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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7 views

Spatial prediction using glm variable AGB into a brick raster image [on hold]

I have those columns, whom they calls AGB and LINEAR. I would like to predict the Column AGB in glm models, because it improve my R² model. I have my data set, it is in *.csv format. I did these ...
0
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1answer
20 views

Dimensionality reduction of small vectors (image processing)

I have N small floating point vectors of length K (typically, N is in the millions and K=9). I need to compute a lot (millions and millions) of squared euclidean distances between those vectors. It ...
0
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0answers
19 views

Classify pixels in image

I'm working in a solution to classify the pixels of an image. The objective is to separate the foreground from the background. I'm using a supervised learning method, so foreground and background ...
0
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0answers
22 views

Bag of Features: why the distance between two histograms of the same image is different than 0?

I'm trying to implement a Content Based Image Retrieval application for small image datasets. I'm testing it just with 1 thousands images from Caltech1001. The approach that I'm using is the classic ...
2
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2answers
50 views

What does it mean an histogram vector normalization with L1/L2 norms?

I was reading these slides about Bag of Features (BoF). At slide 23 you can read: each image is represented by a vector, typically 1000-4000 dimension, normalization with L1/L2 norm What does ...
0
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2answers
18 views

Can we use Bag of Visual Words to compute similarity between images directly?

I'm implementing a Content Based Image Retrieval application (CBIR). I've read about the Bag of Features model and it's considered an intermediate-step algorithm in some application. For example, ...
1
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0answers
27 views

Will doing image cropping excessively lead to poor performance of deep learning model?

I am currently building a deep learning model to recognize images. From what I have read, data augmentation such as random cropping of images will lead to less overfitting of the model. However, I am ...
0
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0answers
19 views

Character Concatenations using ANN

I am new to machine learning, I want to do character concatenation/joining via Neural Networks. Please suggest me, how to approach to the problem? I propose I could simple provide image of un-...
3
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0answers
78 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
62 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
14 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
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0answers
71 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 ...
0
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0answers
8 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
54 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
13 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{...
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0answers
16 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
22 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
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0answers
20 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: ...
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0answers
19 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
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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
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0answers
30 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
29 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
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1answer
53 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
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1answer
48 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
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0answers
33 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 ...
6
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1answer
134 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
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1answer
37 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
35 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
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0answers
67 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
73 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
13 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
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0answers
14 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
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0answers
33 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
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0answers
73 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
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1answer
59 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
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1answer
27 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
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0answers
54 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
29 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
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0answers
29 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
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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
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0answers
154 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
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0answers
50 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
292 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
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1answer
60 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
361 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
80 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
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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
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0answers
55 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
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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
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1answer
78 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?