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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3
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0answers
42 views

Big difference of accuracy between C-SVC and nu-SVC using SVM

I'm currently dealing with an image classification problems. The objective is to classify the images to 4 classes, with 8000 images in the training set and 14000 images to predict. I'm using the SVM ...
0
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0answers
52 views

Free space detection on image

I have set of images with front or back rear cars which was obtained by cascade detector. I need to detect free space on image, (more precisely I need just to know how big car is in pixels). ...
0
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0answers
11 views

How to set up neural network for finding text area?

I use neuroph and I want to find a text in an image. It is not even a photograph text but a computer painted java window with the same font except varying sizes/bolds or italics. My image is 300x150, ...
-2
votes
0answers
19 views

MATLAB: How to import a batch of image iteratively in a folder and export processed image iteratively? [migrated]

Here is what I need to do: All images (40,000 of them) are under a file and they are all named "roof_*.jpg"; ...
1
vote
0answers
31 views

build my own 'pickle' image dataset for CNN training

I am trying to use Python & Tensorflow to make Convolution Neural Network to classify images. I found some examples from the Udacity assignment4 for notMINST image set. Now my question is : I ...
1
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0answers
6 views

Matlab : Correct way of determining mean in post-processing of features

I need to post process the feature vectors of N = 1000 images, each of dimension m = 100 converting the real valued features to ...
0
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0answers
7 views

How should I exclude results using standard error info - Levenberg-Marquardt

I have a 3D data map of estimated physiological values in the brain. i.e., 1 value for each 'voxel'. These values are that of a parameter resulting from a non-linear curve fit, using the Levenberg-...
0
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0answers
27 views

How to normalize data for VGG-16 pretrained model?

I can't really find any data on how to go about normalizing the input for the following VGG-16 model I am using https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3 Right now I am inputting a ...
0
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0answers
12 views

Improve Chan-Vese algorithm by machine learning

Chan-Vese Algorithm is an unsupervised image segmentation method. It finds the boundary curve $C$ by minimizing the object function $$ F(c_1, c_2, C) = \mu \cdot \text{Length}(C) + \nu \cdot \text{...
0
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0answers
12 views

Are there online repositories of trained classification models? e.g. Conv-nets

I am quite new to machine learning, and I am curious as to whether other developers publish their trained classifiers (with trained weights) online for others to test out and use. I know there are a ...
0
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0answers
18 views

Dynamic Bag of Words / Features

I'm trying to implement a Bag of Features for a set of images submitted in different moments by a set of users. If the clusters change, then we need to recompute at LEAST all the "visual words" which ...
0
votes
0answers
12 views

Bag of Features / Visual Words + Locality Sensitive Hashing

PREMISE: I'm really new to Computer Vision/Image Processing and Machine Learning (luckily, I'm more expert on Information retrieval), so please be kind with this filthy peasant! :D MY APPLICATION: ...
0
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0answers
15 views

How to provide a score value to an image based on pattern information in it?

I have a say 30 two-dimensional arrays (to make things simple, although I have a very big data set) which form 30 individual images. Many of these images have similar base structure, but differ in ...
0
votes
0answers
10 views

Is normalization necessary in the first step and what is the actual block size in the algorithm?

I have been reading the Dalal Triggs HOG thesis and I am trying to understand the initial step where it takes the detection window and tries to normalize it ? I am not able to understand the need of ...
0
votes
0answers
28 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 ...
0
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0answers
26 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 ...
0
votes
1answer
40 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: ...
0
votes
1answer
37 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 ...
0
votes
0answers
29 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 ...
5
votes
1answer
64 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 ...
0
votes
1answer
24 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 ...
1
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0answers
34 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 ...
0
votes
0answers
54 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 pre-...
0
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1answer
51 views

Convolutional Neural Network for 3D point cloud?

Can Convolutional Neural Networks or Deep Architectures be used for generating 3D point clouds ?
0
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0answers
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 ...
0
votes
0answers
13 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 (usually)...
3
votes
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. ...
1
vote
0answers
61 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] ...
0
votes
1answer
56 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 ...
0
votes
1answer
26 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 ...
0
votes
0answers
40 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 ...
1
vote
1answer
26 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 ...
0
votes
0answers
26 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 ...
0
votes
0answers
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 ...
0
votes
0answers
108 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 ...
1
vote
0answers
45 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 ...
2
votes
1answer
216 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 ...
0
votes
1answer
57 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 ...
1
vote
1answer
232 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: ...
2
votes
3answers
72 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 ...
0
votes
0answers
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 ...
1
vote
0answers
49 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 ...
0
votes
2answers
44 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 ...
0
votes
1answer
66 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?
4
votes
0answers
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 ...
0
votes
1answer
43 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 ...
1
vote
0answers
45 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 ...
0
votes
0answers
88 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 ...
1
vote
0answers
24 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 (...
1
vote
0answers
52 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 ...