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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 create synthetic images using transformation, rotation etc in Matlab for classification task [on hold]

I have a highly skewed data set XTrain and I am trying to split it into train/validation/test. 99% belongs to the majority while only 1% (sometimes even less) ...
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1answer
26 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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13 views

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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0answers
18 views

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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0answers
14 views

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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17 views

Image Classification using openCV [closed]

I am currently working on a project, where the problem statement is to detect handwritten text from a image of a particular form. As a pre-processing step I have extracted texts in the form of ...
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0answers
27 views

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
39 views

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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25 views

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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0answers
28 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
18 views

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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0answers
11 views

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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13 views

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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0answers
10 views

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
62 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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0answers
29 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
116 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
36 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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0answers
26 views

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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0answers
27 views

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
37 views

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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0answers
12 views

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
32 views

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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0answers
19 views

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
24 views

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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0answers
58 views

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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0answers
19 views

training GANs to inpaint images

I want to train a GAN, the PGGAN from NVIDIA(official implementation available with Tensorflow here), to inpaint images which have been cropped in free form. I have a data set of images from a ...
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1answer
15 views

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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0answers
13 views

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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13 views

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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11 views

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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1answer
31 views

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 ...
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1answer
76 views

How to pass training data to a neural network for digit recognition [closed]

I'm building a neural network from scratch that is using basic c# functionality like 2D arrays, loops etc I do not want to use TensorFlow or any other packages because my goal is to move this code to ...
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0answers
13 views

Why do we subtract the $cdf_{min}$ in this step of histogram equalisation?

In my question I am referring to histogram equalisation as it is described in the wikipedia article in this example here. Given an grey image $I$ of size $M \times N$, in histogram equalisation one ...
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1answer
23 views

biological fluorescence supervised machine learning [closed]

Some general questions... So it appears with most classification learners one must come up with a series of quantifiable variables to associate with each observation. This might include mean and std ...
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0answers
35 views

Are null space of matrix and kernel function same?

I have recently started learning about machine learning and have come across kernels and null spaces. I understand that null space is the set of all vectors that satisfy the equation A.v = 0 (Where A ...
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2answers
101 views

How to set class weights for multi-class image segmentation?

I am trying to set class weights for a neural network with an imbalanced dataset. Let's say I have the following values: I have 8000 images of class A, 1100 images of class B, 400 images of class C, ...
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0answers
15 views

Metric to determine distance of good pixels to a target value in an image with many defective pixels

An xray source emits radiation with strength being a function of some variables, one is kv. The xray strength is supposed to increase monotonically as kv increases. Then an xray detector intercepts ...
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0answers
25 views

Benchmarks and state-of-the-art methods for semantic segmentation of 3D meshes?

I'm wondering what benchmarks there exist for semantic segmentation of 3D meshes? I have already found "A Benchmark for 3D Mesh Segmentation"; is this currently the only benchmark that exists for 3D ...
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19 views

Spatial Transformer Networks and Data Augmentation

The famous Deep Mind paper STN allows for input data transformation, as seen in https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html does not apply input transformation to the ...
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2answers
36 views

Using a neural network to learn to distinguish blurry images?

Is it possible to train a neural network to detect whether an image is blurry or not? I'm currently using synthetically blurred images to build the classifier. However, I'm worried that this approach ...
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0answers
114 views

PCA - Background removal

I saw this example in a python notebook on Fast.ai. In the notebook they are removing the background and keeping objects in the foreground in a video sequence by using different methods(SVD, random ...
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1answer
65 views

Detect visual attention area in an image [closed]

I'm trying to detect the visual attention area in a given image and crop the image into that area. For instance, given an image of any size and a rectangle of say LxW dimension as an input, I would ...
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0answers
125 views

Can I increase the accuracy of Unet by training on signal vs background (2 class) VS signal only (1 class)?

I have a Unet deep learning architecture, and it is working ok at detecting my signal, however, its accuracy is not sufficient for the purposes I am trying to use it. Let me show you an example: My ...
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3answers
250 views

Convolution with a non-square kernel

So far I've only encountered convolution kernels which are square (ie, have the same rows as columns). Are there any cases in which a non-square kernel makes sense? If not, why?
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45 views

ML techniques to classify simple polygons

I'm curious about suitability of Machine Learning techniques to classify grayscale images of polygons into categories defined by number of their sides, which will be kept small. Images will be "...
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1answer
87 views

Are there networks specialised on object detection for a single class of object?

I want to detect the location of a single class of object, which might occur multiple times in an image. Specifically, this relates to research on detecting brake lights for autonomous vehicles. I ...
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0answers
23 views

Simple way for calculating unary potential for an image pixel in segmentation problem?

In an image segmentation using a Markov network how do you calculate unary potential for each pixel? I have an original image(rgb) and a ground truth image and two classes(foreground|background). ...
2
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1answer
99 views

SVM classification on distance matrix

How can I do an SVM classification when I only have a distance matrix (pairwise matrix)? Edited: I want to classify my data in two groups: healthy and sick. My original data are histograms (which are ...
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0answers
39 views

Generating complex controlled image data

In order to demonstrate properties of statistical models it is often useful to generate data, which is messy, but messy in a controlled way which is well-understood by the generator. For example, to ...