Publisher Summary This chapter focuses on learning and recognizing 3D objects from
multiple views in a neural system. In this approach of designing a 3D object recognition …

In the context of computer vision, the recognition of three-dimensional objects typically
consists of image capture, feature extraction, and object model matching. During the image …

We investigated how varying the number of unique parts within an object influences
recognition across changes in viewpoint The stimuli were shaded objects composed of five …

Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation
and use of what is seen in the image. In this book, Shimon Ullman focuses on the processes …

We describe a novel approach, based on ideal observer analysis, for measuring the ability
of human observers to use image information for 3D object perception. We compute the …

How does the brain represent visual objects? In simple perceptual generalization tasks, the
human visual system performs as if it represents the stimuli in a low-dimensional metric …

Background: Human observers can recognize three-dimensional objects seen in novel
orientations, even when they have previously seen only a relatively small number of different …

In many computational approaches to vision it has been emphasised that object recognition
involves the encoding of view-independent descriptions prior to matching to a stored object …

Visually recognizing objects at different orientations and distances has been assumed to
depend either on extracting from the retinal image a viewpoint–invariant, typically three …

We present a method for planning sequences of views for recognition and pose (orientation)
determination of 3-D objects of arbitrary shape. The approach consists of a learning stage in …