Range image segmentation using a relaxation oscillator network

Abstract

Abstract-A locally excitatory globally inhibitory oscillator network (LEGION) is constructed and applied to range image segmentation, where each oscillator has excitatory lateral connections to the oscillators in its local neighborhood as well as a connection with a global inhibitor. A feature vector, consisting of depth, surface normal, and mean and Gaussian curvatures, is associated with each oscillator and is estimated from local windows at its corresponding pixel location. A context-sensitive method is applied in order to obtain more reliable and accurate estimations. The lateral connection between two oscillators is established based on a similarity measure of their feature vectors. The emergent behavior of the LEGION network gives rise to segmentation. Due to the flexible representation through phases, our method needs no assumption about the underlying structures in image data and no prior knowledge regarding the number of regions. More importantly, the network is guaranteed to converge rapidly under general conditions. These unique properties may lead to a real-time approach for range image segmentation in machine perception.Index Terms-LEGION, oscillatory correlation, pulse-coupled neural networks, range image segmentation, relaxation oscillator.

Enhancement of temperature-modulated NbO2-based relaxation oscillator via interfacial and bulk treatments

“…CMOS oscillator devices such as ring oscillators, though well established, are facing problems such as high power density and the lack of frequency tunability [2]. There are several applications for the ONN, such as an oscillatory neural autoencoder [10], solving cluster analysis problems [11], robotic controls [12,13], and pattern recognition [14][15][16]. The frequency tunability of a device is essential in ONN applications particularly in pattern recognition based on the frequency shift keying scheme.…”

Discrete-Time Dynamic Image Segmentation Using Oscillators with Adaptive Coupling

“…In the locally excitatory globally inhibitory oscillator network model proposed by [4] [5], coupled oscillators are represented by ordinary differential equations. This method effectively segments input images into image regions [6] [7]. However, these continuous dynamical systems must be integrated over time to produce oscillation, which requires considerable computation time and introduces approximation errors in a numerical simulation.…”

A novel method combining cellular neural networks and the coupled nonlinear oscillators’ paradigm involving a related bifurcation analysis for robust image contrast enhancement in dynamically changing difficult visual environments

“…The last decades have witnessed significant attention devoted to the study of nonlinear coupled oscillators [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] with various related applications in diverse areas such as electrical engineering [18,19], mechanics [15], electromechanics [14] and electronics [16], to name a few. In some previous works [18][19][20][21], we have shown some interesting applications of the coupling between van der Pol and Duffing oscillators in both electronics and electromechanics.…”

“…In some previous works [18][19][20][21], we have shown some interesting applications of the coupling between van der Pol and Duffing oscillators in both electronics and electromechanics. Further, in recent literature a number of notable works have underscored the various applications and the high potential of the nonlinear dynamics-based image processing [1][2][3][4][5][6][7][8][9][10][11][12][13]: (a) the use of the cellular neural network (CNN) paradigm for contrast enhancement [1], edge detection [11,17] and image segmentation [2-10, 12, 13], and (b) the use of the so-called LEGION model (involving nonlinear coupled oscillators) mainly for image segmentation [3]. The relevant literature does however not provide sufficient information concerning the application of nonlinear coupled/uncoupled oscillators in image processing, especially for the specific task of contrast enhancement.…”