Computer Vision Case Study

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1. INTRODUCTION
1.1 Overview
What is computer vision?
Vision is the task of ”see”. It is seeing with understanding other than seeing with camera. When we ”see” things, our eyes (sensing device) capture the image, then pass the information to brain (interpreting device). The brain interprets the image, gives us meanings of what we see. Similarly, in computer vision the camera serves as a sensing device, and the computer acts as an interpreting device to interpret the image the camera captures.
Computer vision relates to many areas, including biology, psychology, information, engineering, physics, math’s, and of course computer science [9].
1.2 Motivation
Question: How many kinds of objects there are in this world?
Answer: It is still an open
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in images or videos. The task of object detection can be defined as: Given an input image, determine if there are objects of a given class (e.g. People, cars.) in the image and where they are located [3]. As an example, it might be easy to train a domestic helper robot to recognize the presence of coffee machine with nothing else in the image. On the other hand, imagine the difficulty of such robot in detecting the machine on a kitchen slab that is cluttered by other utensils, gadgets, tools, etc. The searching or recognition process in such scenario is very difficult. So far, no effective solution has been found for this problem [4]. Object detection and recognition are important problems in computer vision. Since these problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real-time, and dynamic object detection/recognition methods are still unavailable. The accuracy level of any algorithm or even Google glass project is below 16% for over 22,000 object categories. With this accuracy, it’s practically unusable…show more content…
Content-based image retrieval (CBIR)- When the retrieval is based on the image content, it is referred as CBIR. A supervised learning system, called OntoPic, which provides an automated keyword annotation for images and content–based image retrieval is presented.
1.5.2. Industrial inspection- Parts of machinery can be recognized using object recognition and can be monitored for malfunctioning or damage.
1.5.3. Surveillance- Objects can be recognized and tracked for various video surveillance systems. Object recognition is required so that the suspected person or vehicle, for example be tracked.
1.5.4. Robotic- The research of autonomous robots is one of the most important issues in recent years. The humanoid robot soccer competition is very popular. The robot soccer players rely on their vision systems very heavily when they are in the unpredictable and dynamic environments. The vision system can help the robot to collect various environment information such as the terminal data to finish the functions of robot localization, robot tactic, barrier avoiding, etc. It can decrease the computing efforts, to recognize the critical objects in the contest field by object features which can be obtained easily by object recognition

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