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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18 views

Generating training data for face recognition

I'm in the process of learning Machine Learning and like to do some hands-on experiments. I thought I'd take photos of friends and colleagues to see if I can create a face recognition model from ...
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20 views

How to predict on part of image after training on other part of image?

I have images of identity cards (manually taken so not of same size) and I need to extract the text in it. I used tesseract to predict bounding boxes for each letter and am successful to some extent ...
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1answer
14 views

auto-encoder for unequal image sizes

I would like to use an autoencoder on my training images. The problem is that each image has different size and Matlab gives me an error: ...
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1answer
24 views

type of image to create a dataset for image recognition using convolution neural network

I was trying to create a dataset for animal detection using convolution neural network. It was for some open source project. For the training and testing, I thought to create a dataset myself. for ...
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26 views

Why do we have normally more than one fully connected layers in the late steps of the CNNs?

As I noticed, in many popular architectures of the convolutional neural networks (e.g. AlexNet), people use more than one fully connected layers with almost the same dimension to gather the responses ...
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32 views

Why do we normalize images by subtracting the dataset's image mean and not the current image mean in deep learning?

There are some variations on how to normalize the images but most seem to use these two methods: Subtract the mean per channel calculated over all images (e.g. VGG_ILSVRC_16_layers) Subtract by ...
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1answer
17 views

Skewness and Kurtosis in an Image

I am working with textures in an image. I have implemented algorithms to calculate the skewness and kurtosis in an image histogram. Great, delighted with that. I am using RGB as my color model. I know ...
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0answers
28 views

How to think about the architecture of the Convolutional Neural Network?

Recently, I've started to learn more about CNNs to use them in some computer vision tasks. At the moment, I have roughly good knowledge about different parts of a CNN such as layers, solvers, loss ...
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36 views

What is zero mean and unit variance in terms of image data?

I am new to deep learning. I am trying to understand some concepts. I know "mean" is an average value and "variance" is deviation from mean.I have read some research papers, all say that we ...
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1answer
27 views

Convolutional Neural Network for 3D point cloud?

Can Convolutional Neural Networks or Deep Architectures be used for generating 3D point clouds ?
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12 views

Neural Networks in Image Processing - Literature Reviews

I'm looking for good Literature Reviews on the use of Neural Networks in "Image Processing/Image Retrieval/Image Classification" and generally anything Image Related... Has some work been done in ...
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11 views

Detecting images inside website snapshots and extracting them with machine learning

I was wondering if I could detect images inside website snapshots and cut them out of the snapshot. for example I can download Image Snapshots from any website via PhantomJS. this gives me a ...
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0answers
32 views

Help in understanding a clustering technique using neural network

I am having difficulty in understanding a technique for clustering and segmentation of biomedical images using the concept of time series. The paper on which the Question is based is : M. Lacomi et. ...
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45 views

What is the efficient preprocessing data in image classification task with CNN?

I am new in deep learning on image classification. I know that Machine learning algorithm are very dependent to data normalization. Usually, if we have a training data set represented with X [N*D] ...
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1answer
48 views

Translational variance in convolutional neural networks

Convolutional networks have been proven to work very well detecting a shape independently of where it is in the image, which is referred as translational invariance. In the case where the position ...
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1answer
24 views

What advices do you have for a starter in multiple image recognition?

So, I have experience in machine learning for NLP and a little in neural networks for NLP, but never so far done anything in computer vision in this area so bear with me if what I am asking is a ...
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16 views

Help in understanding an image clustering technique described in a paper

Paper titled :Mammographic images segmentation based on chaotic map clustering algorithm DOwnload link presents a technique of image clustering using chaotic map. I explain briefly : A chaotic map is ...
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31 views

No change in accuracy big vs small training set size ConvNet

I am doing some small experiments with image classification in Caffe using the AlexNet architecture. I use a dataset of 50 classes with each class containing 1,000 training images. After about 2k ...
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1answer
21 views

Drop in results upon addition of new features in random forest model

I am training a classification random forest for object detection in images. I have several features (like HoG, edge features etc) which work good enough separately. But when I train using all ...
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19 views

Understanding filter space in convolutional neural networks and its reduction in Inception architecture

From this source I acquired a quite good understanding of 1x1 convolutions in Inception CNN and how they perform a reduction in the filters dimension. There is one thing I would like to clarify ...
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18 views

How to use a neural network in image recognition? [duplicate]

I understand the idea behind neural networks, but i do not comprehend the practical application of one in an image. For example, if I train a network against a photo of the letter 'A' (30 x 30 ...
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62 views

Image Segmentation with a challenging background

[cross-posted from datascience, as no answers received] I'm working on an animal classification problem, with the data extracted from a video feed. The recording was made in a pen, so the problem is ...
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0answers
37 views

Face authentication system using Convolution Neural Network (CNN)

I'm working on developing an face authentication system using Convolution Neural Network (CNN). I know that the CNN can be used to classify two classes. However, my problem is how can I train the CNN ...
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1answer
126 views

How to train convolutional neural networks with multi-channel images?

I have $m$ labeled images, each with 224x224 pixels and 5 different image channels. What is the best way to train a CNN architecture using this data when $m$ is small (less than 2000)? Is it possible ...
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1answer
49 views

Question about prior in bayesian image processing

I am learning Bayesian image processing. Bayesian approach will take prior knowledge about image into account. From one material, it says knowledge is expressed via probability functions. I understand ...
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1answer
150 views

Class Balancing in Deep Neural Network

I was trying to do class balancing on the image semantic segmentation problem for some classes in the images are in the minority. The weight for each class is calculated as mentioned in this paper: ...
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3answers
59 views

Add New Object Class in Deep Learning Network

Assuming that I have a trained deep learning network that can detect 10 classes of objects (road, sky, tree, etc.) in images. It takes in RGB images and outputs a probability map of size ...
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20 views

Ising-Like Priors with Fractal Boundaries (Application to Image Processing)

Overview: I'm interested in looking for priors that "look a little like" the Ising model, but have different large-scale behaviour. In particular, I'm looking for priors that give rise to large ...
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44 views

Improving the results coming from an image recognition API

We are developing a software application that will automatically suggest tags (keywords) for images that are being uploaded into a database of already-tagged (by a human) images. We are using a 3rd ...
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2answers
43 views

What it mean by Training SVM

I am new to image processing. As my project I am doing "image classifier using SVM". I have the idea of my final software "I select some image and give it as input to my software and it will classify ...
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52 views

Looking for a CNN implementation for 3D images

I'm looking for an implementation in python (or eventually matlab) of Convolutional Neural Networks for 3D images. By 3D I mean 3 spatial dimensions (i.e. not 2D+channels or 2D+time). Any advice?
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26 views

Time series and images : difference and terminology

A time series is an ordered collection of random variables. Considering a one-dimensional time series $A_i = {a_{i1},a_{i2},\ldots,a_{it}}$ where $t$ denotes the time index. So, the time series is a ...
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1answer
40 views

Need guidance on image classification problem with large feature matrix

So I've got an interesting problem that I'm struggling with and I wanted to hear some ideas on possible solutions. The data is not public and I can't go into much detail. The problem involves a ...
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37 views

Detect bounding box of tables in PDF page

I am new to Machine Learning, doing courses and reading papers, and would like to solve the following problem as my learning journey: Given a PDF page I would like to detect the bounding boxes of all ...
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75 views

find outlier in 1D array

I have a satellite images, which have none values (which are not all equal to 0) on right and left side of an image and it is not a strait line. I would liked to write a program, which finds the ...
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22 views

CNN localizing object?

For classying images/objects CNNs are one possible or even the state-of-the-art solution but what if one wants to localize an object in an image? I thought if I use only convolutional layers without ...
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41 views

Help: Random Forest optimization (image classification)

I'm having trouble classifying images using a random forest. The images all have a very similar scale, but they may be rotated arbitrarily around a fixed point in the image. The core problem is ...
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0answers
16 views

how can use otsu's method?

I want to do binarization on my images. if my images have Intensity homogeneities, it means that they don't have local variation in both background and foreground I can use global thresholding. I ...
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0answers
70 views

Image recognition fails when given image not in training set

I am a beginner in Neural network. What I am trying to implement is an image recognizing tool. Neuroph Studio was used for training and creating the trained data set. A set of images of cars were ...
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0answers
26 views

Shifting input over image for CNN object detection?

I've got a CNN which is trained supervised and calculates if a specific object is present in an image. Since I am interested in the object's position in the image I tried to alter my architecture so ...
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1answer
73 views

How to prepare colored images for neural networks?

I have seen many examples online regarding the MNIST dataset, but it's all in black and white. In that case, a 2D array can be constructed where the values at each array element represent the ...
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0answers
84 views

Neural Network for Image Recognition fails to converge

I have implemented a MLP neural network, which is being used to recognise human faces. The NN's input layer has 175 neurons which represent an image (25x25 dimensions), the data for each image is ...
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1answer
46 views

Recognition of digit of size other than those in the training set using DNNs

I have a DNN trained on MNIST data (image 1 for digit '4') that recognizes images from the test set with high precision. Each digit is centered and all of them are roughly of the equal size. Will it ...
3
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2answers
83 views

Theoretical justification for bag of words

Bag of words and visual bag of words are successful machine learning approaches. Does anyone know of a theoretical justification for why / when they work? What I am trying to explain is the good ...
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0answers
10 views

What are state-of-the art scanned page segmentation techniques?

I am interested in extracting images from scanned book pages. I assume this is something Google would do for their image search, since they have scanned millions of books. After a quick look at the ...
3
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1answer
156 views

Why do we need to normalize the images before we put them into CNN?

I am not clear the reason that we normalise the image for CNN by (image - mean_image)? Thanks!
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0answers
25 views

What are good / simple techniques available for segmenting non-cursive handwriting images?

I need to process English hand-written form fields. So the hand-writings are expected to be mostly non-cursive but the letters may occasionally overlap with each other slightly, with some punctuation ...
3
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1answer
788 views

Comparing two histograms using Chi-Square distance

I want to compare two images of faces. I calculated their LBP-histograms. So now I need to compare these two histograms and get something that will tell how much these histograms are equal (0 - 100%). ...
2
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0answers
278 views

Recurrent neural network for object tracking & position filtering?

Would a recurrent neural network be appropriate for object tracking tasks? Mainly I will have 3D feature vectors $(x, y, t)$ where $x$ and $y$ are the positions of an object in the image and $t$ is ...
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1answer
79 views

Advantage of latent SVM for part-based object detection

In the famous paper Object Detection with Discriminatively Trained Part Based Models, the authors use a Latent SVM approach to learn the detector of each part, because the localization of the parts in ...