K. B. Neelima Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.107-109 www.ijera.com 107 | Page Image Detection and Count Using Open Computer Vision (Opencv) K. B. Neelima 1 , Dr. T. Saravanan 2 1 Research Scholar, Assistant Professor. 2 Professor & HOD, Bharath University, Chennai. ABSTRACT The purpose of this paper is to introduce and quickly make a reader to provide basics of OpenCV (Open Source Computer Vision) without having to go through the lengthy reference manuals and books. OpenCV is actually an open source library for image and video analysis, originally introduced more than decade ago by Intel. The latest major change took place in 2009 (OpenCV2) which includes main changes to the C++ interface. Nowadays the library has >2500 optimized algorithms. It is extensively used around the world, having >2.5M downloads and >40K people in the user group. Regardless of whether one is a novice C++ programmer or a professional software developer, unaware of OpenCV, the content should be interesting mainly for the researchers and graduate students in image processing and computer vision areas. Keywords: OpenCV, image processing, computer vision, calibration, face detection. I. INTRODUCTION OpenCV means Intel Open Source Computer Vision Library. It is a collection of C functions and a few C++ classesuses to implement the Image Processing and Computer Vision algorithms. The Key features about it are Cross-Platform API of C functions FREE for commercial and non-commercial uses. This means the user can take advantage of high speed implementations of functions commonly used in Computer Vision/Image Processing. OpenCV was designed for computational efficiency and with a strong focus on real time applications. OpenCV can be written in optimized C and takes the advantage of multicore processors. OpenCV automatically uses the appropriate integrated Performance Primitives (IPP) library at runtime. One of OpenCV’s goals is to provide a simple infrastructure of computer vision to help people for build the fairly sophisticated vision applications quickly. The OpenCV library contains over 500 functions that span many areas in vision, including factory product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. II. DOWNLOADING AND INSTALLING OPENCV The OpenCV site is on the Source Forge at http://SourceForge.net/projects/opencvlibrary and the OpenCV Wiki [OpenCV Wiki] page is at http://opencvlibrary.SourceForge.net . For Linux, the source distribution is the fi le opencv-1.0.0.tar.gz; for Windows, you want OpenCV 1.0.exe. However, the most up to date version is always on the CVS server at Source Forge. Once installed anyone can download the libraries and then install them. In the text file named INSTALL directly under the .../opencv/ directory, the installation instruction on Linux or Mac OS are availed and also describes how to build and run the OpenCV testing routines. INSTALL lists the additional programs you’ll need in order to become an OpenCV developer, such as automake, autoconf, libtool, and swig. Windows-Get the executable installation from Source Forge and run it. It will install OpenCV, register Direct Show filters, and perform various post-installation procedures. The system is ready to start OpenCV. You can always go to the .../opencv/_make directory and openopencv.sln with MSVC++ or MSVC.NET 2005, or open opencv.dsw with lower versions of MSVC++ and build debug versions or rebuild release versions of the library. To add the commercial IPP performance optimizations to Windows, obtain and install IPP from the Intel site (http://www.intel.com/soft ware/products/ipp/index.htm );use version 5.1 or later. Make sure the appropriate binary folder (e.g., c:/program fi les/intel/ipp/5.1/ia32/bin) is in the system path. III. OPENCV STRUCTURE AND CONTENT OpenCV is broadly structured into five main components, four of which are shown in Figure 1.0. The CV component contains the basic image processing and higher-level computer vision algorithms; ML is the machine learning library, which includes many statistical classifiers and clustering tools. For storing and loading video and images, the HighGUI contains I/O routines and functions and CXCore contains the basic data structures and content. RESEARCH ARTICLE OPEN ACCESS