| This book is about detecting and recognizing 2D objects in gray-level images. Howare
models constructed? Howare 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. |
