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"Artificial vision"
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Ferroelectric-controlled graphene plasmonic surfaces for all-optical neuromorphic vision
Artificial visual systems can recognize desired objects and information from complex environments, and are therefore highly desired for pattern recognition, object detection, and imaging applications. However, state-of-the-art artificial visual systems with high recognition performances that typically consist of electronic devices face the challenges of requiring huge storage space and high power consumption owing to redundant data. Here, we report a terahertz (THz) frequency-selective surface using a graphene split-ring resonator driven by ferroelectric polarization for efficient visual system applications. The downward polarization of the ferroelectric material offers an ultrahigh electrostatic field for doping p-type graphene with an anticipated Fermi level. By optimizing the geometric parameters of the devices and modulating the carrier behaviors of graphene, our plasmonic devices exhibit a tunable spectral response in a range of 1.7–6.0 THz with continuous transmission values. The all-optical neural network using graphene plasmonic surfaces designed in this study exhibited excellent performance in visual preprocessing and convolutional filtering and achieved an ultrahigh recognition accuracy of up to 99.3% in training the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset. These features demonstrate the great potential of graphene plasmonic devices for future smart artificial vision systems.
Journal Article
Emotion recognition : a pattern analysis approach
\"Written by leaders in the field, this book provides a thorough and insightful presentation of the research methodology on emotion recognition in a highly comprehensive writing style. Topics covered include emotional feature extraction, facial recognition, human-computer interface design, neuro-fuzzy techniques, support vector machine (SVM), reinforcement learning, principal component analysis, the hidden Markov model, and probabilistic models. The result is a innovative edited volume on this timely topic for computer science and electrical engineering students and professionals\"-- Provided by publisher.
Book
Development of an artificial vision algorithm using image processing techniques to assess the characteristics of export-grade bananas
The main objective of this study is to develop a computer vision algorithm capable of identifying export-quality bananas, simplifying the quality control process. Unlike previous research, such as that of Figueroa and Roa (2016), which focused on different fruits using segmentation and clustering techniques, this work integrates a controlled lighting system and advanced neural networks trained with TensorFlow. These innovative approaches ensure an average accuracy of 97.22%, surpassing previous standards in the field and demonstrating their applicability in industrial settings.
Journal Article
Robotics, Vision and Control : Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition
Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in an accessible but informative style, easy to read and absorb, and includes over 1000 MATLAB and Simulink® examples and over 400 figures. The book is a real walk through the fundamentals of mobile robots, arm robots. then camera models, image processing, feature extraction and multi-view geometry and finally bringing it all together with an extensive discussion of visual servo systems. This second edition is completely revised, updated and extended with coverage of Lie groups, matrix exponentials and twists; inertial navigation; differential drive robots; lattice planners; pose-graph SLAM and map making; restructured material on arm-robot kinematics and dynamics; series-elastic actuators and operational-space control; Lab color spaces; light field cameras; structured light, bundle adjustment and visual odometry; and photometric visual servoing. \"An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished!\" OUSSAMA KHATIB, Stanford.
Book
Bipolar synaptic organic/inorganic heterojunction transistor with complementary light modulation and low power consumption for energy-efficient artificial vision systems
Photoelectric synaptic transistors integrate optical sensing and synaptic functions into a single device, which has significant advantages in neuromorphic computing for visual information, recognition, memory, and processing. However, the weight updating of existing photoelectric synapses is predominantly based on separate utilization of light and electrical stimuli to regulate synaptic excitation and inhibition. This approach significantly restricts the processing speed and application scenarios of devices. In this work, we propose bipolar synaptic organic/inorganic heterojunction transistor (BSOIHT) that can effectively simulate bidirectional (excitatory/inhibitory) synaptic behavior under light stimulation. Furthermore, by changing the position of electrode contacts and the metals of source and drain electrodes, carrier injection of the transistor is significantly improved with reduced synaptic event power consumption down to 2.4 fJ. Moreover, the BSOIHTs are adopted to build the neuromorphic vision system, which effectively facilitates image preprocessing and substantially enhances the recognition accuracy from 44.93% to 87.01%. This paper provides new avenues for the construction of energy-efficient artificial vision systems.
Journal Article
Ultrathin Gallium Nitride Quantum-Disk-in-Nanowire-Enabled Reconfigurable Bioinspired Sensor for High-Accuracy Human Action Recognition
Highlights
A novel GaN/AlN-based ultrathin quantum-disks-in-nanowires sensor was fabricated, demonstrating voltage bias tunable response characteristics to light stimuli.
Image enhancement functionality and a robust reservoir computing system were demonstrated based on the voltage tunable long-term and short-term persistent photocurrent respectively.
Furthermore, a high-performance artificial vision system with the two integrated functions was demonstrated, achieving a remarkable improvement in human action recognition.
Human action recognition (HAR) is crucial for the development of efficient computer vision, where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks across sensor-processor interfaces. However, the absence of interactions among versatile biomimicking functionalities within a single device, which was developed for specific vision tasks, restricts the computational capacity, practicality, and scalability of in-sensor vision computing. Here, we propose a bioinspired vision sensor composed of a GaN/AlN-based ultrathin quantum-disks-in-nanowires (QD-NWs) array to mimic not only Parvo cells for high-contrast vision and Magno cells for dynamic vision in the human retina but also the synergistic activity between the two cells for in-sensor vision computing. By simply tuning the applied bias voltage on each QD-NW-array-based pixel, we achieve two biosimilar photoresponse characteristics with slow and fast reactions to light stimuli that enhance the in-sensor image quality and HAR efficiency, respectively. Strikingly, the interplay and synergistic interaction of the two photoresponse modes within a single device markedly increased the HAR recognition accuracy from 51.4% to 81.4% owing to the integrated artificial vision system. The demonstration of an intelligent vision sensor offers a promising device platform for the development of highly efficient HAR systems and future smart optoelectronics.
Journal Article
A human-like visual-attention-based artificial vision system for wildland firefighting assistance
In this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system.
Journal Article
Practical guide to machine vision software : an introduction with LabVIEW
For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments.Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision.With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.
eBook
Will Retinal Implants Restore Vision?
A number of research groups are developing electrical implants that can be attached directly to the retina in an attempt to restore vision to patients suffering from retinal degeneration. However, despite promising results in animal experiments, there are still several major obstacles to overcome before retinal prostheses can be used clinically.
Journal Article