Beyond core object recognition: Recurrent processes account for object recognition under occlusion.

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans.

Converging reports indicate that face images are processed through specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans.

The importance of visual features in generic vs. specialized object recognition: a computational study.

It is debated whether the representation of objects in inferior temporal (IT) cortex is distributed over activities of many neurons or there are restricted islands of neurons responsive to a specific set of objects. There are lines of evidence demonstrating that fusiform face area (FFA-in human) processes information related to specialized object recognition (here we say within category object recognition such as face identification). Physiological studies have also discovered several patches in monkey ventral temporal lobe that are responsible for facial processing.

Feedforward object-vision models only tolerate small image variations compared to human.

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations.

The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing.

Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation.

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization.

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information.

How can selection of biologically inspired features improve the performance of a robust object recognition model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system.