computer vision book

I. Overview of mask generation At the end of the previous section, we have obtained the classification and regression information of the image to be detected. we extract the regression information (that is, the border information of the target to be detected) separately, and combine the pyramid feature mrcnn_feature_maps, generate a mask. # Detections # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in # normalized coordinates

Python Computer Vision: Chapter 1 Image Processing basics, python Image ProcessingChapter 1 basics of image processing

after filtering, divided into M regions.application II. TrackingThe target tracking algorithm based on mean shift is used to calculate the eigenvalue probability of the pixels in the target region and the candidate region, and then the target model and the candidate model are described, then the similarity function is applied to measure the similarities between the initial frame target model and the current frame candidate region. Select the candidate model with the maximal similarity function

Chen Yu Si Yuan[Http://yuhuazou.sinaapp.com] FoundA good academic conference calendar siteHttp://www.confsearch.orgYou can also use the embedded framework (embeddedIFRAME) integrated into your own web page, easy to use. Some computer vision-related meetings were selected here, which were updated from time to time, The csdn blog is really a weakness and cannot be embedded. If you want to learn more, please

To protect the vision of the revolution, especially for office workers in front of the computer, pay attention to the following points: The distance from the eye to the screen is kept above 60 centimeters. The farther the better, if you can't see the screen, increase the font. The vertical position of the screen is between 15 degrees and 50 degrees under the eye horizontal line. It is not only good fo

: flannbasedmatcher to optimize the training process and create an index tree for the descriptor, this operation will play a huge role in matching a large amount of data (for example, searching for matching images in a data set of hundreds of images ). Brute-force matcher does not operate in this process, but stores train descriptors in the memory.Sample Code #include Lab results References FLANN project HomepageFLANN manual PDFLearning opencv -- SURF (feature points) FLANNOpencv documentation

criteria for the underlying meanShift());Termcriteria template class This class is used as the termination condition of the iteration algorithm. Its constructor requires three parameters:One is type, the second parameter is the maximum number of iterations, And the last parameter is a specific threshold. TermCriteria(int type, int maxCount, double epsilon); The types include cv_termcrit_iter, cv_termcrit_eps, cv_termcrit_iter + cv_termcrit_eps, which indicate that the iteration termination co

rewrite the above code -img = Cv2.imread ('Beauti.jpeg', Cv2. Imread_grayscale) - PrintImg.shape - #Img[0][0] = 0 in PrintImg.item (0,0) - Img.itemset ((0,0), 0) toCv2.imwrite ('Mypic-gray.png', IMG) + - #Remove the green channel theimg = Cv2.imread ('Beauti.jpeg') *img[:,:,1] =0 $Cv2.imwrite ('No_green.png', IMG)Panax Notoginseng PrintImg.shape,img.size,img.dtype - theimg = Cv2.imread ('Beauti.jpeg') + #To display a picture, you must enter two parameters ACv2.imshow ('My Image', IMG) the #Wi

()Mask =numpy.uint8 (Numpy.ones (gray.shape)) keypoints=S.detect (Gray, mask)#displaying images and feature pointsVis =Cv2.cvtcolor (Gray, Cv2. COLOR_GRAY2BGR) forKinchKeypoints[::10]: cv2.circle (Vis, (int (k.pt[0)), int (k.pt[1]), 2, (0, 255, 0), 1) cv2.circle (Vis, (int (k.pt[0)), int (k.pt[1])), int (k.size), (0, 255, 0), 2) Cv2.imshow ('Local descriptors', Vis) Cv2.waitkey () Cv2.imwrite ('c:/users/public/pictures/sample pictures/koala2.jpg', Vis)②duang~ jumping out of a picture like this

that the human eye is not sensitive to the image due to insufficient illumination.　　McCann AlgorithmThis algorithm is intended to produce a better estimate of the uneven illumination, the extraction of the control information is no longer the weighted mode of the Gaussian convolution, but the intensity of a spiral is selected to be weighted, compared with the Gaussian weighting so that the greater range of illumination information can be obtained, and the specified number of iterations based on

fromMatplotlib Import Pyplot aspltimg=cv2.imread ("1.jpg") Gray=Cv2.cvtcolor (Img,cv2. Color_bgr2gray) #颜色转为灰度ret, Thresh=cv2.threshold (Gray,0,255, Cv2. thresh_binary_inv+Cv2. Thresh_otsu) #可为图像设一个阈值kernel=np.ones ((3,3), np.uint8) opening=cv2.morphologyex (Thresh,cv2. morph_open,kernel,iterations=2) #去除噪声sure_bg=cv2.dilate (opening,kernel,iterations=3) Dist_transform=cv2.distancetransform (Opening,cv2. DIST_L2,5) #可以通过distanceTransform来获取确定的前景区域. That is, this is the most likely foreground ar

region or any region that is created by the merge is added. Iii. Diversification StrategiesThe author gives two strategies for diversification: color space diversification, similar diversification. Color Space DiversityThe authors used different color methods in 8, mainly to consider scenes and lighting conditions. This strategy is mainly applied to the generation of the original region in the image segmentation algorithm in "1". The main color space used are: (1) RGB, (2) Grayscale I, (3

Computer Vision (Computer Vision, CV) is a science that studies how to make machines "look". The first doctoral thesis in the field, published by Larry Roberts, MIT, in 1963, "Machine perception of three-dimensional solids," marks the beginning of the study of CV as a new AI direction. Today, after more than 50 years o

dense map, and in this case you need to consider what the minimum map resolution requirement is for the user to see. Because the existing mobile phone or flat on the 3D camera resolution is generally VGA (640x480), this case rebuilt the 3D model is not photorealistic that effect. And without considering the time and cost of computing, a high precision, high-precision, calibrated high-end lidar+ HD camera system should be able to meet a very good experience. Another example of a game company to

This blog attempts to use some of the most intuitive, image, examples of the way to explain the relevant concepts For learners of a photogrammetry (photogrammetry) or three-dimensional computer vision (3D computer vision), the first thing to contact is the conversion between various coordinate systems, and the Photogr

http://blog.csdn.net/zhangyingchengqi/article/details/50969064First, machine learning1. Includes nearly 400 datasets of different sizes and types for classification, regression, clustering, and referral system tasks. The data set list is located at:http://archive.ics.uci.edu/ml/2. Kaggle datasets, Kagle data sets for various competitionsHttps://www.kaggle.com/competitions3.Second, computer vision"Machine le

Security monitoring system won the National Computer software copyright registration Source: Huaqing Vision Research and Development Center January 7, 2016, by huaqing Foresight Research and development of "intelligent security monitoring System V1.0" won the National Computer software Copyright registration certificate. The system is widely used in embedded te

,frame=Camera.read () Gray=Cv2.cvtcolor (Frame,cv2. Color_bgr2gray) faces=face_cascade.detectmultiscale (Gray,1.3,5) for(X,Y,W,H)inchfaces:img=cv2.rectangle (frame, (x, y), (x+w,y+h), (255,0,0),2) F=cv2.resize (Gray[y:y+h,x:x+w], ( $, $)) Cv2.imwrite ('./DATA/%S.PGM'%Str (count), F) Count+=1Cv2.imshow ("Camera", frame)if(Cv2.waitkey (int( +/ A))) (0xFF==ord ("Q")): Breakcamera.release () cv2.destroyallwindows ( )if__name__=="__main__": Generate ()2. Human Face recognitionOpe

as mkdir SHAREVM under/home3 command: sudo mount-t vboxsf SHARE/HOME/SHAREVM can be sharedThird, installation CCV1 git clone https://github.com/liuliu/ccv.git2 git checkout stable # switch to stable branch3 Downloading some dependent librariessudo apt-get install clang libjpeg-dev libpng-dev libfftw3-dev libgsl0-dev libblas-dev liblinear-dev Libblas-dev 4 cd Lib./configure Force5 CD. /binMake6 You can see a lot of executable files in the bin after make is finished.Iv. using ICF DPM for pedestri

Intersection of all line segments-primary articles tags (space-delimited): Computer vision, Graphic science Intersection of all line segments-primary articles Reference: "Computational geometry-Algorithms and Applications" Deng Junhui translation Tsinghua University Press This paper presents a small example of how to compute the intersection point of all line segments without considering the use of po