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For three-dimensional cases, one should consider the information used in the object recognition task Two different cases are:
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Intensity: There is no surface information available explicitly in intensity images Using intensity values, features corresponding to the three-dimensional structure of objects should be recognized 25-dimensional images: In many applications, surface representations with viewer-centered coordinates are available, or can be computed, from images This information can be used in object recognition Range images are also 25-dimensional These images give the distance to different points in an image from a particular view point
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Segmented The images have been segmented to separate objects from the background As discussed in 3 on segmentation, object recognition and segmentation problems are closely linked in most cases In some applications, it is possible to segment out an object easily In cases when the objects have not been segmented, the recognition problem is closely linked with the segmentation problem
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Images represent a scene from a camera's perspective It appears natural to represent objects in a camera-centric, or viewer-centered, coordinate system Another possibility is to represent objects in an object-centered coordinate system Of course, one may represent objects in a world coordinate system also Since it is easy to transform from one coordinate system to another using their relative positions, the central issue in selecting the proper coordinate system to represent objects is the ease of representation to allow the most efficient representation for feature detection and subsequent processes A representation allows certain operations to be efficient at the cost of other operations Representations for object recognition are no exception Designers must consider the parameters in their design problems to select
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CHAPTER 15 OBJECT RECOGNITION
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the best representation for the task The following are commonly used representations in object recognition
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If objects usually appear in a relatively few stable positions with respect to the camera, then they can be represented efficiently in an observer-centered coordinate system If a camera is located at a fixed position and objects move such that they present only some aspects to the camera, then one can represent objects based on only those views If the camera is far away from objects, as in remote sensing, then three-dimensionality of objects can be ignored In such cases, the objects can be represented only by a limited set of views-in fact, only one view in most cases Finally, if the objects in a domain of applications are significantly different from each other, then observer-centered representations may be enough Observer-centered representations are defined in image space These representations capture characteristics and details of the images of objects in their relative camera positions One of the earliest and most rigorous approaches for object recognition is based on characterizing objects using a feature vector This feature vector captures essential characteristics that help in distinguishing objects in a domain of application The features selected in this approach are usually global features of the images of objects These features are selected either based on the experience of a designer or by analyzing the efficacy of a feature in grouping together objects of the same class while discriminating it from the members of other classes Many feature selection techniques have been developed in pattern classification These techniques study the probabilistic distribution of features of known objects from different classes and use these distributions to determine whether a feature has sufficient discrimination power for classification In Figure 152 we show a two-dimensional version of a feature space An object is represented as a point in this space It is possible that different features have different importance and that their units are different These problems are usually solved by assigning different weights to the features and by normalizing the features
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