A multi-layer sparse coding network learns contour coding from natural images

A Non-Negative Sparse Coding Network Learns Contour Coding and Integration From Natural Images

Face Recognition using an Affine Sparse Coding approach

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

Learning Image Decompositions with Hierarchical Sparse Coding

We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures...

Contour Segmentation with Recurrent Neural Networks of Pulse-Coding Neurons

The performance of technical and biological vision systems crucially relies on powerful processing capabilities. Robust object recognition must be based on representations of segmented object candidates which are kept stable and sparse despite the highly variable nature of the environment. Here, we propose a network of pulse-coding neurons based on biological principles which establishs such re...

Efficient Visual Coding: From Retina To V2

The human visual system has a hierarchical structure consisting of layers of processing, such as the retina, V1, V2, etc. Understanding the functional roles of these visual processing layers would help to integrate the psychophysiological and neurophysiological models into a consistent theory of human vision, and would also provide insights to computer vision research. One classical theory of t...