Human and non-human primates excel at visual recognition tasks. The primate visual
system exhibits a strong degree of selectivity while at the same time being robust to changes …

We introduce a new general framework for the recognition of complex visual scenes, which
is motivated by biology: We describe a hierarchical system that closely follows the …

Recently, salient object detection (SOD) has witnessed vast progress with the rapid
development of convolutional neural networks (CNNs). However, the improvement of SOD …

In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

Reliably detecting objects in broad-area overhead or satellite images has become an
increasingly pressing need, as the capabilities for image acquisition are growing rapidly …

This thesis describes an effort to construct a scene understanding system that is able to
analyze the content of real images. While constructing the system we had to provide …

We introduce an unsupervised co-hierarchical analysis of a set of shapes, aimed at
discovering their hierarchical part structures and revealing relations between geometrically …

This paper presents an extension of the HMAX model, a neural network model for image
classification. The HMAX model can be described as a four-level architecture, with the first …

The biologically inspired model (BIM) proposed by Serre presents a promising solution to
object categorization. It emulates the process of object recognition in primates' visual cortex …

Described is a bio-inspired vision system for object recognition. The system comprises an
attention module, an object recognition module, and an online labeling module. The …