Now showing items 1-10 of 115
From primal templates to invariant recognition
We can immediately recognize novel objects seen only once before -- in different positions on the retina and at different scales (distances). Is this ability hardwired by our genes or learned during development -- and ...
Generalization and Properties of the Neural Response
Hierarchical learning algorithms have enjoyed tremendous growth in recent years, with many new algorithms being proposed and applied to a wide range of applications. However, despite the apparent success of hierarchical ...
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after ...
The Levels of Understanding framework, revised
I discuss the "levels of understanding" framework described in Marr's Vision and propose a revised and updated version of it to capture the changes in computation and neuroscience over the last 30 years.
Sparse Correlation Kernel Analysis and Reconstruction
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis ...
Rotation Invariant Object Recognition from One Training Example
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 ...
The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Obect Classes
Most psychophysical studies of object recognition have focussed on the recognition and representation of individual objects subjects had previously explicitely been trained on. Correspondingly, modeling studies have often ...
People Recognition in Image Sequences by Supervised Learning
We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support ...
Feature Selection for Face Detection
We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of ...
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes
We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called ...