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opencv android-edge detection

I am creating a project in android ,where i need to detect the edges of the object in very intial stage.But the edges are not appropriate they are broken .I have tried otsu thresholding method for edge detection.
Now i m trying to get the intensity from histogram ,can anyone help me to figure out the mean from the histogram of an image using calchist method in opencv. Also, i am giving a thought to dividing the image into blocks of 3* 3,as then computing each block.,but enable to find how to do that .
I am using opencv library.
Thanks,
Please do respond.

Canny, sobel,prewitt and many edge detection algorithms perform edge detection.
But, as you said,they remain incapable to extract all edges.
You can use some edge linking algorithms to link those broken edges.
I used ant colony algorithm(genetic algorithm) to make the image understandable.
But there are dozens of linking algorithm you can use.
Cheers.

Object matching in OpenCV 3.1 for Java

I found out that there is Chamfer Matching available in OpenCV. I can see there is a function chamferMatching() for C++ and there seems to be a way to use it in Python, too. However, I was unable to find how to use this feature in Java. I am using OpenCV 3.1 Where can I find it in the Java interface? Is it even available there?
If not, what can I use instead? I am trying to recognize fruits. For now apples in particular. I want to match precomputed apple contour to found contours in the image (chamfer matching). After finding a possible apple I am planning to use a classifier to make sure the color and texture are correct.
Template matching seems to be a bad choice because it doesn't work with recognizing rotated objects. I am wondering if I can use Feature Descriptors. Note that I am not trying to recognize a particular apple and I don't know if Feature Descriptors are good for this.
Any thoughts?
EDIT: Ok, I decided to use the findContours() function to get all of the contours in the image, then filter them by area and compare each of the filtered contours with others, designated as templates from training, using matchShapes(). I implemented this and it is not working right (because findContours() is not detecting the apple contours) but I'll post another question with this specific problem. Here I want to ask if this way sounds ok and if there is a better way to detect and compare contours.

Ok, I figured it out. There seems to be no Chamfer Matching in OpenCV. It is implemented in JavaCV and there is no sign of it in the native code. Since I'm using OpenCV for Java it is not a good solution for me.
This answer helped me a lot. It is in C++ but it can easily be written in Java.
Initially, I am training the program using a database of 100 images of green apples. The training is actually just storing the largest contour of every photo in a file.
The key to my problem was dividing the image into the 3 different channels resulting in 3 different grayscale images. I transform them using Canny and dilate. Now I check every one of them for contours and it is very likely I will detect the contours of the apple in at least one of them. Once I have all the contours from the 3 images, I filter them by size and then comparing them with every single contour from the training data. If the contour is close enough to one of them I assume it is a contour of an apple.
There seems to be quite a lot of false positives but they will be filtered out when my coleague implements the module doing checks for color and texture of the selected contours (their content).
Here's our project's repository if it would be of help to anyone.

Scalable clipping mask

I need to to clip variablesized images into puzzle shaped pices like this(not squares): http://www.fernando.com.ar/jquery-puzzle/
I have considered the posibility of doing this with a php library like Cairo or GD, but have little to no experience with these librays, and see no immidiate soulution for creating a clipping mask dynamicaly scalable for different sized images.
I'm looking for guidance/tips on which serverside programing language to use to accomplish this task, and preferably an approach to this problem.

You can create an image using GD with the size of the puzzle piece. and then copy the full image on that image with the right cropping to get the right part of the image.
Then you can just dynamically color in every part of the piece you want to remove with a distinct color (eg #0f0) and then use imagecolorallocatealpha to make that color transparent. Do it for each piece and you have your server side image pieces.
However, if I where you I would create the clipping mask of each puzzle peace in advance in the distinct color. That would make two images per connection (one with the "circle" connecter sticking out and one where this circle connector fits into). That way you can just copy these masks onto the image to create nice edges quickly.

GD is quite complicated, I've heard very good things about Image Magick for which there is a PHP version and lots of documentation on php.net. However, not all web servers would have this installed by default.
http://www.php.net/manual/en/book.imagick.php

If you choose to do it using PHP with GD then the code here may help:
http://php.amnuts.com/index.php?do=view&id=15&file=class.imagemask.php
Essentially what you need to do with GD is to start with a mask at a particular size and then use the imagecopyresampled function to copy the mask image resource to a larger or smaller size. To see what I mean, check out the _getMaskImage method class shown at the url above. A working example of the output can be seen at:
http://php.amnuts.com/demos/image-mask/
The problem with doing it via GD, as far as I can tell, is that you need to do it a pixel at a time if you want to achieve varying opacity levels, so processing a large image could take a few seconds. With ImageMagick this may not be the case.

Object detection with a generic webcam

Here’s my task which I want to solve with as little effort as possible (preferrably with QT & C++ or Java): I want to use webcam video input to detect if there’s a (or more) crate(s) in front of the camera lens or not. The scene can change from "clear" to "there is a crate in front of the lens" and back while the cam feeds its video signal to my application. For prototype testing/ learning I have 2-3 images of the “empty” scene, and 2-3 images with one or more crates.
Do you know straightforward idea how to tackle this task? I found OpenCV, but isn't this framework too bulky for this simple task? I'm new to the field of computer vision. Is this generally a hard task or is it simple and robust to detect if there's an obstacle in front of the cam in live feeds? Your expert opinion is deeply appreciated!

Here's an approach I've heard of, which may yield some success:
Perform edge detection on your image to translate it into a black and white image, whereby edges are shown as black pixels.
Now create a histogram to record the frequency of black pixels in each vertical column of pixels in the image. The theory here is that a high frequency value in the histogram in or around one bucket is indicative of a vertical edge, which could be the edge of a crate.
You could also consider a second histogram to measure pixels on each row of the image.
Obviously this is a fairly simple approach and is highly dependent on "simple" input; i.e. plain boxes with "hard" edges against a blank background (preferable a background that contrasts heavily with the box).

You dont need a full-blown computer-vision library to detect if there is a crate or no crate in front of the camera. You can just take a snapshot and make a color-histogram (simple). To capture the snapshot take a look here:
http://msdn.microsoft.com/en-us/library/dd742882%28VS.85%29.aspx

Lots of variables here including any possible changes in ambient lighting and any other activity in the field of view. Look at implementing a Canny edge detector (which OpenCV has and also Intel Performance Primitives have as well) to look for the outline of the shape of interest. If you then kinda know where the box will be, you can perhaps sum pixels in the region of interest. If the box can appear anywhere in the field of view, this is more challenging.

This is not something you should start in Java. When I had this kind of problems I would start with Matlab (OpenCV library) or something similar, see if the solution would work there and then port it to Java.
To answer your question I did something similar by XOR-ing the 'reference' image (no crate in your case) with the current image then either work on the histogram (clustered pixels at right means large difference) or just sum the visible pixels and compare them with a threshold. XOR is not really precise but it is fast.
My point is, it took me 2hrs to install Scilab and the toolkits and write a proof of concept. It would have taken me two days in Java and if the first solution didn't work each additional algorithm (already done in Mat-/Scilab) another few hours. IMHO you are approaching the problem from the wrong angle.
If really Java/C++ are just some simple tools that don't matter then drop them and use Scilab or some other Matlab clone - prototyping and fine tuning would be much faster.

There are 2 parts involved in object detection. One is feature extraction, the other is similarity calculation. Some obvious features of the crate are geometry, edge, texture, etc...
So you can find some algorithms to extract these features from your crate image. Then comparing these features with your training sample images.

Auto scale and rotate images

Given:
two images of the same subject matter;
the images have the same resolution, colour depth, and file format;
the images differ in size and rotation; and
two lists of (x, y) co-ordinates that correlate the images.
I would like to know:
How do you transform the larger image so that it visually aligns to the second image?
(Optional.) What are the minimum number of points needed to get an accurate transformation?
(Optional.) How far apart do the points need to be to get an accurate transformation?
The transformation would need to rotate, scale, and possibly shear the larger image. Essentially, I want to create (or find) a program that does the following:
Input two images (e.g., TIFFs).
Click several anchor points on the small image.
Click the several corresponding anchor points on the large image.
Transform the large image such that it maps to the small image by aligning the anchor points.
This would help align pictures of the same stellar object. (For example, a hand-drawn picture from 1855 mapped to a photograph taken by Hubble in 2000.)
Many thanks in advance for any algorithms (preferably Java or similar pseudo-code), ideas or links to related open-source software packages.

This is called Image Registration.
Mathworks discusses this, Matlab has this ability, and more information is in the Elastix Manual.
Consider:
Open source Matlab equivalents
IRTK
IRAF
Hugin

you can use the javax.imageio or Java Advanced Imaging api's for rotating, shearing and scaling the images once you found out what you want to do with them.

For a C++ implementation (without GUI), try the old KLT (Kanade-Lucas-Tomasi) tracker.
http://www.ces.clemson.edu/~stb/klt/