A method for unrestricted recognition of three-dimensional objects was developed. By unrestricted, we imply that the recognition will be done independently of object position, scale, orientation, and pose against a structured background. It does not assume any preceding segmentation or allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feedback learning procedure, percepts are mapped on system states corresponding to manipulation parameters of the object. The method uses a learning architecture with channel information representation. This article discusses how objects can be represented. We propose a structure to deal with object and contextual properties in a transparent manner.

In real-world scenes, intrinsic object information is often degraded due to occlusion, low contrast, and poor resolution. In such situations, the object recognition problem based on intrinsic object representations is ill-posed. A more comprehensive representation of an object should include contextual information [11,13]: Obj.

In real-world scenes, intrinsic object information is often degraded due to occlusion, low contrast, and poor resolution. In such situations, the object recognition problem based on intrinsic object representations is ill-posed. A more comprehensive representation of an object should include contextual information [11,13]: Obj.

In such approaches, an object is defined by means of local features.

In other words, we have the categories we do because they preserve existing similarities among objects and are therefore informative. According to Goodman, one researchers are adopting richer and must specify in what respect two objects categories? What is the role approaches. If two objects are similar only of similarity in categorization? Can we approaches address some because they are in the same category, of the shortcomings of previous approaches.

We present a neural model that can perform eye movements to a particular side of an object regardless of the position and orientation ofthe object in space, a generalization of a task which has been recently used by Olson and Gettner [4] to investigate the neural structureof object-centered representations. Our model uses an intermediate representation in which units have oculocentric receptive fields-just like collicular neurons-whose gain is modulated by the side of the object to which the movement is directed, as well as the orientation of the object. We show that these gain modulations are consistent with Olson and Gettner's single cell recordings in the supplementary eye field. This demonstrates that it is possible to perform an object-centered task without a representation involving anobject-centered map, viz., without neurons whose receptive fields are defined in object-centered coordinates. We also show that the same approach can account for object-centered neglect, a situation inwhich patients with a right parietal lesion neglect the left side of objects regardless of the orientation of the objects. Several authors have argued that tasks such as object recognition [3] and manipulation [4]are easier to perform if the object is represented in object-centered coordinates, arepresentation in which the subparts of the object are encoded with respect to a frame of reference centered on the object. Compelling evidence for the existence of such representations in the cortex comes from experiments on hemineglect-a neurological syndrome resulting from unilateral lesions of the parietal cortex such that a right lesion, for example, leads patients to ignore stimuli located on the left side of their egocentric space. Recently, Driver et al. (1994) showed that the deficit can also be object-centered.

We present a neural model that can perform eye movements to a particular side of an object regardless of the position and orientation of the object in space, a generalization of a task which has been recently used by Olson and Gettner [4] to investigate the neural structure of object-centered representations. Our model uses an intermediate representation in which units have oculocentric receptive fields-just like collicular neurons-whose gain is modulated by the side of the object to which the movement is directed, as well as the orientation of the object. We show that these gain modulations are consistent with Olson and Gettner's single cell recordings in the supplementary eye field. This demonstrates that it is possible to perform an object-centered task without a representation involving an object-centered map, viz., without neurons whose receptive fields are defined in object-centered coordinates. We also show that the same approach can account for object-centered neglect, a situation in which patients with a right parietal lesion neglect the left side of objects regardless of the orientation of the objects. Several authors have argued that tasks such as object recognition [3] and manipulation [4] are easier to perform if the object is represented in object-centered coordinates, a representation in which the subparts of the object are encoded with respect to a frame of reference centered on the object. Compelling evidence for the existence of such representations in the cortex comes from experiments on hemineglect-a neurological syndrome resulting from unilateral lesions of the parietal cortex such that a right lesion, for example, leads patients to ignore stimuli located on the left side of their egocentric space. Recently, Driver et al. (1994) showed that the deficit can also be object-centered.

We present a neural model that can perform eye movements to a particular side of an object regardless of the position and orientation of the object in space, a generalization of a task which has been recently used by Olson and Gettner [4] to investigate the neural structure of object-centered representations. Our model uses an intermediate representation in which units have oculocentric receptive fields-just like collicular neurons-whose gain is modulated by the side of the object to which the movement is directed, as well as the orientation of the object. We show that these gain modulations are consistent with Olson and Gettner's single cell recordings in the supplementary eye field. This demonstrates that it is possible to perform an object-centered task without a representation involving an object-centered map, viz., without neurons whose receptive fields are defined in object-centered coordinates. We also show that the same approach can account for object-centered neglect, a situation in which patients with a right parietal lesion neglect the left side of objects regardless of the orientation of the objects. Several authors have argued that tasks such as object recognition [3] and manipulation [4] are easier to perform if the object is represented in object-centered coordinates, a representation in which the subparts of the object are encoded with respect to a frame of reference centered on the object. Compelling evidence for the existence of such representations in the cortex comes from experiments on hemineglect-a neurological syndrome resulting from unilateral lesions of the parietal cortex such that a right lesion, for example, leads patients to ignore stimuli located on the left side of their egocentric space. Recently, Driver et al. (1994) showed that the deficit can also be object-centered.

Recognition of specific objects, such as recognition of a particular face, can be based on representations that are object centered, such as 3D structural models. Alternatively, a 3D object may be represented for the purpose of recognition in terms of a set of views. This latter class of models is biologically attractive because model acquisition - the learning phase - is simpler and more natural. A simple model for this strategy of object recognition was proposed by Poggio and Edelman (Poggio and Edelman, 1990). They showed that, with few views of an object used as training examples, a classification network, such as a Gaussian radial basis function network, can learn to recognize novel views of that object, in partic- 42 E. Bricolo, T. Poggio and N. Logothetis

Recognition of specific objects, such as recognition of a particular face, can be based on representations that are object centered, such as 3D structural models. Alternatively, a 3D object may be represented for the purpose of recognition in terms of a set of views. This latter class of models is biologically attractive because model acquisition - the learning phase - is simpler and more natural. A simple model for this strategy of object recognition was proposed by Poggio and Edelman (Poggio and Edelman, 1990). They showed that, with few views of an object used as training examples, a classification network, such as a Gaussian radial basis function network, can learn to recognize novel views of that object, in partic- 42 E. Bricolo, T. Poggio and N. Logothetis