opencv object detection

Cvcanny function of Image edge detection--OPENCV Category: C/ void Cvcanny (const cvarr* image, cvarr* edges, double threshold1, double threshold2, int aperture_size=3); Image single-channel input images. Edges the output image of a single-channel storage edge threshold1 The first threshold threshold2 the second threshold Aperture_sizesobel operator kernel size (see Cvsobel). The function Cvcanny uses the C

threshold value. This point is the corner point.To avoid calculating eigenvalues (time-consuming), use the following formula instead:The determinant of the Det matrix, traces of the trace matrix. It has been proved that the value of this equation is higher when two eigenvalues are high. The parameter k is generally set to 0.05~0.5.Harris Corner detection, can be improved by maximal value inhibition, that is, to ensure that the strength of the corner

Tags: des style blog HTTP color OS ar use Feature Points, also known as points of interest and key points, are highlighted and representative points in the image. Through these points, we can identify images, perform image registration, and perform 3D reconstruction. This article mainly introduces several functions in opencv to locate and represent key points. I. Harris corner A corner is the most basic key point in an image. It is composed of s

Document directory 1.1 cvkan 1.2 cvCreateTrackbar 1.3 CvTrackbarCallback [OpenCV Getting Started Guide] Part 3 "Canny edge detection" The principle of image edge detection is to detect all the gray-scale points in the image, and these points are connected to form several lines, which can be called the edge of the image. A multi-level edge

Image algorithm Test IteratoralgorithmfeaturesOriginal articles, reproduced please specify the source: http://blog.csdn.net/crzy_sparrow/article/details/7391511Article directory:First, Harris Angle point detection basic theorySecond, OPENCV code implementationThird, improved Harris corner point detectionFour, fast corner point detectionV. ReferencesVi. Appendices (information and source code)First, Harris C

In the previous article, "OpenCV feature2d learning--surf and SIFT operators to achieve feature point detection", the use of SIFT and surf operators for feature point detection, here is trying to use fast operator for feature point detection.Fast's full name is:Features from Accelerated Segment test, the main feature values are fast, much faster than other known

The realization of vehicle detection and tracking based on OpenCV Http://www.cnblogs.com/yanneu/p/6251892.html Recently, the teacher assigned a job, is to do a video-based vehicle detection and tracking, with about two weeks of time to do a simple, the effect is not ideal, but can not suppress some of their own knowledge to write down, here to put some blog on t

resources on the Internet to help you understand this great edge detection algorithm. Threshold selection, to try to choose the low threshold!!! Because if the threshold selection is too high, it causes the outer quadrilateral of the invoice to be unclosed, which prevents the contour line from being found correctly. Although the low threshold value produces a lot of noise, the noise will be ignored in subsequent steps because contour

"OpenCV Getting Started" chapter III Canny edge detection The image edge detection principle is to detect the image of all the gray value changes in the larger point, and these points are connected to form a number of lines, these lines can be called the edge of the image. The canny edge detection operator is a multi-

the output image variable space, it is required to output the image variable is an initialization, the size and type are already initialized good variablesNot_draw_single_points: Feature points of a single point are not plottedDraw_rich_keypoints: Drawing feature points when drawing a circle with a direction, this method simultaneously displays the image coordinates, size, and direction, is the most can display the characteristic information one kind of drawing way.1#include 2#include 3#include

I haven't written a blog for a long time, and my student's career ends. I will not summarize it. Today, I will record the Adaboost training and detection process in opencv, so that it is convenient for others ~~~ Ah, haha ~~~~ I. Basic Knowledge preparation First, opencv currently only supports training and detection

characteristic point optical flow field.Advantage: In the ideal case, it can detect the independent motion of the object, do not need to know the scene in advance any information, can be very accurate to calculate the speed of the moving object, and can be used in the case of camera motionDisadvantage: The calculation method of the global optical flow field is large, and the noise, multi-source, shadow and

Recently began to learn OPENCV, want to detect the ring on the picture, found that Hough transform can do such an effect, so try to use the Opencv3 Hough transform to do the next ring detection. OpenCV in the Hough transform function: The first parameter, the Inputarray type of image, is the 8-bit grayscale single-channel image, which is the source image.The se

write in front of the articleThese days because the work needs to learn image detection, stupid I do not want to stare at OPENCV start to learn (; ′⌒ '), even the ability to check information is weak 〒▽〒Praise My best man ticket (*^▽^*) The man is not the image processing but love my stupid (the "contest")Let me give him the request (our store?? Omega??) Our store??? He put the game down and spent a day or

Hough Line transformation can only be the edge binary image.4. The specific principles of the Hough Line detection refer to:52944708Http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.htmlhttp://lib.csdn.net/article/opencv/24201The code is as follows:#Line Detection#using Hough linear transformation to do straight

Common Methods for moving object detection include optical flow, background subtraction, and frame difference ). The Background Subtraction Method and the interframe difference method are suitable for static cameras, while the optical flow method is used for camera motion, but the calculation is relatively large. The following describes how to use the cvupdatemotionhistory function in

Original address: opencv for iOS Study Notes (6)-mark detection 3 Precise marking position // Adjust the posture of the tag Based on the camera rotation. // marker: the captured tag STD: Rotate (marker. points. begin (), marker. points. begin () + 4-nrotations, marker. points. end ()); After the tags are captured and filtered Based on the tag encoding, we should redefine their corners. This step helps est

, and the method of integrating graph can accelerate the solution of the value of the class Haar feature.One of the most basic class Haar features is a rudimentary weak classifier, which is optimized after the weak classifier is called the optimized weak classifier.A strong classifier is formed by combining multiple optimized weak classifiers.But the application of a single strong classifier is not good in actual detection,So it was proposed to cascad

", cv_window_autosize); -Imshow ("Input Image", SRC); - - Mat DST, Dst_gray; +Resize (Src,dst,size ( -, -));//Change Size - + Cvtcolor (dst,dst_gray,color_bgr2gray); A atHogdescriptor Detector (Size ( -, -), Size ( -, -), Size (8,8), Size (8,8),9); -vectorfloat> descriptors;//histogram vector -Vectorlocations; -Detector.compute (Dst_gray, Descriptors,size (0,0), Size (0,0), locations); -printf"Number of HOG descriptors:%d", Descriptors.size ()); - inWaitkey (0); - return 0; to}Pedestrian

*argv[]) { Charfilenamestring[ -]; Charwindownamestring[ -]; Charresultfilenamesring[ -]; Mat Srcimage,grayimage,blurimage,thresholdimage,gradientximage,gradientyimage,gradientimage,morphimage; for(intFileCount =1; FileCount 8; filecount++) {sprintf (filenamestring,"f:\\opencv\\ barcode Detection and identification \\barcode_0%d.jpg", FileCount); sprintf (windownamestring,"result 0%d", FileCount); sp