Perceptual organization is a challenging visual task that aims to perceive and group the individual visual element so that it is easy to understand the meaning of the scene as a whole. Most recent methods building upon advanced Convolutional Neural Network (CNN) come from learning discriminative representation and modeling context hierarchically. However, when the visual appearance difference between foreground and background is obscure, the performance of existing methods degrades significantly due to the visual ambiguity in the discrimination process. In this paper, we argue that the figure-ground assignment mechanism, which conforms to human vision cognitive theory, can be explored to empower CNN to achieve a robust perceptual organization despite visual ambiguity. Specifically, we present a novel Figure-Ground-Aided (FGA) module to learn the configural statistics of the visual scene and leverage it for the reduction of visual ambiguity.

In this paper, we argue that the figure-ground assignment mechanism, which conforms to human vision cognitive theory, can be explored to empower CNN to achieve a robust perceptual organization despite visual ambiguity.

In this paper, we argue that the figure-ground assignment mechanism, which conforms to human vision cognitive theory, can be explored to empower CNN to achieve a robust perceptual organization despite visual ambiguity.

Perceptual organization is a challenging visual task that aims to perceive and group the individual visual element so that it is easy to understand the meaning of the scene as a whole. Most recent methods building upon advanced Convolutional Neural Network (CNN) come from learning discriminative representation and modeling context hierarchically. However, when the visual appearance difference between foreground and background is obscure, the performance of existing methods degrades significantly due to the visual ambiguity in the discrimination process. In this paper, we argue that the figure-ground assignment mechanism, which conforms to human vision cognitive theory, can be explored to empower CNN to achieve a robust perceptual organization despite visual ambiguity. Specifically, we present a novel Figure-Ground-Aided (FGA) module to learn the configural statistics of the visual scene and leverage it for the reduction of visual ambiguity.

Estimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision. A promising ap(cid:173) proach to this problem involves using mixture models, where the motion of each object is a component in the mixture. However, ex(cid:173) isting methods typically require specifying in advance the number of components in the mixture, i.e. the number of objects in the scene. Here we show that the number of objects can be estimated auto(cid:173) matically in a maximum likelihood framework, given an assumption about the level of noise in the video sequence. We derive analytical results showing the number of models which maximize the likeli(cid:173) hood for a given noise level in a given sequence.

Figural analogy problems have long been a widely used format in human intelligence tests. In the past four decades, more and more research has investigated automatic item generation for figural analogy problems, i.e., algorithmic approaches for systematically and automatically creating such problems. In cognitive science and psychometrics, this research can deepen our understandings of human analogical ability and psychometric properties of figural analogies. With the recent development of data-driven AI models for reasoning about figural analogies, the territory of automatic item generation of figural analogies has further expanded. This expansion brings new challenges as well as opportunities, which demand reflection on previous item generation research and planning future studies. This paper reviews the important works of automatic item generation of figural analogies for both human intelligence tests and data-driven AI models. From an interdisciplinary perspective, the principles and technical details of these works are analyzed and compared, and desiderata for future research are suggested.

The Laboratory for Artificial Intelligence is comprised of several groups performing research in different areas of Artificial Intelligence. The LAIR was formed back in the 1970s, and the core researchers from that era form the Cognitive Systems group. There are a number of other groups at OSU conducting research in Artificial Intelligence areas; while not formally part of LAIR they have overlapping interests and are sometimes part of collaborative projects. LAIR research areas often cut across groups; while each section below describes a general activity it is best to see individulal faculty members' sites for more specific information. Computation Learning Theory is concerned with developing algorithms to allow computers to make decisions and find patterns in data by observing a data (rather than through explicitly specified rules).

Inspired by the extraordinary computing power promised by quantum computers, the quantum mind hypothesis postulated that quantum mechanical phenomena are the source of neuronal synchronization, which, in turn, might underlie consciousness. Here, I present an alternative inspired by a classical computing method with quantum power. This method relies on special distributed representations called hyperstrings. Hyperstrings are superpositions of up to an exponential number of strings, which -- by a single-processor classical computer -- can be evaluated in a transparallel fashion, that is, simultaneously as if only one string were concerned. Building on a neurally plausible model of human visual perceptual organization, in which hyperstrings are formal counterparts of transient neural assemblies, I postulate that synchronization in such assemblies is a manifestation of transparallel information processing. This accounts for the high combinatorial capacity and speed of human visual perceptual organization and strengthens ideas that self-organizing cognitive architecture bridges the gap between neurons and consciousness.

A figure-ground segregation network is proposed based on a novel boundary pair representation. Nodes in the network are boundary segments obtained through local grouping. Each node is excitatorily coupled with the neighboring nodes that belong to the same region, and inhibitorily coupled with the corresponding paired node. Gestalt grouping rules are incorporated by modulating connections. The status of a node represents its probability being figural and is updated according to a differential equation.

A figure-ground segregation network is proposed based on a novel boundary pair representation. Nodes in the network are boundary segments obtained through local grouping. Each node is excitatorily coupled with the neighboring nodes that belong to the same region, and inhibitorily coupled with the corresponding paired node. Gestalt grouping rules are incorporated by modulating connections. The status of a node represents its probability being figural and is updated according to a differential equation.