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OPENCV \ library

OPENCV \ library
OpenCV is an open source computer vision library originally developed by Intel. It is free for commercial and research use under a BSD license. The library is cross-platform, and runs on Mac OS X, Windows and Linux. This implementation is not a complete port of OpenCV. real-time capture video file import basic image treatment (brightness, contrast, threshold, …) object detection (face, body, …) blob detection Future versions will include more advanced functions such as motion analysis, object and color tracking, multiple OpenCV object instances … For more information about OpenCV visit the Open Source Computer Vision Library Intel webpage, the OpenCV Library Wiki, and the OpenCV Reference Manual (pdf). Installation instructions Optionally, you can download these OpenCV Processing examples or, for pure Java users, these OpenCV Java samples. Documentation Credits The OpenCV Processing Library is a project of the Atelier hypermédia at the École Supérieure d'Art d'Aix-en-Provence. Related:  openCVjavaComputer Vision

BlobDetection library / v3ga This library is aimed at doing computer vision by finding ‘blobs’ on an image , that is to say areas whose brightness is above or below a particular value. It allows to compute blobs’edges as well as blobs’bounding box. However, this library does not perform blob tracking, it only tries to find all blobs each frame it was fed with. It was primarly developped for Processing but can be used in any java programs. October 2012 I just updated the examples of the library to work with the latest version of Processing (2.0b3). The examples still work with Processing 1.5.1 but you have to see the instructions in the code. August 2011 Wow. September 2006 Publishing this new website. December 2005 Added computation of triangles for each blobs, allowing fast drawing of filled blobs. May 2005 Release as .jar, which can be included in Processing (BETA). June 2004 First release of an EdgeDection procedure.

OpenCV History[edit] Advance vision research by providing not only open but also optimized code for basic vision infrastructure. No more reinventing the wheel.Disseminate vision knowledge by providing a common infrastructure that developers could build on, so that code would be more readily readable and transferable.Advance vision-based commercial applications by making portable, performance-optimized code available for free—with a license that did not require to be open or free themselves. The first alpha version of OpenCV was released to the public at the IEEE Conference on Computer Vision and Pattern Recognition in 2000, and five betas were released between 2001 and 2005. The second major release of the OpenCV was on October 2009. In August 2012, support for OpenCV was taken over by a non-profit foundation,, which maintains a developer[2] and user site.[3] Applications[edit] OpenCV's application areas include: Programming language[edit] OS support[edit] Windows prerequisites[edit]

blobscanner - A blob detection library for Processing. Blobscanner is a small lightweight library for the Processing Programming Environment which can be used for blob detection and analysis in images and videos. It performs quite well, also, when the signal analyzed is a live stream. Please refer to the project blog for information about the status of this project. In the following image is visible an example of hand tracking using Blobscanner in conjunction with a skin detector algorithm. The hand center of mass can be easily computed with Blobscanner. In this video, Blobscanner is used to create a simple artistic effect on a web cam video stream. NOTE: the code repository for the new version (v. 0.1-alpha) is hosted on github If you wish you can donate to this project with Flattr or you can use Paypal on blobscanner site (here isn't allowed).

vision_opencv electric: Documentation generated on January 11, 2013 at 11:58 AMfuerte: Documentation generated on December 28, 2013 at 05:43 PMgroovy: Documentation generated on March 27, 2014 at 12:20 PM (job status).hydro: Documentation generated on March 27, 2014 at 01:33 AM (job status).indigo: Documentation generated on March 27, 2014 at 01:22 PM (job status). Documentation The vision_opencv stack provides packaging of the popular OpenCV library for ROS. For OpenCV vision_opencv provides several packages: cv_bridge: Bridge between ROS messages and OpenCV. image_geometry: Collection of methods for dealing with image and pixel geometry In order to use ROS with OpenCV, please see the cv_bridge package. As of electric, OpenCV is a system dependency. Using OpenCV in your ROS code You just need to add a dependency on opencv2 and find_package it in your CMakeLists.txt as you would for any third party package: Report an OpenCV specific Bug For issues specific to OpenCV: Tutorials

HandVu Application Documentation homepage | index | basics | common | ARtk demo | troubleshooting HandVu Application Documentation This document assumes that you have successfully installed HandVu and at least one of the sample applications on your system. Please read at least the basics and common functionality. While a lot of functionality is common to all sample applications, some key shortcuts do not work in all applications, indicated with a (*). Basics: Setup and Hand Postures Camera: HandVu is designed for a camera that views the space in front of a sitting or standing person from a top-down view. Hand detection: The hand is detected only in a standard posture and orientaentirelytion with respect to the camera, called the closed posture: recognized postures. Hand tracking: Once the hand has been detected, you can move the hand around in any posture. Posture recognition: All of the six recognized postures can be performed at any time during tracking and they will be recognized. Common Functionality GestureServer

Canny edge detector The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works. Development of the Canny algorithm[edit] Canny's aim was to discover the optimal edge detection algorithm. good detection – the algorithm should mark as many real edges in the image as possible.good localization – edges marked should be as close as possible to the edge in the real image.minimal response – a given edge in the image should only be marked once, and where possible, image noise should not create false edges. Stages of the Canny algorithm[edit] Noise reduction[edit] The image after a 5x5 Gaussian mask has been passed across each pixel. Here is an example of a 5x5 Gaussian filter, used to create the image to the right, with = 1.4. Finding the intensity gradient of the image[edit] Non-maximum suppression[edit]

image processing - hand gesture recognition in Opencv Generalised Hough transform The problem of finding the object (described with a model) in an image can be solved by finding the model's position in the image. With the Generalised Hough Transform, the problem of finding the model's position is transformed to a problem of finding the transformation's parameter that maps the model into the image. As long as we know the value of the transformation's parameter, the position of the model in the image can be determined. The original implementation of the GHT uses edge information to define a mapping from orientation of an edge point to a reference point of the shape. The main drawbacks of the GHT are its substantial computational and storage requirements that become acute when object orientation and scale have to be considered. Ballard suggested using orientation information of the edge decreasing the cost of the computation. Jump up ^ D.H.

Hand and Finger Detection using JavaCV This post is part of a series on Natural User Interfaces (NUIs) published by Dr. Andrew Davison and deals with detecting hands from a webcam video feed using JavaCV. Note: all the source code for this chapter is available for download from The colored blob detection code of chapter 5 (available at can be used as the basis of other shape analyzers, which I’ll illustrate here by extending it to detect a hand and fingers. In Figure 1, I’m wearing a black glove on my left hand. Figure 1. I utilized the HSVSelector application of chapter 5 to determine suitable HSV ranges for the black glove. Figure 2. The various stages in Figure 2 are almost identical to those carried out by the ColorRectDetector.findRect() method in section 4.1 of chapter 5. Figure 3. The hull and defects are obtained from the contour with standard OpenCV operations, which I’ll explain below. Figure 4. Figure 5. 1. Figure 6.

Face detection using HTML5, javascript, webrtc, websockets, Jetty and OpenCV Through HTML5 and the corresponding standards, modern browsers get more standarized features with every release. Most people have heard of websockets that allows you to easily setup a two way communication channel with a server, but one of the specifications that hasn't been getting much coverage is the webrtc specificiation. With the webrtc specification it will become easier to create pure HTML/Javascript real-time video/audio related applications where you can access a user's microphone or webcam and share this data with other peers on the internet. For instance you can create video conferencing software that doesn't require a plugin, create a baby monitor using your mobile phone or more easily facilitate webcasts. All using cross-browser features without the use of a single plugin. Update: in the newest versions of the webrtc spec we can also access the microphone! For this we need to take the following steps: Which tools and technologies do we use What do we use at the backend:

OpenCV 2.4 on Ubuntu 12.04 Edit (August 2013): Just used these steps to install OpenCV on Ubuntu 12.10 without problems. OpenCV 2.4.0 was released on May 2012 providing, a lot of improvements and bug fixes. For a complete change log see here. This post will hopefully make your life easier to configure, compile, install and test it out. This time I tried to categorize and explain the dependencies, so that it is easier to configure it for your needs. As always, comments and suggestions are welcome. Ubuntu 12.04 provides a package of OpenCV 2.3.1 that you can easily install by typing: sudo apt-get install libopencv-dev If you do not care about having the latest version you could skip the rest of the post. Install Dependencies Essentials These are libraries and tools required by OpenCV. sudo apt-get install build-essential checkinstall cmake pkg-config yasm Image I/O Libraries for reading and writing various image types. sudo apt-get install libtiff4-dev libjpeg-dev libjasper-dev sudo apt-get install libtbb-dev make .