This paper describes a new approach to the model base indexing stage of visual object recognition. Fast model base indexing of 3D objects is achieved by accessing a database of encoded 2D views of the objects using a ...

Invariance to various transformations is key to object recognition but existing definitions of invariance are somewhat confusing while discussions of invariance are often confused. In this report, we provide an operational ...

This paper presents a detailed error analysis of geometric hashing for 2D object recogition. We analytically derive the probability of false positives and negatives as a function of the number of model and image, ...

This paper sketches a hypothetical cortical architecture for visual 3D object recognition based on a recent computational model. The view-centered scheme relies on modules for learning from examples, such as Hyperbf-like ...

Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural ...

Numerous psychophysical experiments have shown an important role for attentional modulations in vision. Behaviorally, allocation of attention can improve performance in object detection and recognition tasks. At the neural ...

Determining the identity and pose of occluded objects from noisy data is a critical part of a system's intelligent interaction with an unstructured environment. Previous work has shown that local measurements of the ...

Determining the identity and pose of occluded objects from noisy data is a critical step in interacting intelligently with an unstructured environment. Previous work has shown that local measurements of position and ...

A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations ...

In model-based recognition, ad hoc techniques are used to decide if a match of data to model is correct. Generally an empirically determined threshold is placed on the fraction of model features that must be matched. ...

Discriminative learning is challenging when examples are setsof local image features, and the sets vary in cardinality and lackany sort of meaningful ordering. Kernel-based classificationmethods can learn complex decision ...

Riesenhuber \& Poggio recently proposed a model of object recognitionin cortex which, beyond integrating general beliefs about the visualsystem in a quantitative framework, made testable predictions aboutvisual processing. ...

Localized operators, like Gabor wavelets and difference-of-Gaussian filters, are considered to be useful tools for image representation. This is due to their ability to form a  sparse code that can serve as a basis set ...

This paper discusses how sparse local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degreed ...

In this note we discuss how recognition can be achieved from a single 2D model view exploiting prior knowledge of an object's structure (e.g. symmetry). We prove that for any bilaterally symmetric 3D object one non- ...

Many object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. With perfect image data, these ...

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust ...

Alignment is a prevalent approach for recognizing 3D objects in 2D images. A major problem with current implementations is how to robustly handle errors that propagate from uncertainties in the locations of image ...

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of ...

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor--based on a set of ...