lbp 6200d

Image features, image texture, image frequency domain and other angles to extract the characteristics of the image.LBP, local two-valued model, local feature description operator, has strong texture feature description ability, has illumination invariance and rotational invariance. Using Python for a simple LBP algorithm experiment:1 fromSkimageImportData,io2 ImportMatplot.pyplot as Plt3 ImportCv24 fromSkimage.featureImportLocal_binary_pattern5Image

first, the original LBP algorithm The basic idea of LBP is to sum up the results of the pixels of the image and the pixels around it. This pixel is used as the center to compare the threshold values of adjacent pixels. If the center pixel's brightness is greater than or equal to his neighboring pixel, mark him as 1, otherwise mark it as 0. You will use binary numbers to denote each pixel, such as 11001111.

Local binary pattern (LBP) is a very important feature in the field of machine vision. LBP can effectively handle illumination changes and is widely used in texture analysis and texture recognition.The LBP algorithm is very simple, simply speaking, is to compare the grayscale value of a pixel in the image with the gray value of the pixel in its neighborhood, as s

In this paper, the extraction of LBP features of the face, using the round operator of LBP, through the identification of the samples in the ORL92112 face database, according to statistics, the training set and test sets of the accuracy rate reached 100%; The image after LBP processing is shown in the following illustration: As shown in the above illustration, t

From: LBP (local binary mode) partial two value mode _ month to see the sunrise _ Sina Blog Http://blog.sina.com.cn/s/blog_6e93dc1901010r15.html LBP (local binary mode) partial two value mode (2012-03-15-10:38:17) Reprint Tags: it classification: Learning LBP (local Binary patterns, locally two-valued mode) is an operator that describes the local texture characte

LBP is a simple and effective feature extraction algorithm for texture classification. The LBP operator was presented in 1996 by Ojala and others, and the main thesis is "Multiresolution gray-scale and rotation invariant texture with the local Binary patterns ", Pami, vol., No.7, July 2002. LBP is the abbreviation of "Local binary pattern". The local two-value pa

Based on the characteristics of hep (histograms of equivalent patterns "1"), which has good texture classification effect, LBP (local binary patterns "2") is the most commonly used feature under the HEP Framework and has a brightness, Rotation and other good invariant properties. In the block-based video smoke detection, it is often used as a feature of texture classification. However, the image of a block is localized. This article mainly proposes th

Original: http://blog.csdn.net/dujian996099665/article/details/8886576 first, the original LBP algorithm The basic idea of LBP is to sum the pixels of an image with the contrast of its local surrounding pixels. This pixel is used as the center to compare the threshold values of neighboring pixels. If the luminance of the center pixel is greater than or equal to his neighboring pixels, mark him as 1, otherwi

Original: http://www.cnblogs.com/mikewolf2002/p/3438698.html In this chapter we study the principles and use of LBP images, because next we will use the histogram of LBP images for face recognition. Resources: Http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html Http://www.cnblogs.com/mikewolf2002/p/3438166.html The basic idea of LBP is to c

1 ImportCv22 ImportNumPy as NP3 4 5 defOLBP (src):6DST = Np.zeros (src.shape,dtype=Src.dtype)7 forIinchRange (1,src.shape[0]-1):8 forJinchRange (1,src.shape[1]-1):9 PassTenCenter =src[i][j] oneCode =0; aCode |= (src[i-1][j-1] >= center) ; -Code |= (src[i-1][j] >= center) ; -Code |= (src[i-1][j+1] >= center) ; theCode |= (src[i][j+1] >= center) ; -Code |= (src[i+1][j+1] >= center) ; -Code |= (src[i+1][j] >= center) ; -Code |= (src[i+1][j-1] >= center) ; +Code |=

Description: Here briefly introduces a variety of feature extraction algorithms, follow-up. In the pattern recognition, the identification is based on the image characteristics when the matching recognition or classifier classification is identified. The extracted features are used to represent the whole image content, match or classify the image target according to the feature. Common feature extraction algorithms are divided into the following 3 categories: ① based on color characteristics: su

In this paper, a new method of human detection based on depth maps from 3D sensor Kinect is proposed. First, the pixel filtering and context filtering is employed to roughly repair defects on the depth map due to Informatio n inaccuracy captured by Kinect. Second, a dataset consisting of depth maps with various indoor human poses are constructed as benchmark. Finally, by introducing Kirsch mask and three-value codes to local Binary pattern, a novel local ternary Direction pattern (LTDP) feature

OPENCV provides two programs that can train their own cascading classifiers opencv_haartraining and Opencv_traincascade. Opencv_traincascade is a new program that is written in C + + using the OpenCV 2.x API. The main difference is that Opencv_traincascade supports both Haar and LBP (local Binary Patterns), and it is easy to add other features. Compared with the Haar feature, LBP features are integer featur