We propose a method for image segmentation based on a neural oscillator network. Unlike previous methods, weight adaptation is adopted during segmentation to remove noise and preserve significant discontinuities in an image. Moreover, a logarithmic grouping rule is proposed to facilitate grouping of oscillators representing pixels with coherent properties. We show that weight adaptation plays the roles of noise removal and feature preservation. In particular, our weight adaptation scheme is insensitive to termination time and the resulting dynamic weights in a wide range of iterations lead to the same segmentation results. A computer algorithm derived from oscillatory dynamics is applied to synthetic and real images and simulation results show that the algorithm yields favorable segmentation results in comparison with other recent algorithms. In addition, the weight adaptation scheme can be directly transformed to a novel feature-preserving smoothing procedure. We also demonstrate that our nonlinear smoothing algorithm achieves good results for various kinds of images.

Relaxation oscillations exhibiting more than one time scale arise naturally from many physical systems. This paper proposes a method to numerically integrate large systems of relaxation oscillators. The numerical technique, called the singular limit method, is derived from analysis of relaxation oscillations in the singular limit. In such limit, system evolution gives rise to time instants at which fast dynamics takes place and intervals between them during which slow dynamics takes place. A full description of the method is given for LEGION (locally excitatory globally inhibitory oscillator networks), where fast dynamics, characterized by jumping which leads to dramatic phase shifts, is captured in this method by iterative operation and slow dynamics is entirely solved. The singular limit method is evaluated by computer experiments, and it produces remarkable speedup compared to other methods of integrating these systems. The speedup makes it possible to simulate large-scale oscillato...

The auditory system must separate an acoustic mixture in order to create a perceptual description of each sound source. It has been proposed that this is achieved by a process of auditory scene analysis (ASA) in which a number of streams are produced, each describing a single sound source. Few computer models of ASA attempt to incorporate attentional effects, since ASA is typically seen as a precursor to attentional mechanisms. This assumption may be flawed: recent work has suggested that attention plays a key role in the formation of streams, as opposed to the conventional view that attention merely selects a pre-constructed stream. This study presents a conceptual framework for auditory selective attention in which the formation of groups and streams is heavily influenced by conscious and subconscious attention. This framework is implemented as a computational model comprising a network of neural oscillators which perform stream segregation on the basis of oscillatory correlation. Within the network, attentional interest is modelled as a gaussian distribution in frequency. This determines the connection weights between oscillators and the attentional process- the attentional leaky integrator (ALI). A segment or group of segments are said to be attended to if their oscillatory activity coincides temporally with a peak in the ALI activity. The output of the model is an ‘attentional stream’: a description of which frequencies are being attended at each epoch. The model successfully simulates a range of psychophysical phenomena. Furthermore, a number of predictions are made and a psychophysical experiment is conducted to investigate the time course of attentional allocation in a binaural streaming task. The results support the model prediction that attention is subject to a form of ‘reset ’ when the attentional focus is moved in space. Acknowledgements The inspiration and initial psychophysical data upon which this work is based came from a presentation

Abstract—Scene analysis is a major aspect of perception and continues to challenge machine perception. This paper addresses the scene-analysis problem by integrating a primitive segmentation stage with a model of associative memory. Our model is a multistage system that consists of an initial primitive segmentation stage, a multimodule associative memory, and a short-term memory (STM) layer. Primitive segmentation is performed by locally excitatory globally inhibitory oscillator network (LEGION), which segments the input scene into multiple parts that correspond to groups of synchronous oscillations. Each segment triggers memory recall and multiple recalled patterns then interact with one another in the STM layer. The STM layer projects to the LEGION network, giving rise to memory-based grouping and segmentation. The system achieves scene analysis entirely in phase space, which provides a unifying mechanism for both bottom-up analysis and top-down analysis. The model is evaluated with a systematic set of three-dimensional (3-D) line drawing objects, which are arranged in an arbitrary fashion to compose input scenes that allow object occlusion. Memory-based organization is responsible for a significant improvement in performance. A number of issues are discussed, including input-anchored alignment, top-down organization, and the role of STM in producing context sensitivity of memory recall. Index Terms—Associative memory, grouping, integration, locally excitatory globally inhibitory oscillator network (LEGION), scene analysis, segmentation, short-term memory (STM). I.