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while developing simple algorithm for object detection in a scene with matlab tool box
here is the code I wrote
http://imgur.com/QVFD0yP
then I found these errors
http://imgur.com/N6BKzG8
please can anybody help
The command detectSURFFeatures() takes a 2-D (grayscale) image as input. Your first image, box_image, is a color image. Convert it to grayscale by using the command rgb2gray() and use to detect SURF features.
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my project is to count White fly in an image using matlab, I'm new to image processing so I don't know where to start from , I searched for papers about the topic but I could't find anything useful , my question is to help me start from a point and if you can suggest some papers can help me , thanks .
There is a lot of research in this area, but not specific to Matlab. Try using google scholar to search for papers with "computer vision counting" or similar keywords.
Richard Szeliski also has a very good, free to download textbook on Computer Vision which could be helpful: http://szeliski.org/Book/. Topics like Feature Detection, Segmentation and Recognition might be useful, depending on your exact problem.
When you have an idea of what you want to do, have a look at Matlab's Computer Vision toolbox: http://au.mathworks.com/help/vision/index.html
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Basically I need to extract the identification number of marathon runners from the image. Till now I was able to get the bib part alone from the whole image. Now I need to extract the numbers from that image:
I need to extract 1430 from the image. I have tried some methods like OCR and blob detection techniques but they are not successfull for all images.
Have you tried using Stroke Width Transform (SWT)? You can find a Matlab implementation of the first stages of SWT here.
Take a look at this example in the Computer Vision System Toolbox.
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I have tried manual detection using LS polynomial fitting here. But that cannot be used in my project as mine has to be a fully automated system.
Take a look at the Scale-Invariant Feature Transform, or SIFT. This video explains it well. You "train" a detector with one or more images of eyelids, and the detector locates similar regions in the input images. It's the de facto general purpose feature detector - although more specialized tools like face detectors are faster.
The "Scale-Invariant" part means that it can detect the same object at different sizes and rotations.
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I am developing an app in which I need to put my iPhone on any surface and detect the taps on that surface. Please provide me any link or idea regarding that.
Thanks.
See sample code out there for using the accelerometer to detect movement.
Then test varying amounts of detected movement and determine what you think is "tap".
As a suggestion - you could use the accelerometer in the phone to detect vibrations. I'm not sure if it will be accurate enough for you, and you will need to do some processing of the results to filter for the correct type of vibration.
This isn't simple, so if you were hoping for some code or library to do this I don't think you'll find what you need.
If you do manage to do it, you could open source your implementation and help the next person who comes along.
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I am searching for segmenting gray matter from a T1 weighted brain MRI scan. But I could not get the correct tutorial to follow it. Please suggest me an algorithm that works better and accurately to segment the gray matter alone from the T1 wieghted MRI scan image. There are several tools to segment gray matter in matlab but I need algorithm to segment the gray matter. Please suggest me the algorithm.
Why reinvent the wheel? SPM does a good job of segmentation and the MATLAB source code is freely available: http://www.fil.ion.ucl.ac.uk/spm/
You can examine the algorithm that is used and customize it for your own purposes if you wish. It produces probabilistic maps of gray matter, white matter, and csf that you can use in subsequent analyses. There are also a variety of options to complete the segmentation in both normalized and native space. I highly recommend it as a place to get started, and then you can branch off from there depending on your needs.