Electronics Science Fair Project
Object Recognition using a scanner, web camera, and a VB6 computer program

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Project Information
Title: Recognize and count objects such as coins using an optical image acquisition device (scanner, web camera, etc.) and a computer analysis program written in VB6.
Subject: Robotics
Grade level: Middle School - Grades 7-9
Academic Level: Advanced
Project Type: Demonstrative
Cost: Medium
Awards: 1st place, Canada Wide Virtual Science Fair (2007)
Affiliation: Canada Wide Virtual Science Fair (VSF)
Year: 2007
Description: This project examines the potential for using a computer program and an image acquisition device to recognize and perform additional calculations ( i.e. count, sum) with 2D objects. This project was accomplished by developing a Visual Basic 6 program and testing the use of a web camera and a scanner as the image acquisition devices.
Link: www.virtualsciencefair.org...

Sub-domains of computer vision include scene reconstruction, event detection, tracking, object recognition, learning, indexing, motion estimation, and image restoration.

Object recognition in computer vision is the task of finding a given object in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems in general. David Lowe pioneered the computer vision approach to extracting and using scale-invariant SIFT features from images to perform reliable object recognition.

For any object in an image, there are many 'features' which are interesting points on the object, that can be extracted to provide a "feature" description of the object. This description extracted from a training image can then be used to identify the object when attempting to locate the object in a test image containing many other objects. It is important that the set of features extracted from the training image is robust to changes in image scale, noise, illumination and local geometric distortion, for performing reliable recognition. Lowe's patented method can robustly identify objects even among clutter and under partial occlusion because his SIFT feature descriptor is invariant to scale, orientation, affine distortion and partially invariant to illumination changes. This article presents Lowe's object recognition method in a nutshell and mentions a few competing techniques available for object recognition under clutter and partial occlusion.

In computer vision, 3D single object recognition involves recognizing and determining the pose of user-chosen 3D object in a photograph or range scan. Typically, an example of the object to be recognized is presented to a vision system in a controlled environment, and then for an arbitrary input such as a video stream, the system locates the previously presented object. This can be done either off-line, or in real-time. The algorithms for solving this problem are specialized for locating a single pre-identified object, and can be contrasted with algorithms which operate on general classes of objects, such as face recognition systems or 3D generic object recognition. Due to the low cost and ease of acquiring photographs, a significant amount of research has been devoted to 3D object recognition in photographs.

Source: Wikipedia (All text is available under the terms of the GNU Free Documentation License)

For More Information: Object Recognition

Source: Wikipedia (All text is available under the terms of the Creative Commons Attribution-ShareAlike License)

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