计算机视觉+人体姿态识别+双目视觉

Computer vision application

院（系）电子与信息工程学院

专业集成电路工程

学生吕广兴14S158054

Computer vision application

The directory

Report: Computer vision application (2)

1.The object of the project (2)

2.The method and the principle applied to the project (2)

2.1 Platform (2)

2.2 The principle of transform the RGB image to the gray image (2)

2.3 The principle of image enhancement (2)

2.4 The principle of thresholding (3)

2.5 The principle of classifier (3)

3.The content and the result of the project (4)

3.1 The main steps in the project (4)

3.2 About human body posture recognition (4)

About three kinds of methods are most common: (4)

3.3.Stereo vision (11)

4.Reference (18)

Report: Computer vision application

1.The object of the project

The object of the project is Gesture recognition and location in the interior of people.

2.The method and the principle applied to the project

2.1 Platform

The platform is based on Visual Studio 2012 and OpenCV 2.4.10.

2.2 The principle of transform the RGB image to the gray image

There are three major methods to transform the RGB image to the gray image.

The first one is called the maximum value that is set the value of R, G, and B to the maximum of these three.

Gray=R=G=B=max(R, G, B)

The second one is called mean value which is set the value of R, G, and B to the mean value of these three.

Gray=R=G=B=(R+G+B)/3

The third one is called weighted average that is giving different weights to the R, G and B according to the importance or other indicators, and then adding the three parts together. In fact, human’s eye is very high se nsitive to green, then red, last blue.

Gray=0.30R+0.59G+0.11B

2.3 The principle of image enhancement

Image enhancement is the process of making images more useful. There are two broad categories of image enhancement techniques. The first one is spatial domain technique, and it is a direct manipulation of image pixels that includes point processing and neighborhood operations. The second one is frequency domain technique, and it is a manipulation of Fourier transform or wavelet transform of an image.

The principle of the median filter is to replace the value of a pixel by the median of the gray levels in the neighborhood of that pixel(the original value of the pixel is included in the computation of the median). It forces the points with distinct gray levels to be more like their neighbors.

In addition, we also apply the morphological image processing after smoothing. Morphological image processing (or morphology) describes a range of image processing techniques that deal with the shape (or morphology) of features in an image. The basic ideal of Morphology is to use a special structuring element to measure or extract the corresponding shape or characteristics in the input images for further image analysis and object recognition. The mathematical foundation of morphology is the set theory. There are two basic morphological operations: erosion and dilation.

2.4 The principle of thresholding

Thresholding is particularly useful for segmentation in which we want to isolate an object of interest from a background. At the same time, thresholding segmentation is usually the first step in any segmentation approach. The blow formula is the basic principle of image segmentation. When the gray level is no bigger than the threshold, we will set the pixel value zero(black). In contrast, when the gray level is bigger than the threshold, we will set the pixel value 255(white).

threshold

r threshold

r s ><==,255,0{ When it comes to the threshold, we get the value through the image histograms 2.5 The principle of classifier

The classifier is a algorithm or device that separates objects into different classes. Usually, a classifier consists of three parts. First one is the sensor, for instance, imaging device, fingerprint reader, etc...Second one is feature extractor, for example, edge detector or property descriptor. Third one is classifier which uses the extracted features for decision making or Euclidian distance, or other methods.

Features should can be regarded as the descriptors we introduced before. And the feature should be representative and useful for classification.

When it comes to the feature space, the set of all possible patterns form the feature vector. Each feature vector is a point in the so-called feature space. Similar objects yield similar measurement results. Nearby points in feature space correspond to similar objects. Distance in feature space is related to similarity. Points that belong to the same class form a cloud in feature space.

Divide the data set into a training set and a test set. The performance of a classifier should be assessed by the classification error on an independent test set. This set should not contain objects that are included in the training set. Determine a decision boundary by minimizing the classification error of the training set. Determine the classifier performance by computing the classification error of the test set.