feature-detection | 易学教程

Filled circle detection using CV2 in Python?

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问题 I am trying to detect all the circles in images like this. I have many different images like this but in all the circles will be black (or almost black) and of the same size (+/- a couple of pixels). I believe there are exactly 2943 circles in each image. These conditions never change. I may not be able to control the sizes of the circles across images (usually ranges between 15-45 pixels in radii - the example image provided above has radii of 20-21 pixels). I need to be able to detect the

Advice for algorithm choice

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问题 I have to do a project that tries to scan the shape of the vehicles and detect what type of vehicle it is , the scanning will performed with a sensors called “vehicle scanner” they are just 50 beams of lights, each beam with receptor and emission as it is shown in the picture: I get from the sensors the raw sate of each beam (block or unblock) and with that continuous scanning we can create a probably very low res image of the vehicle. My question is what algorithms/technique I can use to

OpenCV on iOS: False matching with SurfFeatureDetector and FlannBasedMatcher

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问题 I am trying to use OpenCV's feature detection tools in order to decide whether a small sample image exists in a larger scene image or not. I used the code from here as a reference (without the homography part). UIImage *sceneImage, *objectImage1; cv::Mat sceneImageMat, objectImageMat1; cv::vector<cv::KeyPoint> sceneKeypoints, objectKeypoints1; cv::Mat sceneDescriptors, objectDescriptors1; cv::SurfFeatureDetector *surfDetector; cv::FlannBasedMatcher flannMatcher; cv::vector<cv::DMatch> matches

Using document length in the Naive Bayes Classifier of NLTK Python

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问题 I am building a spam filter using the NLTK in Python. I now check for the occurances of words and use the NaiveBayesClassifier resulting in an accuracy of .98 and F measure for spam of .92 and for non-spam: 0.98. However when checking the documents in which my program errors I notice that a lot of spam that is classified as non-spam are very short messages. So I want to put the length of a document as a feature for the NaiveBayesClassifier. The problem is it now only handles binary values. Is

Fourier Transform and Fourier Descriptors to extract shapes features on Java

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问题 I am trying to build a simple system to recognize simple shapes using Fourier descriptors: I am using this implementation of Fast fourier transform on my program: (link below) http://www.wikijava.org/wiki/The_Fast_Fourier_Transform_in_Java_%28part_1%29 fft(double[] inputReal, double[] inputImag, boolean direction) inputs are: real and imag part (which are essentially x,y coordinates of boundary parameter I have) and outputs are the transformed real and imag numbers. question: How can i use

Access violation reading in FeatureDetector OpenCV 2.4.5

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问题 I tried the sample codes about matching to many images in OpenCV 2.4.5 and I modified that code. I found the error code: Unhandled exception at 0x585a7090 in testing.exe: 0xC0000005: Access violation reading location 0x00000000. Its fault is at featureDetector->detect(queryImage, queryKeypoints) . I can't find the solution from that problem. Please help me. #include <opencv2\highgui\highgui.hpp> #include <opencv2\features2d\features2d.hpp> #include <opencv2\contrib\contrib.hpp> #include

How to use Mikolajczyk's evaluation framework for feature detectors/descriptors?

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问题 I'm trying the assess the correctness of my SURF descriptor implementation with the de facto standard framework by Mikolajczyk et. al. I'm using OpenCV to detect and describe SURF features, and use the same feature positions as input to my descriptor implementation. To evaluate descriptor performance, the framework requires to evaluate detector repeatability first. Unfortunately, the repeatability test expects a list of feature positions along with ellipse parameters defining the size and

How to improve features detection in opencv

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I am working on a project that I need to detect features on images using opencv. I am using SURF detector; SURF extractor; BFMatcher matcher; for detection, extraction and matching points. It works well for some images, but fails on some other images. For example, the system fails on this image: Apparently, this image has some texture and the feature detector should detect them, but no feature is detected and consequently no match is generated. How can I improve this feature detection? Can I use any image processing technique for this? Is there any other detector that I can use which help in

Warp perspective and stitch/overlap images (C++)

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问题 I am detecting and matching features of a pair of images, using a typical detector-descriptor-matcher combination and then findHomography to produce a transformation matrix. After this, I want the two images to be overlapped (the second one ( imgTrain ) over the first one ( imgQuery ), so I warp the second image using the transformation matrix using: cv::Mat imgQuery, imgTrain; ... TRANSFORMATION_MATRIX = cv::findHomography(...) ... cv::Mat imgTrainWarped; cv::warpPerspective(imgTrain,

Detecting outliers in SURF or SIFT algorithm with OpenCV

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Which method is the best to compare two images and discard outliers points? In find_obj.cpp opencv example, they use FLANN, but don't discard outliers. I have seen some methods like using Kmeans or graphs. There is a fairly reliable and efficient way to both reject noisy points and determine the transformation between your points of interest. The algorithm that is usually used to reject outliers is known as RANSAC (http://en.wikipedia.org/wiki/RANSAC), and the algorithm used to determine the transformation can take several forms, but the most current state of the art is known as the five-point