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We propose a novel method for identifying and classifying motions that offers significantly reduced computational cost as compared to deep convolutional neural network systems with comparable performance. Our new approach is inspired by the information processing network architecture of biological visual processing systems, whereby spatial pyramid kernel features are efficiently extracted in real-time from temporally-differentiated image data. In this paper, we describe this new method and evaluate its performance with a hand motion gesture recognition task.

Fast and efficient motion estimation is essential for a number of applications including the gesture-based user interface (UI) for portable devices like smart phones. In this paper, we propose a highly efficient method that can estimate four degree of freedom (DOF) motional components of a moving object based on an event-based vision sensor, the dynamic vision sensor (DVS). The proposed method finds informative events occurred at edges and estimates their velocities for global motion analysis. We will also describe a novel method to correct the aperture problem in the motion estimation.

We report on the neuromorphic sound localization circuit which can enhance the perceptual sensation in a hearing aid system. All elements are simple leaky integrate-and-fire neuron circuits with different parameters optimized to suppress the impacts of synaptic circuit noises. The detection range and resolution of the proposed neuromorphic circuit are 500 us and 5 us, respectively. Our results show that, the proposed technique can localize a sound pulse with extremely narrow duration (∼ 1 ms) resulting in real-time response.

An algorithm to recognize four degrees of freedom gesture by using event-based image sensor is developed. The gesture motion includes three translations and one rotation. Each pixel of the event-based image sensor produces an event when temporal intensity change is larger than a pre-defined value. From the time-stamps of the events, a map of pseudo optical flow is calculated. The proposed algorithm achieves the gesture recognition based on this optical flow. It provides not only directions but also magnitudes of velocity. The proposed algorithm is memory-wise and computationally efficient because it uses only a current time-stamp map and local computation. This advantage will facilitate applications for mobile devices or on-chip development.

In this paper we present a simple technique for real-time touchless hand gesture user interface (UI) for mobile devices based on a biologically inspired vision sensor, the dynamic vision sensor (DVS). The DVS can detect a moving object in a fast and cost effective way by outputting events asynchronously on edges of the object. The output events are spatiotemporally correlated by using novel event-driven processing algorithms based on leaky integrate-and-fire neurons to track a finger tip or to infer directions of hand swipe motions. The experimental results show that the proposed technique can achieve graphic UI capable finger tip tracking with milliseconds intervals and accurate hand swipe motion detection with negligible latency.

This demonstration shows a natural gesture interface for console entertainment devices using as input a stereo pair of dynamic vision sensors. The event-based processing of the sparse sensor output allows fluid interaction at a laptop processor load of less than 3%.

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition.

We propose a link performance monitoring technique based on a dedicated optical monitoring channel to dynamically assess link performance like link OSNR and link MPI crosstalk for path computation in transparent optical networks.

A field trial of GMPLS-controlled all-optical networking was successfully demonstrated with assistance of OPMs capable of monitoring in-band OSNR. The reliable and stable network operation was achieved thanks to the GMPLS control plane and OPMs.

We report on the nonlinear inter-channel crosstalk in analog phase-modulated wavelength-division-multiplexed systems. The two-channel experimental results show a reduction of the crosstalk by ~15 dB, compared to intensity-modulated systems.

An all-optical sampling-based Q-factor monitor applicable to multi-bitrated and formatted signals was successfully demonstrated in a network testbed. Quality of GMPLS lightpaths could be diagnosed correlating the actually measured BER.

A GMPLS-controlled all-optical mesh networking was successfully demonstrated using a path computation-capable network management system (NMS). The developed NMS can manage topology and physical impairments, and create GMPLS-based lightpaths according to path computation results.

We propose and demonstrate a simple self-protecting architecture for WDM PON. The protection time was less than 10 ms, and the power penalty caused by the protection process was negligible.