Develop Reference

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.

Extract similar area of two images

I wonder that extracting similar area of images is possible. if it is possible,i am going to find insigne of copmany which created it.
There are two images below. The red rectangles in the images are that i try to find area. The program is going to find similar area comparing images. I tried to find it using opencv,but i couldn't do it.

First thing in mind:
Convert images to grayscale
Divide image into small areas (patches)
Each patch should be labelled as 1 if entropy of image is high and 0 if low (to discard patches without letters)
For two images, compare all patches across images based on:
Histogram on sobel image (Bhattacharya distance is normalized)
Correlation (Minmax normalization)
Advanced descriptors (like SIFT) (L2 normalization)
Min distance wins.
You can narrow down the '1' patches with a text detector (Algorithm to detect presence of text on image).

find perceptual similarity between two images in java

I'm looking for an algorithm that can find perceptual similarity between two images, actually i want to input one picture into system and it search whole my database which contain huge amount of picture and then retrieve the images which have more perceptual similarity with source image, could any body please help me ?
I mean i want to find similar pic. i heard some algorithm can find similar pictures base on the source pic's shape, color and etc (pixel by pixel). i wanna have the system that i input the source image and system retrieve the similar images based on perceptual features like shape, color, size and etc.
Thanks

You need to define carefully what 'perceptually similar' means to you, before trying to find a measurable entity that captures that. Imagine a picture of of a grass field under a blue sky with a horse. Should your application retrieve all horse pictures? Or all pictures with green grass and a blue sky? In the latter case, the above mentioned color histograms are a good start. Alternatively you could look at gaussian mixture models (GMM), they are used quite a bit in retrieval. This code could be a starting point and this article Image retrieval using color histograms
generated by Gauss mixture vector quantization
More complicated is the so called "bag of words" or "visual words" approach. It is increasingly used for image categorization and identification. This algorithm usually starts by detecting robust points in an image, meaning that these points will survive certain image distortions. Example popular algorithms are SIFT and SURF. The region around these found points is captured with a descriptor, which could for example be a smart histogram.
In the most simple form, one can collect all data from all descriptors from all images and cluster them, for example using k-means. Every original image then has descriptors that contribute to a number of clusters. The centroids of these clusters, i.e. the visual words, can be used as a new descriptor for the image. The VLfeat website contains a nice demo of this approach, classifying the caltech 101 dataset. Also noteworthy, are results and software from Caltech itself.

One simple way to start is comparing the Color Histogram.
But the following article proposes the use of Joint Histogram instead. You may also take a look.
http://www.cs.cornell.edu/rdz/joint-histograms.html

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/