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Image Data Analysis - Science topic

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I have over 50 datasets in a single forest plot. I am having some trouble in adjusting the font size and colors when designing high resolution forest plots? Any tutorials and remedies?
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same problem , incomplete display the whole picture?
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To get a rough estimate of the depth accuracy of a stereo camera system, I want to use the formula shown in the attached picture. What I am not sure about is what is usually taken as the assumption for the disparity error delta_d. Is it 1 pixel? A certain fraction of a pixel? Why?
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Dear Victor Sluiter i found the formula in https://people.inf.ethz.ch/pomarc/pubs/GallupCVPR08.pdf
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I want to evaluate a mass segmentation algorithm. how wan I generate the MiniMIAS database ground truth???
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If the ground truth images are not available publically, the images should be manually annotated by or under the supervision of an expert.
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In machine learning based image classification , how to extract features from image dataset. I want to classify images of different shapes, i have database for each  shape, now what the next step i should follow for classification.
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There are many methods. If you are using Python you can see this also it provides many useful functions :
With openCV you can see :
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Visual data is becoming increasingly used in qualitative research ranging from participant created art (e.g., drawings, photos) to pop culture text (e.g., film, tv, advertisements). What approaches have you found most meaningful, useful, and/or appropriate?
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You will need a Computer-assisted qualitative data analysis software.
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I have some mammogram images which are too large. How can I reduce size of images into 8 bits?
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You can read this article:
Mammogram Image Size Reduction Using 16-8 bit Conversion Technique
Regards
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Dear all, 
currently, I am working on content wise image classification, Can you please specify me about image recognition algorithm?
Thanks,
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Some of the algorithms used in image recognition (Object Recognition, Face Recognition) are SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), PCA (Principal Component Analysis), and LDA (Linear Discriminant Analysis).
Regards
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I have downloaded BRATS 2015 training data set inc. ground truth for my project of Brain tumor segmentation in MRI. A file in .mha format contains T1C, T2 modalities with the OT. Please suggest how to access these files in MathWorks (MATLAB) and further how to proceed for segmentation procedure?
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I am developing an adaptive thresholding method based on integral sum images and sauvola method. I need a dataset to test my method against the ground truth. any help is appreciated.
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i hope content of this link can be helpful for your task!!
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I have image(attached) with certain overlapping, i want to segment it with watershed algorithm using distance transform but not able to achieve it. Steps followed: distance transform on binary image,then dilation and opening and finally watershed.
kindly suggest
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Anu Singh The only problem with scikit-image watershed segmentation is that if you don't provide accurate markers it over-segments the image. To get fairly accurate markers you can use SNOW algorithm in PoreSpy module in Github. It uses scikit image watershed segmentation but with correctly identified markers. Also the package is open source and fairly easy to learn.
Link:
I am attaching binarized version of you image that I used to perform watershed segmentation using SNOW algorithm. The final watershed image and a simple code script is also attached. Let me know if you have further any issue.
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Hello,
Does anyone know of methods (preferably using Fiji) to assess quantitative data to analyze double positive cells. So I have figures from FITC/TRITC/DAPI channels and not all of my signals overlap and I would like to have a method to show that certain percentage of cells are overlapping.
Are there any plugins or other tools besides FIJI to assess that?
Any help would be greatly appreciated.
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There are some good options out there.
Here's a good description of the concepts, and it lists a few plugins.
I read this paper a while back but I haven't tried the plugin.
There are also several videos on YouTube that take you step-by-step.
Have a very specific type of output you are looking for. For example, plugins like Coloc 2 don't give you information about how well your images/channels align with each other. Whereas, EZcolocalization may give you more of a per-cell-colocalization.
I hope that helps. Good luck!
- Melissa
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I am looking for a publically available video/image dataset in indoor spaces like stores or malls. I came across Multiple Object Tracking (MOT) Dataset. However, I am looking for something more specific.
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You can look for Caviar dataset. They have a dataset of a shopping mall. May be of your help ( Clips from Shopping Center in Portugal (2nd Set) ):
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i want to train a knn classifier to segment an image into four classes. i train the classifier with four classes.but when testing the network I get only two classes. what could cause this error?
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Yes, straight forward in R, no problem.
It is a classification based algorithm, and deals with many levels of Factor variables.
All the best.
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I have started my research on the topic detection of glaucoma using image processing techniques. I need more colour fundus image dataset for training and testing tne neural network. Where can I find them?
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For finding of homorgraphy transformation matrix, I have intrinsic paramters of camera and latitude and longitude angles of two images and I have to find rotation matrix to find homopgralhy transformation matrix?
How can I find rotation matrix from latitude and longitude angles of two matrix? 
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Dr Darshana Mistry Hello, I am wondering if you ever found a method for calculating a cameras extrinsic parameters solely based on latitude and longitude of the camera?
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Does anyone have a fuzzy method to solve mixed pixel in crop (sugarcane/rice) classification for district level area?
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yes, first you have to check your comailed database.
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I am staining lung tissue with H&E stain for a school project and have to quantify inflammation using ImageJ. Does anyone know how to do that or have literature that explains it? Thank you in advance.
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Could anyone share the answers please.
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I am working on LANDSAT 7 surface reflectance (16 bit signed) image there is problem like SRC. This is a common problem in LANDSAT 7 (8bit signed) which will be solved by focal annalyisis tool in ERDAS but when i am dealing with LANDSAT 7 surface reflectance (16 bit signed) its not working.
May i get any solution to solve the SRC.
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Hope you are doing good and you resolved this two year back problem.
I am also facing the same problem, have you resolved this problem ? If you, can you please help me out.
Thank You!
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Mean, Standard Deviation, Root Mean Square, Mean Absolute, Skewness are used for calculating the feature vector?
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Pixel differences for detectin variations in different image scales
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I am right now working on 1-D local binary pattern of an EEG signal. After getting the local binary pattern of the signal we should extract histogram features. please help me how to extract them using matlab.
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%function [ LBP ] = LBP( I2) [f,p] = uigetfile('*.jpg;*.bmp'); I = imread([p f]); I = imresize(I,[256 256]); cform = makecform('srgb2lab'); lab = applycform(I,cform); ll = lab(:,:,1); I2=ll; m=size(I2,1); n=size(I2,2); for i=2:m-1 for j=2:n-1 c=I2(i,j); I3(i-1,j-1)=I2(i-1,j-1)>c; I3(i-1,j)=I2(i-1,j)>c; I3(i-1,j+1)=I2(i-1,j+1)>c; I3(i,j+1)=I2(i,j+1)>c; I3(i+1,j+1)=I2(i+1,j+1)>c; I3(i+1,j)=I2(i+1,j)>c; I3(i+1,j-1)=I2(i+1,j-1)>c; I3(i,j-1)=I2(i,j-1)>c; LBP(i,j)=I3(i-1,j-1)*2^7+I3(i-1,j)*2^6+I3(i-1,j+1)*2^5+I3(i,j+1)*2^4+I3(i+1,j+1)*2^3+I3(i+1,j)*2^2+I3(i+1,j-1)*2^1+I3(i,j-1)*2^0; end end figure(); imshow(LBP);
how do I extract lbp feature vector after doing the above code and what feature vector are we getting ? so that I can feed this feature vector for neural classification.
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I've been working using RTKLIB for first time and unsure regarding on combining the data file. For example, I want to combine three different days data for processing. I've tried using wildcard and it didn't work.
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Hi Ainul Husna Abdul Rahman and Jareer Mohammed , apologizes for writing here; I know this conversion 2 years ago, but I have a similar question:
I am using RTKLIB as well to process GNSS data, I need to download a reference station called "camo320" from this link ftp://ordnancesurvey.co.uk/gps/rinex/2018/320/ . This station is close to my current location.
I do Not need to pat attention to what hour the files are,I've been told that I just need to download them all from that day (16/11/2018), use the wildcard, and RTKpost will automatically grab the files it needs from that folder (I do not know how to do it). I downloaded each file individually and put them all in one folder, then I uploaded them from RTKLIB. pos (select all), but this does not work.
Could anyone please explain to me clearly how can I download all files from the specific day (16/11/2018)?
Many thanks in advance,
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Can anyone suggest me how to write a Mean Shift code for image segmentation in MATLAB?
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till Its not working
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I want to take a deeper look at the facial expressions of people with profound intellectual and multiple disabilities (PIMD) to analyse their emotional expressions.
For this purpose, I am searching for a ready-to-use software, which combines image processing (e.g., OpenPose, OpenFace) and machine learning. In addition, I would prefer a software that is free (i.e., Open Source) or at least for non-commercial research purposes.
So, I am not looking for methods, but for ready-to-use software, which includes a feature to train my own models. The reason is that every person with PIMD shows very unique behaviour signals and, therefore, you need one model for each emotion of a person.
Finally, I do not need a GUI or visualization, a simple command line application would enough.
A hint would be very helpful.
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I do not know if there is a "ready-made" software available for this, but my collaborators at University of Basel, GRAVIS group in the Department of mathematics and informatics have been working on facial recognition software.
Their project that comes close to what you seek is "Social judgement of faces" lead by Sandro Schönborn.
You can visit their webpage here:
The software they use - scalismo - is opensource.
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I have two stationery time series that I want to see are they correlated or not. I decided to work with cross correlation, there is a good answer in Correlation between two time series but I dont know how to calculate p-value(as http://stats.stackexchange.com/users/11032/michael-chernick said) and decide that its a good answer, if there are correlated do something. I dont know how to do this. I want to code it with C++ and use it for simulation in Omnet++ . Could someone help me to find a way or introduce a document to me to understand the mathematical way to calculate it and program it in C++? P.s : My data are the number of packets which send from two nodes to an OLT in a network continuously. something like this:
from time 0 to 10:
series 1 : 2 5 3 7 9 2 0 1 8 4
series 2: 3 4 6 0 5 9 2 5 3 7 . series 2 has 6 min delay. or maybe there is no delay in other case, I must calculate the delay also (I can calculate delay with omnet++ but I dont know are they correlated at the end or not)
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Did you figure out how to calculate the p value for cross-correlations?
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I can't find images for this topic. Is there a website with these images?
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Hi!
You can download image fusion dataset from below links
Keith A. Johnson, J. Alex Becker, “ http://www.med.harvard.edu/aanlib/home.html”, The whole brain atlas data set
Regards
Thilak Babu
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I want to compare different facial expression recognition approaches on a (not yet) publically available dataset.
For this purpose I need at least one software that I can apply on my dataset videos out-of-the-box. A software based on a state-of-the-art method would be perfect. I also would prefer software that is free, at least for non-commercial research purposes.
I am not looking for methods, but for ready-to-use software including the trained recognition models.
Update: I do not need a fancy GUI or visualization, a simple command line application which reads an video file and writes a text file with FACS scores for each of the frames would be most helpful.
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We also published our code and trained model with our (winning) contribution to the ICCV 2017 Real Versus Fake Expressed Emotion Challenge, see https://github.com/fsaxen/NIT-ICCV17Challenge
The method is descibed in P. Werner, F. Saxen, and A. Al-Hamadi,
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Suppose I have a dicom file with pixel size (0.5,0.5), and I want to make the pixel size to (0.7,0.7) or vice versa. How can I change/modify the pixel size of a dicom file?
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i tried to rectify 3 uncalibrated images (i dont have any data about the image no  matrix ) of an object than from this rectification i got the MI(mutual information) for all images
bit now i would like to extract 3d image from those data, i tried to use triangulation but i got a flat image like 2D image
any could help please
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It doesn't and cannot work without any prior knowledge:
  1. The imaging equation of the lens (could be omitted if it's the 'normal focus' type).
  2. Not knowing the sensor's resolution (pixels/mm) will result in some scaling error - a minor issue.
  3. You need the 3D coordinates of the camera positions from where the images were taken. (If using a rotating table: just rotate the camera virtually by the x degrees the table is rotated between the images.
Remark: It would clearly help to show us your images and the resulting model. Might be some systematic error (e.g. in the setup).
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In the FBP algorithm, the ramp filter |w| has the zero response for the 0-frequency, thus its output has zero for the DC component, i.e., the average value would be zero, then, there would be negative values to compensate the common positive values. Since the nagative values are introduced by the ramp filtering and the image is positive generally, how to perform the normalization before the quantitative evaluation against the reference image? 
Thanks.
This question is related with my previous one:
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The DC component can be recovered based on the "volume conservation property" of the Radon transform. Please refer to:
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Hi,
I have an image processing question. I will greatly appreciate you if you help me out how I can count the number of pixels in a known range of RGB values in an image in Matlab?
for example in the attached picture, how can I count the number of pixels in the leaves?
Thanks,
Mojtaba
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I imagine that you have a range R_c = [min:max] where c is red, green and blue. Now for each image channel (R,G,B), you binarize the image such that pixels are set to one when they are within the range:
R_bin =  (R >= MinG) & (R <= MaxG);
G_bin =  (G >= MinG) & (R <= MaxG);
B_bin = ( (B >= MinB) & (R <= MaxB));
Now, with "and" operation, you can obtain a binary image which is one when all the pixels are within the required range:
bin = R_bin & G_bin & B_bin;
by summing this up, you obtain the number of pixels
N = sum(bin(:));
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I am trying to code an image forgery detector proposed in a scientific paper. This approach uses Mean Squared Error inconsistency between an image and its re-interpolated version using a set of known Color Filter Arrays (CFA). However, I don't know how to do the first step of the algorithm, which is re-sampling an image with a CFA pattern (they have 4 available). Please take a look in the cfa.png image and cfa2.png image. Can you explain to me how to use the CFA patterns to resample an image as described? My first thought was: it seems that these CFA patterns can be used as kernels for convolution. But convolution normally involves image intensities but in the cfa1.png image, they talk about locations (x,y). Can you help me to understand how this is performed? 
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Hello, Lambert.
First of all, I am not the author of this publication. I am just trying to understand it so I can code it (I am using it as a baseline for an approach I am developing). The authors are from the NYU and you can find the paper at http://isis.poly.edu/~forensics/pubs/dirik_icip09.pdf
Second, you are right. The authors say that strong compression will make the approach fail. They just don't define the exact value of a 'strong compression'.
And yes, this is really an arms race, like everything we do in digital image forensics :-)
Thank you so much for your comments.
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I'm trying to analyse images of animal tissues produced with a 3DHistech slide scanner. I usually use ImageJ but have problems fitting large enough images in the memory. Are there any other free alternatives, or tricks that could be used with ImageJ? My analysis is simple, just detecting & measuring circular holes of certain size (adipocytes). The images are like 25000 pixels x 25000 pixels.
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QuPath Open source software for digital pathology
Orbit Image Analysis
Both software are easier than matlab. There cut images into tiles automatically.
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Dear researchers,
i am working on disparity estimation methods. I am using sum of squared difference methods.The method is applied on  two images with different views.I got the disparity map. I got a disparity map with zeros ,ones and two's.My asumption is that region covered with 0's in map shows similarity and other regions with 1's and 2 shows dissimilarity.This is my assumption.I would like to measure the similarity in the map in terms of number of 1's covering the regions.
My point is that my methodology is correct or not? please advice me in this regard?
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Dear Venkata,
Your question is too vague to be able to assemble a meaningful answer. Please provide more details on your problem and in particular address the following points:
- What is the origin of your stereo images (ground-based, airborne or space-borne) and what is their spatial resolution?
- What is the nature of your target, and is it flat or rough (i.e., if it is a land surface, does it include topography)?
- Have these images been co-registered so that pixel (i, j) in both images correspond to the same point on the target?
- What are the format and mathematical nature of your stereo images (Black and White, RGB or multi-spectral; binary, integer, floating point)?
- What method did you use to generate the 0/1/2 values included in your 'similarity.xlsx' file? Specifically, the sum of squared differences between the values retrieved from two images would yield a single number, so I don't understand how you could map that...
- Also, what is the intended meaning of the scores 0, 1, and 2?
Once your problem and your approach are better understood, it may be possible to provide some guidance about how to proceed. Cheers, Michel.
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Hello,
I am working on Ultrasound images and calculating gray level co-occurence matrix (GLCM) and gray level difference matrix (GLDM) at four orientations(0,45,90,135 degree). Here, suppose we find Contrast at 0 ,45,90,135. Now, is there any specific reason why we need to average the Contrast of all the directions and take it as only one feature or we can take it as four different features. And is the averging of feaure has to be done in both the cases i.e. GLDM and GLCM. Please guide.
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Hello,
The reason is to prevent a directional-bias. However it is not mandatory, I personally have used both forms.
Regards.
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how to convert 3d hyperspectral dataset to 2d dataset without loosing data in matlab
for dimension reduction
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You must use Reshape command to unfold your cube of hyperspectral data;
Example:
suppose you have Indiana Pine Dataset 145*145*220
you can convert by following function:
im=indiana pine (145*145*220)
function v = ToVector(im)
% takes MxNx3 picture and returns (MN)x3 vector
sz = size(im);
v = reshape(im, [prod(sz(1:2)) sz(3)]);
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As part of PhD work, I have been working on content based image retrieval systems, which are based on image feature extraction using local patterns. What are other possible research areas, where feature extraction methods can be employed. 
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According to me one of the best possible research area currently is to identify emoitons out of some facial features. There is hardware tool called kinect through which you can start your basic research work under this area 
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I'm working on diagnosis of eye diseases using image segmentation. I need papers or work previously done in that area
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I am currently working on a psychology research project which uses a dual-video task comprised of anxiety-provoking and positive videos to be shown side-by-side. I really want to try and match up the videos as much as possible by perceptual characteristics. For example, sizes of objects on screen, colours, textures, etc. Does anyone know of an algorithm, program, or app which could be used for this purpose?
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Dear Joanna, my advise is to look at papers of Moncef Gabbouj's group in Tampere University of Technology. They deal with content based image retrieval and search for similar video. Best regards, Vladimir. 
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1) For each key point, I took a 5x5 neighborhood in the detected key point scale and computed magnitude and orientation of every point in 5x5 neighborhood.Now I used the 36 bin histogram and assigned the peak orientation as the orientation of the key point.
I've just stored the peak orientations of key points in an array. How will this array useful in the later stages ?
2) Now, for every key point I took a 16x16 neighborhood and sub-divided into 16 4x4 grids. For each grid I created a 8 bin histogram , creating a 128 descriptor for each key point.
How will this descriptor is robust to orientation ?Please explain this point with some intuition.
Are the above steps correct ?   Did I miss any step in between ?
I've computed the same steps for the 2nd image.
Now, Can anyone explain how to match the key points ? with some intuition behind .
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Generally scanned image's resolution is 2000+ in width. And maybe it's size is larger than 5 MB which greatly impacted in website's page load & speed in worse manner. So, I want to introduce a feature that can reduce image's resolution to 800px by compromising image quality but not too worse.
Any suggestion to this work? 
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Thank You Prof. @Stephane Breton for introducing me to new technology foundation Discrete Fractional Fourier Transform (DFFT) with various paper recommendations.
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Please provide me some useful materials to have a basic understanding on any Image Registration based application
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Hi,
Image registration can take different forms depending on the domain it is applied to. For example, it is quite common to see research papers discussing the best approaches to register multi-modality scans (e.g., CT and PET scan images, rigid and non-rigid MR and CT registration, etc.). In microbiology, image registration can be helpful in visualising the response of cells under several conditions. In 3D reconstruction, in light microscopy, we did a work where we optimised the post-alignment values of tomographic projections for fine tuning 3D sectioning.
A good start for you to read could be the below slides.
 
Regards,
Abbas
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Image processing and neural network
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Hi Mahmood,
As our colleagues say in their previous answers, it depends on if you are looking for "modality propagation" algorithms and the actual application of the synthesized pseudo-CTs. I would recommend to take a look at these papers as well:
I hope this helps.
Good luck!
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Can anyone suggest me the standard interpretation values (Tables) for PSNR (Peak to Signal Noise Ratio) and Kappa coefficient (Accuracy Assessment parameters) for Image Processing to check whether the value generated as the outcome of the proposed module falls within the acceptable range or not?
@ 2D Image @Tabular representation @ Intervals
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Hi Rebika,
I have used PSNR quite extensively to check for the amount of distortions introduced to images (quality measurement), but I am not aware of any standard table in conjunction with image processing. I, however, came across several research papers that mention an acceptable PSNR level of 40 dB or above.
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Spectral matting is a method permits the segmentation of  the foreground taking into consideration all the details of it.  
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Not at all.
I'am waiting you'r remarks. 
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Hello all,
I'm trying to develop an effective reporting method for surface roughness of colony biofilms. Currently, I only have simple 2-D images available from a scanner, but would be open to suggestions for better imaging methods as well. I've attached a representative image for the kinds of colony biofilms I'm dealing with. These images are representative of the kinds of differences I'd like to describe quantitatively: http://imgur.com/a/G90AC
Thanks for taking the time to read!
-S
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I have found that macro imaging is very important.  See the following articles for related discussion:
A focused review is offered by Boltz et al. (2017). From biofilm ecology to reactors: a state-of-the-art review.
Innovative techniques, sensors, and approaches for imaging biofilms at different scales. Authors: Thomas Neu and John Lawrence
Assessing the influence of biofilm surface roughness on mass transfer by combining optical coherence tomography and two-dimensional modeling
Authors: Chunyan Li; Michael Wagner; Susanne Lackner; Harald Horn
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Let's say we find SIFT key-points from the gray-scale image (average of the RGB image) but we have calculated several feature images (with the same size as the original image), and we want to calculate SIFT descriptors for all those, from the same image locations. That is we do not want to end up with different number and location of points for each feature image. I could not do this at least in the vl-feat implementation. Is there any implementation (preferably in Matlab) that accepts input for key-point locations to extract SIFT descriptors? 
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Then, you should test the dense sift descriptors which come with color variants. Try vl_phow, there are 3 color variants tacking into account different colorspaces.
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Dear all,
I want to calibrate the coordinates of real word to image. I am unable to get idea how to proceed. 
I need to identify the height of moving object in video considering static background  as in the reference image and check it if is same as in real world. My camera is also fixed at a particular height 
Please suggest .
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Hi
If the scene has a known height of the reference and the background is static, the simple approach are, for example: Approach-1. Known height of the person, or, Approach-2. known height of four white boxes. The actual height of object can be roughly calculated within image by the pixels that occupied by the object.
LSW
Below images are the concept of Approach 1 and 2.
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I want to classify subjects using their normalized iris images.
What are the best image features for this purpose? 
thanks 
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SVM classifier works well for face classification. i even do for eye classification. 
kindly see my paper. 
my observation is it is important to select a best classifier and at the same time we also have to see what feature extractor we are using. in my paper i used Eigen and Fisher feature extraction and SVM classifier. 
it is best suited for face classification and also eye classification. 
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Using HoG transform i obtained feature vector for each image, now  how to classify these images using Sklearn classification algorithm(Knn) using obtained feature vector?? 
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same thing!
you have to take some images which have circle shapes in it. similarly you do it for all shapes. you can take the features of these images. for example HoG features. give the feature vector to the classifier in the training phase. tool for Knn is available.
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Basically SSIM and PSNR are used mainly to compare reconstructed images from the original image but in the literarture these two criterions are frequently used to compare the segmented images from the original image.
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If the thresholding intent is a segmentation then SSIM makes not much sense to me, in such a case I would treat it as a detector and use TP, FP, F1-score and similar measures. If a method depends on some attribute You can consider to plot ROC curve and compute AUC.
If the thresholding is used to re-quantize the image (see the multilevel version of Otsu's method) then the image quality assessment method make sense. SSIM, multiscale SSIM, FSIM, and numerous others.
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I am doing the vein recognition and localization work.I found that it is difficult to distinguish the capillary and the veins,especially the blur veins.I use the frangi filter to extract the veins, but the dark thin capillary become thick after the filter. Then, my algorithm is confused to identify which is the thick vein.I want to recognize the capillary, so that I can do some judgment about the thickness of the veins.  Anyone can give some advise?
The image below is captured by a common industrial camera with infrared filter under the infrared light. 
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Not sure whether the following papers could be of interest:
R. Su, C. Sun, C. Zhang, and T. D. Pham, A New Method for Linear Feature and Junction Enhancement in 2D Images based on Morphological Operation, Oriented Anisotropic Gaussian Function and Hessian Information, Pattern Recognition, 47(10):3193-3208, October 2014.  http://doi.org/10.1016/j.patcog.2014.04.024
R. Lagerstrom, C. Sun, and P. Vallotton, Boundary Extraction of Linear Features Using Dual Paths through Gradient Profiles, Pattern Recognition Letters, 29(12):1753-1757, September 2008.  http://vision-cdc.csiro.au/changs/doc/lagerstrom-etal08.pdf
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Hi
How to calculate confident level in computer vision. I work on object detection and for that purpose detected relevant features. I work on airplane door detection, so I have some relevant features such as, door window, door handle, text boxes, Door frame lines and so on.  Firstly , detect individual features, then in the second level and done some logical organisation of those features where eliminate the wrong detected features.And the end I have some final checks where should remain only features that belong to that object. So my question is with which confident level I can declare that this is the object I like to detect. Any help
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My previous post focused on computer stereo-vision. But in a single image feature detector context, I suggest that you check for the following paper by Meer et al., "Edge Detection with Embedded Confidence", 2001 ( http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.2946&rep=rep1&type=pdf ).
Miller et al. introduced a confidence measure of interest that they integrated into gradient based edge detectors.
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hi, everyone i am working on script identification from multi-lingual document image, i need to know which is the best feature extraction method to extract features from grayscale document image.  
thanking you,
With regards,
SATISH KUMAR 
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You need to identify what are the key differences between the scripts. 
As for an example the second script seems to have lots of horizontal lines, compared to the other two. so you can use hough transform (may be to detect lines) to discriminate it. 
As an overall idea I would say 
SIFT,SURF, Histogram of oriented gradients are good feature extraction mechanisms. 
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I have an image with green background and some shadows around my materials and I want to remove background and shadows then convert RGB color to lab color and get mean values of l*, a*, b* values separately to use these values into total color change (delta E). 
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thanks I've solved my problem.
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Hi, 
   i am working on a camera based document image analysis, where i have to identify the script from multi-lingual document images, hence i need to extract the features using  alexnet is it suitable to extract the document image features. 
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Thank you Luay Shihab, for valuable information regarding the CNN, yes the CNN is one of the important field in deep learning and it has got lots of pretrained CNNs i knew the concept but i need to implement the CNN on my dataset. My objective is to identify the script from different document images for that i have created the dataset of 24,000 images of having 6 classes, from this dataset i need to extract the features and want train them using SVM multiclass. i need some detailed implementation information for CNN. 
with regards,
SATISH KUMAR 
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The original data are time series of x and y coordinates which describe the displacement of an object or a subject on field. The data were collected with an semi automatic tracking system at 30 fps. After that it was applied a 6Hz Butterworth low pass filter.
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Hi Pedro, There is no way of "not adding" noise when performing any operation using inputs carrying already noise in them (unless using 2 correlated inputs). In fact we are not "adding noise" as it already comes from the XY coordinates displacement data. If the 6Hz cut-off low-pass filter is not enough to remove the higher frequency noise of the displacement trajectories I would try a lower frequency cut-off. 
If not feasible or if the result is not the desired one I would try, for instance, a 2D Savistky-Golay polynomial smoothing to the raw data (30Hz) prior to calculate the speed (actually  you can apply directly the 1st derivative SG smoothing). I have the SG implemented in Excel (just need to find it).
Best, LG
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What does it mean to get empty matrix when applying HOG transform on an image?
I am working on a segmentation task and aim to use hog descriptor for pixels of an image . Applying transform, I get empty matrix for some windows. What does it mean?
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you said, "I get empty matrix for some windows".
That can happen for blank patches (ie, filled with the same color), then image gradients will be zero.
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I have a few thousand brain MRI:s (incl. 3d-T1, FLAIR, SWI and others) in  512*512 matrix. I need to resize these to 256*256. Mango performs well, but its hardly practical to convert images one at a time. What would be the easiest way to batch resize?
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Glad I could help :)
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What are the fields of application for high resolution (> =256*256 pixels) face detection ?
The most common application is in a surveillance system. It may be used to check identification-card or in similar case.
But is there any other application? 
I found only three relevant paper, I attached links of them. But they are not exactly what I am looking for. 
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Face detection  has a lot of applications, but  I am considering High Resolution  Face Detection. 
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The images are a obtained from a video sequence captured from my camera.
I am planning to implement the algorithm in opencv c++.  I dont prefer Machine Learning approaches.
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Thank you  Hossein Soleimani Nilesh B Bahadure  David Dunkerley..
I have implemented the sum of laplacian . It seems sufficient for my dataset.
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Hi everyone, 
I have a question about the relation between CNN kernel size and how far a feature map can be shifted. 
Given three consecutive 7x7 kernels, which equal to one 11 kernel, a feature map can be shifted 11 pixels using the three consecutive 7x7 kernels? 
Hope to hear from you.
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Thank you Amin Ullah, i got it.
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can any one explain me the features that can be extracted from fonts?
some are based pixels.some are based curvature...
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If no rotation invariance is required, HoG features in combination with e.g. a pre trained SVM is suitable.
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for fire detection using digital image processing
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See Fire Information for Resource Management System (FIRMS) https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/about-firms.
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I am trying to create a machine learning model to classify cervical cancer images. These are 2-D RGB images. Is there an image processing library designed to preprocess these images and pin point the location of a predefined feature in the image itself.
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OpenCV, in combination with C++ or Python could be an option.
By preprocessing you usually remove information.
What can be done is to shift information from
the overall image to a local place. What that
means depends on you specific problem. If ethics
does not forbid it, could you please post a
sample image, or one painted by yourself?
Regards,
Joachim
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I work in visual speech recognition, after extract the mouth region and perform 2d DCT ,I want to extract the 24 highest energy coefficients of DCT using matlab.
how can I identify the highest energy location in the dct matrix?
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Do you need the values or the indexes as weel?
If you need only the values you can convert the 2D array into a 1D array.
--SingleVector = Matrix(:);
Then, you can use the sort function.
--[sortedX,sortingIndices] = sort(SingleVector ,'descend');
sortedX will be in descending order. Therefore, the first N elements will be the N maximum values.
--maxValues = sortedX(1:N);
You could calculate the 2D indexes using the sortingIndices.
Looking forward to hearing from your comments.
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Hello,
To find occlusion similarity, I have to add structured/block noise in image. How can I add this noise in an image using opencv?
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Hello,
I'm working with ImageJ on analyzing MRI images of Na+ in soil and roots. I have a calibration image  - the whiter the image is, there is higher concentration of Na+. I want to be capable of analyzing an image and to estimate Na+ concentration in the soil and in/around the root. Thanks, Adi
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I am facing a problem with thresholding while I am using Imagej. I am analyzing 2160 images as one stack. I usually divide them as 3 groups (720 images / each group) but this time I got three different values for threshloding (converting images to Black and white). Is any one has any idea why I got this?
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you want to find threshold for each images 
or 
for all 720 images once 
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Hello Dear all,
I am working on edge detection, I must firstly apply a smoothing filter to the image before applying other processing. The problem is the mask chosen gives good results in some images and bad results in others, if I change the mask I get the opposite. What is the image property that measures the noise? In this way, I can change the mask automatically depending on the image noise.
Thank you 
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Thank you dear Atif for your reply, but I want to measure the noise with a single image, for this reason, I can not use PSNR because I don't have the second image with which I can compare.
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Once Haar classifier detects something in an image, i am trying to calculate the average intensity of that region. And compare that region avg. intensity with the intensity of a region where human is present. I am not sure whether this will work or not.
Or should i try passing adaptive thresholded image to the classifier?
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I will try that in my algorithm...
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Hi, can anyone give the clarification of groundtruth image or dataset. Actually what it is and how the ground truth database has been creating. 
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The observed color at any point on an object is influenced by several factors like  the shape and material of the object, the positions and colors of the light sources, and the position of the viewer.
Ground truth is a common terminology that is widely used in various fields to basically refer to any kind of information provided by direct observation.
In Remote Sensing, Ground truth is the information or data collected on site so that the input data (image) can be related to the actual features and is considered to be much correct and appropriate than the input features. This process compares the pixel on a satellite image to what is there in reality (at the present time) in order to verify the contents of the pixel on the image. Further, Error of Commission and Omission can be calculated for accuracy assessment.
Kindly refer to the paper attached where the researchers has tried creating the ground truth dataset for image algorithms (Ground Truth1.pdf) and the second pdf is a chapter on the same.
Hope it will help u in some way.
Regards,
Rebika Rai
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Wavelet transform use to human identification through walking style 
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hello sir,
    In  any researach  it is the work of scholar to investigate and prove which method/tool /algorithm is suitable for a particular appication ,Hence it is better to do experimental investigation on all wavelets and infer about suitability. It is theme of yr work itself. all the best and try all wavelets ....
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I need to have the size of blob fixed and not affected much by illumination..
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Because of the continuous change in illumination. 
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Image processing
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In graphic processing there are a great many different protocols for representing a pixel, more than 80 competing graphic standards existed at one time like vectors and rasters, and still are found occasionally in histories. If you intend to save the image in a particular file format, then I suggest you look up the protocol of that format and adapt your problem to agree with the intended file type.
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I am working on digital image classification. given an image, i need to classify whether it is a cg image or natural image?
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You can use Natural Scene Statistics (NSS). Images from natural scenes exhibit regular statistics behaviors which are not present in CG images, and can be estimated for classifying. Check this article, and other works by Alan Bovik and his team.