. But I have a tad confusion between Horn and Shunck method and Lucas Kannede method. Where are these methods useful and where do these ...
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Is it feasible to train a model from scratch using 10000 images

Hi Everyone I am a beginner in deep learning and doing a project on deep learning for my college. I want to train a CNN that can classify three classes of Skin Cancer namely Melanoma, Sebborhic ...
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Handle loss while converting high dimensional image to specific size in VGG 16

I am training a VGG16 net using transfer learning. I have removed the fully connected layers and used fine tuning to classify objects into few categories but I have faced below problems: 1.I have ...
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What is scale-invariance and log-space translations of a bounding box?

In slow R-CNN paper, the bounding box regression's goal is to learn a transformation that maps a proposed bounding box P to a ground-truth box G and we parameterize the transformation in terms of four ...
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Augmentation of data collected from single stationary source

How do one augment data that is being collected from single stationary sensor source. The orientation, color and size always remain same. Only the pattern in the dataset vary (example : sunspots are ...
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How can K-Means clustering work without spatial information?

Just got stuck at working with K-means clustering. I have looked up this python/skimage commands: ...
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22 views

Audio files and their corresponding spectrograms for image classification process

Suppose I have a dataset of audio files that I have to use for whale sound classification. I am choosing the strategy of treating it as an image classification problem by using their corresponding ...
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Is background subtraction common practice for image classification?

I am going to build a mushroom identification application and using neural networks for image classification. Right now I am thinking about different image processing methods to implement before ...
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1answer
37 views

How do backbone and head architecture work in Mask R-CNN?

In this diagram, we see the two convs. It is said that these convs are a part of the Fully Convolution Network (FCN). In their paper Mask R-CNN (He et al., 2018), they mentioned something about the ...
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What is the best image labelling tool with these features?

I need an image labeling tool that has the following features: Upload images on the fly (via API or something) I want to compare specific pairs of images, and indicate if they are the same, or not ...
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modelling an image matrix with correlated pixel values

Trying to improve a certain algorithm for manipulating certain micrograph images, I would like to experiment with "random" synthetic images as input to the algorithm. The pixel entries would be non-...
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How to ensemble predictions from image classifier and text classifier?

I am doing multiclass classification based on images and text. I have predictions from both image classification and text. I am not sure how to combine them. Should I use probabilities as a feature to ...
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How to reproduce this algorithm?

I am trying to reproduce one of the algorithms presented in the following paper: Rapid Assessment of Annual Deforestation in the Brazilian Amazon Using MODIS Data The authors provide five different ...
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60 views

Image classification with large images

I am new to image classification and hope to set up a model which will classify large images (I am using R keras). Each image will represent a 10m by 10m square with pixels representing 1 cm. I need ...
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1answer
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Per-pixel classification using deep learning models

I want to train a model to classify image pixels in which neighbouring pixels are not considered, only channels (bands) for each pixel. I'm thinking about defining a CNN model which stacks several ...
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Basis vectors for categorical images

I have a sequence of categorical images. For a two category image, each image pixel can have one of two values. I would like to analyze these images using a technique like eigen images. The goal is to ...
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Stereo image classification [closed]

I just been wondering how can I combine stereo vision and Convolutional neural networks for a classification problem
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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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1answer
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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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1answer
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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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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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1answer
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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
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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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1answer
41 views

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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1answer
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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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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
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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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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
183 views

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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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 ...