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is enough for Billy's position), and t = 4- (just before end) seconds Also mark the direction of optical flow for the bird after it starts its flight Mark all distances in the image plane Note that all distances are measured from the center of the lens for convenience
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141 Design a system to count how many people entered the coffee room and what percentage took coffee Assume that you can use multiple
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CHAPTER 14 DYNAMIC
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cameras if required and you are free to determine the location and orientation of cameras 142 Implement an accumulative difference picture-based approach for motion analysis Use this approach to extract images of all people who came to your coffee room and took coffee 143 Develop a program to establish correspondence Use this to determine all corresponding points in a scene containing at least three objects moving in different directions 144 Implement a tracking algorithm using path coherence Test it using a basketball sequence to track the ball Modify this algorithm to work with a mobile camera Assume that you want to design a mobile robot that will track a particular moving object (pointed to it interactively) Apply your tracking algorithm to this problem x'x
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15 Object Recognition
An object recognition system finds objects in the real world from an image of the world, using object models which are known a priori This task is surprisingly difficult Humans perform object recognition effortlessly and instantaneously Algorithmic description of this task for implementation on machines has been very difficult In this chapter we will discuss different steps in object recognition and introduce some techniques that have been used for object recognition in many applications We will discuss the different types of recognition tasks that a vision system may need to perform We will analyze the complexity of these tasks and present approaches useful in different phases of the recognition task The object recognition problem can be defined as a labeling problem based on models of known objects Formally, given an image containing one or more objects of interest (and background) and a set of labels corresponding to a set of models known to the system, the system should assign correct labels to regions, or a set of regions, in the image The object recognition problem is closely tied to the segmentation problem: without at least a partial recognition of objects, segmentation cannot be done, and without segmentation, object recognition is not possible In this chapter, we discuss basic aspects of object recognition We present the architecture and main components of object recognition and discuss their role in object recognition systems of varying complexity
CHAPTER 15 OBJECT RECOGNITION
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Object class
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Figure 151: Different components of an object recognition system are shown
System Component
An object recognition system must have the following components to perform the task: Model database (also called modelbase) Feature detector Hypothesizer Hypothesis verifier A block diagram showing interactions and information flow among different components of the system is given in Figure 15I The model database contains all the models known to the system The information in the model database depends on the approach used for the recognition It can vary from a qualitative or functional description to precise geometric surface information In many cases, the models of objects are abstract feature vectors, as discussed later in this section A feature is some attribute of the object that is considered important in describing and recognizing the object in relation to other objects Size, color, and shape are some commonly used features The feature detector applies operators to images and identifies locations of features that help in forming object hypotheses The features used by a
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