A computational model of texture segmentation

An algorithm for finding texture boundaries in images is developed on the basis of a computational model of human texture perception. The model consists of three stages: (1) the image is convolved with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses; (2) inhibition, localized in space, within and among the neural response profiles results in the suppression of weak responses when there are strong responses at the same or nearby locations; and (3) texture boundaries are detected using peaks in the gradients of the inhibited response profiles. The model is precisely specified, equally applicable to grey-scale and binary textures, and is motivated by detailed comparison with psychophysics and physiology. It makes predictions about the degree of discriminability of different texture pairs which match very well with experimental measurements of discriminability in human observers. From a machine-vision point of view, the scheme is a high-quality texture-edge detector which works equally on images of artificial and natural scenes. The algorithm makes the use of simple local and parallel operations, which makes it potentially real-time

A Computational Model for Quantum Measurement

Is the dynamical evolution of physical systems objectively a manifestation of information processing by the universe? We find that an affirmative answer has important consequences for the measurement problem. In particular, we calculate the amount of quantum information processing involved in the evolution of physical systems, assuming a finite degree of fine-graining of Hilbert space. This assumption is shown to imply that there is a finite capacity to sustain the immense entanglement that measurement entails. When this capacity is overwhelmed, the system's unitary evolution becomes computationally unstable and the system suffers an information transition (`collapse'). Classical behaviour arises from the rapid cycles of unitary evolution and information transitions. Thus, the fine-graining of Hilbert space determines the location of the `Heisenberg cut', the mesoscopic threshold separating the microscopic, quantum system from the macroscopic, classical environment. The model can be viewed as a probablistic complement to decoherence, that completes the measurement process by turning decohered improper mixtures of states into proper mixtures. It is shown to provide a natural resolution to the measurement problem and the basis problem.Comment: 24 pages; REVTeX4; published versio

A Computational Model for Biological Tissues Considering the Influence of Injury on Growth and Remodelling

Biological tissues adapt to changed loading conditions through growth and remodelling (G&R) to reestablish a so-called homeostatic state. On the other hand, loading conditions above their physiological limits, as during trauma or surgical procedures, cause injury and can initiate pathological G&R. Herein, a modelling approach for G&R influenced by injury is presented combining the theories of plasticity and homogenised constrained mixtures. The results show that injury has a significant impact on the G&R behaviour and thus on the accomplishment of homeostasis

A Computational Model of Children's Semantic Memory

A computational model of children's semantic memory is built from the Latent Semantic Analysis (LSA) of a multisource child corpus. Three tests of the model are described, simulating a vocabulary test, an association test and a recall task. For each one, results from experiments with children are presented and compared to the model data. Adequacy is correct, which means that this simulation of children's semantic memory can be used to simulate a variety of children's cognitive processes

A Computational Model of Worker Protest

This paper presents an agent-based model of worker protest. Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If workers are surrounded by more in-group members, they are more risk-taking; if surrounded by more out-group members, more risk-averse. Individual interest and group membership jointly affect patterns of workers protest: rhythm, frequency, strength, and duration of protest outbreaks. Results indicate that when wages are more unequally distributed, the previous outburst tends to suppress the next one, protests occur more frequently, and they become more intensive and persistent. Group identification does not seriously influence the frequency of local uprisings. Both their strength and duration, however, are negatively affected by the ingroup-outgroup assessment. The overall findings are valid when workers distinguish \'us\' from \'them\' through simple binary categorization, as well as when they perceive degrees of similarity and difference from their neighbors.Workers Protest, Tags, Group Identity, Trust, Netlogo