Artificial retina chips for image processing

Artificial retina chips which can simultaneously sense and process real world images are described. The comparison between artificial retinal systems and conventional image processing systems is described. Variable sensitivity photodetection, which is an essential technology for the artificial retina chips, is introduced in detail. The concept, structure, fundamental performance, operating principle, and processing functions for the fabricated artificial retinal chips are described. Applications including interactive games by gesture-input are also introduced. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996.     

Developing an artificial retina by evolutionary cellular automata and self-organizing neural networks

Retinas are very important for human beings to get information about their environment. In this paper, we propose a new method to build artificial retinas which have many features similar to real ones. We use evolutionary cellular automata to extract some basic characteristics of objects, and use self-organizing neural networks to distinguish different objects. The results indicate a way to get computer vision by artificial life. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, Janaury 19–21, 1998     

A sequential machine vision procedure for assessing paper impurities

We present a sequential, two-step procedure based on machine vision for detecting and characterizing impurities in paper. The method is based on a preliminary classification step to differentiate defective paper patches (i.e., with impurities) from non-defective ones (i.e., with no impurities), followed by a thresholding step to separate the impurities from the background. This approach permits to avoid the artifacts which occur when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure and experimentally validate it on a datasets of 11 paper classes. The results show that a marked increase in detection accuracy can be obtained with the two-step procedure in comparison with thresholding alone.     

Intelligent vision system for dynamic environments

This article describes an intelligent vision system that absorbs useful information from its environment and draws useful conclusions. This system can give instructions to locate vacant seats that are currently occupied in a theater. The extraction of useful information without viewing or exposing the inside details of an environment through an active vision system is proposed. Reasoning-based conclusions are drawn for optimum searching. The effectiveness of the proposed method is demonstrated using an experiment. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

神经网络在机器人视觉图像命令识别中的应用

在智能机器人技术中,视觉识别是关键.在智能机器人视觉系统获得的图像中,由于图像倾斜而造成的识别错误是视觉识别难以解决的问题.针对机器人所要完成的具体任务,对机器人的视觉识别问题进行探讨,为实现机器人对图像命令的识别,首先对机器人视觉系统获得的倾斜图像,采用Hough变换进行倾斜度检测并进行校正,然后采用人工神经网络法进行识别,根据识别结果对机器人的下一步运动进行决策与控制,达到了预期的目的.实验结果表明,该方法具有较高的识别率.     

A real-time vision system using an integrated memory array processor prototype

This paper describes a real-time vision system (RVS) architecture and performance and its use of an integrated memory array processor (IMAP) prototype. This prototype integrates eight 8-bit processors and a 144-kbit SRAM on a single chip. The RVS was developed with 64 IMAP prototypes connected in series in a 512 processor-system configuration. A host workstation can access the memory on the IMAP prototypes directly through a random access port. Images are inputted and outputted at high speed through serial access ports. The RVS performance is shown in real-time road-image processing and in a neural network simulation, as well as in low-level image processing algorithms, such as filtering, histograms, discrete cosine transform (DCT), and rotation. The RVS image processing is shown to be much faster than the video rate.     

A multisecret sharing scheme for color images based on cellular automata

In this work a new multisecret sharing scheme for secret color images among a set of users is proposed. The protocol allows that each participant in the scheme to share a secret color image with the rest of participants in such a way that all of them can recover all the secret color images only if the whole set of participants pools their shadows. The proposed scheme is based on the use of bidimensional reversible cellular automata with memory. The security of the scheme is studied and it is proved that the protocol is ideal and perfect and that it resists the most important statistical attacks.     

A reconfigurable computing framework for multi-scale cellular image processing

Cellular computing architectures represent an important class of computation that are characterized by simple processing elements, local interconnect and massive parallelism. These architectures are a good match for many image and video processing applications and can be substantially accelerated with Reconfigurable Computers. We present a flexible software/hardware framework for design, implementation and automatic synthesis of cellular image processing algorithms. The system provides an extremely flexible set of parallel, pipelined and time-multiplexed components which can be tailored through reconfigurable hardware for particular applications. The most novel aspects of our framework include a highly pipelined architecture for multi-scale cellular image processing as well as support for several different pattern recognition applications. In this paper, we will describe the system in detail and present our performance assessments. The system achieved speed-up of at least 100× for computationally expensive sub-problems and 10× for end-to-end applications compared to software implementations.     

Retina simulation using cellular automata and GPU programming

This article shows how the architectural modelization of biological retina allows real-time performances, on standard widespread computing systems. First, we describe the biological retina with regard to its pipeline architecture, detailing its layer behaviours and properties. Then we propose a corresponding pipelined model of artificial retina based on cellular automata. In this work, the main innovation is the computing method based on the programming of a personal computer graphical card using OpenGL shading language. The last section demonstrates the efficiency of our model through numerical and graphical results. We lay particular emphasis on the fact that our direct implementation of the Graphical Processor Unit (GPU) provides computation power about 20 times as fast as conventional programming.     

Image security system using recursive cellular automata substitution

This paper presents a novel image security system based on the replacement of the pixel values using recursive cellular automata (CA) substitution. This proposed image encryption method exhibits the properties of confusion and diffusion because of the characteristics of CA substitution are flexible. The salient features of the proposed image encryption method are its losslessness, symmetric private key encryption, very large number of secret keys, and key-dependent pixel value replacement. Simulation results obtained using some color and gray-level images clearly demonstrate the strong performance of the proposed image security system.     

Toward designing intelligent PDEs for computer vision: An optimal control approach

Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing a PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: learning PDEs from training data via an optimal control approach. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way.     

Computer vision technology for seam tracking in robotic GTAW and GMAW

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ±0.17 mm and ±0.3 mm in robotic GTAW and GMAW, respectively.     

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automata-based deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise-free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPS-based devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.     

足球机器人视觉图象的快速识别

针对机器人彩色视觉系统的实时性要求 ,提出了一种基于 YUV色彩空间的利用阈值向量来分割图象的识别方法 ,并改进了种子填充算法 ,从而明显减少了识别的计算量 ,并在保证识别精度的前提下 ,提高了处理速度 .通过在 Miro Sot机器人足球系统上的应用 ,验证了此方法的有效性 ,而该方法与常用的基于 RGB颜色空间的方法进行比较的结果也证明 ,此方法确有明显的优越性 .     

CYCLOPS: A mobile robotic platform for testing and validating image processing and autonomous navigation algorithms in support of artificial vision prostheses

While artificial vision prostheses are quickly becoming a reality, actual testing time with visual prosthesis carriers is at a premium. Moreover, it is helpful to have a more realistic functional approximation of a blind subject. Instead of a normal subject with a healthy retina looking at a low-resolution (pixelated) image on a computer monitor or head-mounted display, a more realistic approximation is achieved by employing a subject-independent mobile robotic platform that uses a pixelated view as its sole visual input for navigation purposes. We introduce CYCLOPS: an AWD, remote controllable, mobile robotic platform that serves as a testbed for real-time image processing and autonomous navigation systems for the purpose of enhancing the visual experience afforded by visual prosthesis carriers. Complete with wireless Internet connectivity and a fully articulated digital camera with wireless video link, CYCLOPS supports both interactive tele-commanding via joystick, and autonomous self-commanding. Due to its onboard computing capabilities and extended battery life, CYCLOPS can perform complex and numerically intensive calculations, such as image processing and autonomous navigation algorithms, in addition to interfacing to additional sensors. Its Internet connectivity renders CYCLOPS a worldwide accessible testbed for researchers in the field of artificial vision systems. CYCLOPS enables subject-independent evaluation and validation of image processing and autonomous navigation systems with respect to the utility and efficiency of supporting and enhancing visual prostheses, while potentially reducing to a necessary minimum the need for valuable testing time with actual visual prosthesis carriers.     

轮式机器人立体视觉平台的设计与实现

介绍了一种基于轮式机器人的立体视觉平台设计方案.给出了双目立体视觉原理和数学模型,从摄像机定标和对应点匹配两个方面讨论了形状与位置三维重建,阐述了双目立体视觉平台的设计,重点介绍了硬件平台的组成结构及各功能模块的电路组成与功能实现.该系统可根据具体应用情况灵活配置,因而结构合理、实用性强.     

异纤在线分拣系统机器视觉技术的应用

棉花中夹带的异性纤维比重虽小,危害却很大。本文针对异纤的检测问题,开发了一种基于机器视觉技术的分拣系统。通过严格的理论证明,建立了图像处理的数学模型,对其算法进行了深入的分析和研究,提出了基于计算机图形学理论中三维旋转坐标变换的图像处理算法,并利用紫外线的荧光效应区分两者的特征差别。实验表明,系统的可靠性、速度等指标均满足工业要求,所提出的数学模型和算法满足系统精确性和实时性的要求。     

Dual-coding representations for robot vision programming in Tekkotsu

We describe complementary iconic and symbolic representations for parsing the visual world. The iconic pixmap representation is operated on by an extensible set of “visual routines” (Ullman, 1984; Forbus et al., 2001). A symbolic representation, in terms of lines, ellipses, blobs, etc., is extracted from the iconic encoding, manipulated algebraically, and re-rendered iconically. The two representations are therefore duals, and iconic operations can be freely intermixed with symbolic ones. The dual-coding approach offers robot programmers a versatile collection of primitives from which to construct application-specific vision software. We describe some sample applications implemented on the Sony AIBO. David S. Touretzky is a Research Professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He earned his B.A. in Computer Science from Rutgers University in 1978, and his M.S. (1979) and Ph.D. (1984) in Computer Science from Carnegie Mellon. Dr. Touretzky’s research interests are in computational neuroscience, particularly representations of space in the rodent hippocampus and related structures, and high level primitives for robot programming. He is presently developing an undergraduate curriculum in cognitive robotics based on the Tekkotsu software framework described in this article. Neil S. Halelamien earned a B.S. in Computer Science and a B.S. in Cognitive Science at Carnegie Mellon University in 2004, and is currently pursuing his Ph.D. in the Computation & Neural Systems program at the California Institute of Technology. His research interests are in studying vision from both a computational and biological perspective. He is currently using transcranial magnetic stimulation to study visual representations and information processing in visual cortex. Ethan J. Tira-Thompson is a graduate student in the Robotics Institute at Carnegie Mellon University. He earned a B.S. in Computer Science and a B.S. in Human-Computer Interaction in 2002, and an M.S. in Robotics in 2004, at Carnegie Mellon. He is interested in a wide variety of computer science topics, including machine learning, computer vision, software architecture, and interface design. Ethan’s research has revolved around the creation of the Tekkotsu framework to enable the rapid development of robotics software and its use in education. He intends to specialize in mobile manipulation and motion planning for the completion of his degree. Jordan J. Wales is completing a Master of Studies in Theology at the University of Notre Dame. He earned a B.S. in Engineering (Swarthmore College, 2001), an M.Sc. in Cognitive Science (Edinburgh, UK, 2002), and a Postgraduate Diploma in Theology (Oxford, UK, 2003). After a year as a graduate research assistant in Computer Science at Carnegie Mellon, he entered the master’s program in Theology at Notre Dame and is now applying to doctoral programs. His research focus in early and medieval Christianity is accompanied by an interest in medieval and modern philosophies of mind and their connections with modern cognitive science. Kei Usui is a masters student in the Robotics Institute at Carnegie Mellon University. He earned his B.S. in Physics from Carnegie Mellon University in 2005. His research interests are reinforcement learning, legged locomotion, and cognitive science. He is presently working on algorithms for humanoid robots to maintain balance against unexpected external forces.     

Three dimentional measurement of fish movement using stereo vision

Recently, the technology of cultivation has been and continuous to be developed in the fisheries and the studies for a high production efficiency are widely conducted. The reaction of fishes under the sound should be studied in order to develop the fish tank with less factors of stress. In this paper, three-dimensional measurement system of the fish motion has been presented in order to study the relation of the fishes under the stimulating sound. Three kinds of fishes are used to detect the reaction against the sounds. Frequency analysis is executed by using the threedimensional position data. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008