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again will a short review with a chart
As the algorithms?


The following chart summarizes the accomplishment of the steps of the algorithm:

HSV Color System = Hue Saturation Value
HSV separated color, brightness and the concentration of the hue .

The original image for each video frame using a color histogram model of the tracked one color Color probability image converted.

By CAMSHIFT algorithm, the size and the center of the color object are found, which is based on the color probability image. The blue dashed

box includes the steps durläuft the mean shift algorithm. In order to set

the position and size in the next video frame, the current position and the current size of the tracked object is passed and used.

This process is repeated.

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feature extraction

CAMSHIFT and mean-shift algorithm in OpenCV OpenCV

used for tracking the CAMSHIFT algorithm. The advantage is that you can also use lower-cost webcams. Such a algorithm that work efficiently and quickly and track in real time, can not exploit the resources of the PC so that other applications can run alongside.
The mean shift algorithm is based on a robust parameter-free technique for growing Dichtheitsgradienten to the summit of the probability distribution to find.

We want a procedure for the distribution of colors found in a video scene.

For this reason, the mean shift algorithm has been modified to make it with dynamically changing color probability distributions to work, coming from the video image sequence. This modified algorithm is the Continuously Adaptive Mean Shift Algorithm (CAMSHIFT).

operation of the algorithms:
mean shift algorithm: first
Setting a search window size
second Set the start position of the search window
third Calculate the center position in the search window
4th Center the search window at this middle position
5th Repeat steps 3 and 4 until convergence (or up to a given threshold
)

The mean shift algorithm works with probability distributions in order to tracking colored objects in video sequences. The color image data must be probability distributions are presented. Color histograms are used to accomplish this.

CAMSHIFT algorithm: first
Set the start position of the second search window
Application of mean shift (see above), Save the zeroth moment
third Equating the search window size to a function of the zeroth moment, which was found in step 2
.
4th Repeat the 2nd and 3 Step up to an agreement ("zeroth moment".
distribution area below the search window window radius, height and width, in the
function used zeroth moment)

The CamShiftAlgorithmus track that X, Y and areas of skin color probability distribution. The Area is proportional to Z, the distance to the camera.

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review master project, MPT and OpenCV with HandVU

A review of the master project to us was available, MPT and OpenCV have with HandVU I decided to use OpenCV with HandVu for the remaining solutions, since these adequately between the three projects at the most robust and our project is best is. can

In the master project still images used to work but we need an application that moving images.
MPT would also use an application that could be. But after some testing I've found with the OpenCV HandVU better because HandVU specifically for the recognition of the Hands is suitable.

Also, I've downloaded a Pongspiel and tested. There are several variations of Pongspiel.

I will try next an interface between the Pongspiel and produce with OpenCV HandVU.

Once this is done, you could try out other projects to take things out for our application in order to optimize these then.

the way I try also to deal with the theory, so I will soon also make it purely a post.

more information about OpenCV & HandVU you can see from the blog of Mrs Demir.

more links to download the programs:

http://www.movesinstitute.org/ ~ Kolsch / HandVu / HandVu.html # download

http: / / sourceforge.net / project / showfiles.php? group_id = 22 870 = 16 937 & package_id

http://sourceforge.net/project/showfiles.php?group_id=124401

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image segmentation

for the hand of all the other things, such as filter, for example, background, etc. can be taken to segment the hand first.

There are various methods for image segmentation that can be applied.
The boundaries between these processes is often blurred. Therefore, one can combine them to achieve better and efficient results.

Pixel-oriented method

The pixel-oriented methods, criteria used to segment allocation only to the pixels, independently of other pixels.

Procedure: Schwelwertverfahren

edge oriented method

The edge-oriented method is searched for edges and contours, which are combined to object boundaries.

methods: Sobel operator, Laplace operators, watershed transformation, Snakes

region-oriented method

The region-based methods are considered contiguous sets of points as a whole.

methods: region growing, split and merge, Pyramid Linking, Region Merging

model-based method is

The model-based methods based on concrete knowledge about the pictures, that is a model.

Procedure: Hough transform, statistical models, template matching

texture-based method

in texture-based method for a texture, ie, after a homogeneous internal structure, rather than segmented according to a uniform color.

Procedure: cooccurrence matrix (Haralick matrices),
texture energy measures (Texture Energy Measure),
Lauflängenmatrizen (Run-Length Matrix),
fractal dimensions and measures,
Markov Random Fields and Gibbs potentials,
structural approaches, theoretical concepts
signal

These methods are sometimes on the borderline for classification or allow simultaneous segmentation and classification.