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Amit Y. 2D Object Detection and Recognition

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Amit Y. 2D Object Detection and Recognition
Издательство MIT Press, 2002, -325 pp.
This book is about detecting and recognizing 2D objects in gray-level images. How are models constructed? How are they trained? What are the computational approaches to efficient implementation on a computer? And finally, how can some of these computations be implemented in the framework of parallel and biologically plausible neural network architectures?
Detection refers to anything from identifying a location to identifying and registering components of a particular object class at various levels of detail. For example, finding the faces in an image, finding the eyes and mouths of the faces. One could require a precise outline of the object in the image, or the detection of a certain number of well-defined landmarks on the object, or a deformation from a prototype of the object into the image. The deformation could be a simple 2D affine map or a more detailed nonlinear map. The object itself may have different degrees of variability. It may be a rigid 2D object, such as a fixed computer font or a 2D view of a 3D object, or it may be a highly deformable object, such as the left ventricle of the heart. All these are considered object-detection problems, where detection implies identifying some aspects of the particular way the object is present in the image—namely, some partial description of the object instantiation.
Recognition refers to the classification among objects or subclasses of a general class of objects present in a particular region of the image that has been isolated. For example, after detecting a face, identify the person, or classify images of handwritten digits, or recognize a symbol from a collection of hundreds of symbols. Both domains have a significant training and statistical estimation component.
Finding a predetermined object in a scene, or recognizing the object present in a particular region are only subproblems of the more-general and ambitious goal of computer vision. In broad terms, one would want to develop an artificial system that can receive an image and identify all the objects or a large part of the objects present in a complex scene from a library of thousands of classes. This implies not only detection and recognition algorithms, but methods for sequentially learning new objects and incorporating them into the current recognition and detection schemes. But perhaps hardest of all is the question of how to start processing a complex scene with no prior information on its contents—what to look for first, and in which particular regions should a recognition algorithm be implemented. This general problem is unsolved, although our visual system seems to solve it effortlessly and very efficiently.
Detection and Recognition: Overview of Models
1D Models: Deformable Contours
1D Models: Deformable Curves
2D Models: Deformable Images
Sparse Models: Formulation, Training, and Statistical Properties
Detection of Sparse Models: Dynamic Programming
Detection of Sparse Models: Counting
Object Recognition
Scene Analysis: Merging Detection and Recognition
Neural Network Implementations
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