Vision system

To date a small palette of isotrigon textures have been available to study how the brain uses higher-order spatial correlation information. We introduce several hundred new isotrigon textures. Special modulation properties are illustrated that can be used to extract neural responses to higher-order spatial correlations. We also ask how many textures make an adequate training set and how representative individual examples are of their texture class. Human discrimination of 90 of these patterns was quantified. Modeling those responses shows that humanlike performance can be obtained providing a fourth-order classifier is used, although more than one mechanism is required.

Using anatomy of Musca domestica's (the housefly) first optic ganglion, the lamina ganglionaris, a biologically inspired artificial neural network is presented. This paper focuses on communication phenomena and activity between specific neurons in the lamina and models nonsynaptic communication between neurons. Test results demonstrate that nonsynaptic communication between neurons can achieve results similar to synaptic connections in early vision systems

Broker (ORB). Infrastructure services within the CORBA model include persistence support, lifecycle support, a component naming service, a trading service for identifying services undertaken by components, and others. Within the context of component based software, another concept has become increasingly important, and referred to as the "Grid". The "Grid" or the "Computational Grid" (CG) signifies a component infrastructure that is based on the provision of infrastructure services which enable multi-vendor and multi-organisational services to be integrated (where a component may provide a single or a collection of services). A global Grid may therefore be a federation of many local Grids, and composed of components of varying granularity. Applications which utilise these services can range from large scale problems modelling some physical phenomenon, such as an oil spill, to the universal availability of customised and personalised services, accessible from wireless and mobile devices. Design patterns play an important role in constructing software services for such a Grid based environment, as many applications are likely to utilise common themes in their construction, and their subsequent interaction. We use the "service" abstraction as the common theme to identify how applications may be composed, shared, and managed over a CG -and define design patterns that become important in the context of such a service model. A central assumption of this paper is that applications on a CG will necessarily involve common services. However, it is important to note that a design pattern can only provide general guidelines to a possible design, and the system designer is responsible for exercising suitable judgement when applying them. Based on a service perspective, a CG, foremost, requires an integration of services representing: (1) computation, (2) data, (3) hardware resources, and (4) people, in an effective manner to solve a particular problem of interest. The concept of a Grid as an infrastructure is important because it is concerned, above all, with large scale pooling of resources, whether computer cycles, data, sensors, or people, undertaken in a transparent and pervasive manner. The Grid is also being associated with the concept of creating a distributed computation environment out of a collection of files, databases, computers and scientific instruments. There are five major application classes that could benefit from CGs [13]: • Distributed supercomputing, in which the Grid is used to aggregate computational resources to address very large problems that cannot be handled on a single system -such as distributed interactive simulations, simulation of physical processes (e.g. stellar dynamics, climate modelling etc) • High throughput computing, in which the Grid is used to schedule large numbers of loosely-coupled or independent tasks, with the goal of putting unused processor cycles to work. Examples include the use of multiple distributed workstations to solve hard cryptographic or complex engineering design problems.

Robot soccer requires vision to be fast, accurate and reliable for a team to perform well. To win competitions, the vision system must work every day under all contest conditions. In practice, the field conditions for robot soccer can vary enormously from field to field, and even from ...

In this paper a calibration method for on-board cameras used on the ARGO autonomous vehicle is presented. A number of markers have been placed on the vehicle's hood, which is framed by the vision system. Thanks to the knowledge of the markers' position it is possible to compute camera position and orientation with respect to the vehicle. Being based on fast computations, this procedure -of basic importance when the camera head has pan-tilt capabilities-can be performed during autonomous driving, without slowing down normal operations.

The automotive industries are more specific in automation of the present systems and making them responsive according to the environment. A control circuit for a wind shield wiper motor is described in this paper which will automatically make the response to the presence of moisture droplets on the windshields and vary its speed of motion accordingly. Different kind of sensors, actuators and mechanical designs were tested and finally we came up with the model using pic microcontroller. The cyst of this project is that the rain sensor is used to detect the amount of the rain and give the signal to the controller. The ADC in the controller detects the sensor input and gives the signal to the driver circuit. The motor driver actuates the motor to run at high speed or low speed based on the amount of rain detected. Initialization of the sensor is done when the vehicle is started and generates the power supply needed for the optimum functioning of the sensor. The threshold voltage value for the sensor is defined for the no rain and when it's raining, and the voltage will always be low. If the voltage reaches the minimum threshold value, the wiper will do its job at the maximum speed.

The payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. An input-shaping controller was implemented on a large bridge crane at the Georgia Institute of Technology to reduce crane payload oscillation. The crane was used to study the performance of human operators as they drove the crane through obstacle courses. An image processing system was implemented to track the movement of the crane payload. Data from these experiments show that operators performed manipulation tasks faster, safer, and more effectively when input shaping was utilized to reduce payload sway.

In this work we present a methodology to design the next generation of real-time vision processors. These processors are expected to achieve high throughput with complex applications, under real-time embedded constraints (time, fault-tolerance, silicon area and power consumption). To achieve these goals, we propose the fusion of two key concepts: the Focal-Plane Image Processing (FPIP) and the Many-Core architectures. We show the concepts and ideas to build-up a methodology able to offer both design space exploration, and a customized programming toolchain for the final architecture. We present implementation details and results for working parts of the framework, and partial results and general comments about the work-in-progress.

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We propose a new RoboCup league called "SSL-Humanoid". In the SSL-Humanoid league, the global vision system used in the RoboCup soccer Small Size Robot League (SSL) is also employed, and two teams of up to five humanoid robots play on a field whose size is half of the SSL field. This league aims to advance AI research for humanoid robots such as cooperation among robots, tactics and strategies. This contribution is possible due to the substitution of local to a global vision system. This is expected to attract new participants who have been interested in RoboCup but did not attend so far. We propose to organize this league as a sub-league of the original SSL. In this paper, we describe background of the proposal, research topics, road map toward 2014 and a summary of games played in 2009. Finally, we introduce team ODENS' system as an example of an SSL-Humanoid team.

Two key features of successful multi-robot systems in RoboCup are robustness of a vision system and optimization of feedback control in order to reach the goal point and generate the action under the team strategy. This paper proposes two new methods. One is a fast image processing method, which is coped with the spatial variance of color parameters in the field, to extract the positions of robots and ball in 1/30 sec. The separation problem in the interlaced format image is solved. Another one is a path generation method in which the robot approaches the goal by changing its direction convergently. With these two algorithms, the real time processing system is realized by generating a stable path under a low quality input image.

Dissertação apresentada ao Instituto de Ciências Matemáticas e de Computação, como parte dos requisitos para a obtenção do título de Mestre em Ciências da Computação e Matemática Computacional. Aos meus colegas do laboratório LCR, por terem sido companheiros e amigos e principalmente ao colega Adriano K. Sanches por ter participado da fase de testes do sistema apresentado neste documento. Ao meu orientador e amigo Prof. Dr. Eduardo Marques, por ter sido conselheiro tanto nas decisões relacionadas a vida acadêmica, como também, nas decisões referentes ao meu futuro profissional. À minha família e principalmente a Ivete, pela sua compreensão e paciência que teve durante o desenvolvimento deste trabalho. A mente que se abre a uma nova idéia jamais voltará ao seu tamanho original... (Albert Einstein) Este trabalho propõe um módulo básico de aquisição e pré-processamento de imagem colorida aplicado a robôs móveis, implementado em hardware reconfigurável, dentro do conceito de sistemas SoC (System-on-a-Chip). O módulo básico é apresentado em conjunto com funções mais específicas de pré-processamento de imagem, que são utilizadas como base para a verificação das funcionalidades implementadas no trabalho proposto. As principais funções realizadas pelo módulo básico são: montagem de frames a partir dos pixels obtidos da câmera digital CMOS, controle dos diversos parâmetros de configuração da câmera e conversão de padrões de cores. Já as funções mais específicas abordam as etapas de segmentação, centralização, redução e interpretação das imagens adquiridas. O tipo de dispositivo reconfigurável utilizado neste trabalho é o FPGA (Field Programmable Gate Array), que permite maior adequação das funções específicas às necessidades das aplicações, tendo sempre como base o módulo proposto. O sistema foi aplicado para reconhecer gestos e obteve a taxa 99,57% de acerto operando a 31,88 frames por segundo.

The ultimate goal is to create an extraterrestrial unmanned system for subsurface mapping and exploration. Neural networks are to be used to recognize anomalies in the profiles that correspond to potentially exploitable subsurface features. The ground penetrating radar (GPR) techniques are likewise identical. Hence, the preliminary research focus on GPR systems will be directly applicable to seismic systems once such systems can be designed for continuous operation. The original GPR profile may be very complex due to electrical behavior of the background, targets, and antennas, much as the seismic record is made complex by multiple reflections, ghosting, and ringing. Because the format of the GPR data is similar to the format of seismic data, seismic processing software may be applied to GPR data to help enhance the data. A neural network may then be trained to more accurately identify anomalies from the processed record than from the original record.

Now a days manufacturing companies are working on the sustainable and smart manufacturing systems. In this paper sustainable manufacturing initiatives is the creation of energy-efficient and manufacturing resources-saving systems. Distributed Decentralized networks of adaptable and flexible mini-factories are not only helpful to reduce emissions through reduction of transports, but also serve for the growth and development of regional economic cycles. This work also makes an attempt at exploring the role of robotics in achieving sustainability in manufacturing. Its describes the advancement made so far in applying robots to manufacturingwelding shot blasting, painting, and so forth, within the sustainability framework. The adoption of new technologies like Robotic Operating Systems (ROS), in the approaches to the design of industrial robots, could enhance sustainability in the use of robots in manufacturing at a reduced cost. This paper gives an overview of trends towards DMS as wel...

The importance of chromatic correlations in representing chromato-structural images is investigated in this paper. A vision system consisting of two information channels, i.e., luminance and chrominance, is formed for the description of an image in both the xyY and HIS color spaces. Features from the correlation directional histograms are extracted. Chromatic correlations show a particularly robust behavior based on statistical redundancy tests. Classification results on a color texture set of 25 images in both the xyY and HIS color spaces show the discriminatory power of such features.

Pan-tilt-verge (PTV) vision system is one of the most widely used in active vision. The main advantage of using such system is its 4 DOF which allows tracking of moving objects efficiently. Besides a physical design of the head, an overall tracking performance of the system depends on its controller. This paper presents a development of controlling PTV head to achieve one of human-like eye movement behaviors, i.e. saccade. The PTV head is driven directly from a controller using visual feedback. The dynamic Jacobian estimation is obtained by using a self-organizing map network with unsupervised learning scheme. The estimated Jacobian is used in PTV head controller and results are desirable for both performance and speed of learning. Moreover, the system can eventually perform tracking without a priori knowledge of the head structure, e.g. mathematical model of the head and hardware calibration. Thus the system can conveniently be implemented.

Pan-tilt-verge (PTV) vision system is one of the most widely used in active vision. The main advantage of using such system is its 4 DOF which allows tracking of moving objects efficiently. Besides a physical design of the head, an overall tracking performance of the system depends on its controller. This paper presents a development of controlling PTV head to achieve one of human-like eye movement behaviors, i.e. saccade. The PTV head is driven directly from a controller using visual feedback. The dynamic Jacobian estimation is obtained by using a self-organizing map network with unsupervised learning scheme. The estimated Jacobian is used in PTV head controller and results are desirable for both performance and speed of learning. Moreover, the system can eventually perform tracking without a priori knowledge of the head structure, e.g. mathematical model of the head and hardware calibration. Thus the system can conveniently be implemented.

The characterization and classification of certain phenomena that occur inside glass furnaces is of the outmost importance for the design of efficient and energy-saving control strategies. This paper describes a computer vision system dealing with both flame analysis and classification problems. The vision system performs classification tasks, assigning each visualized flame to a previously established class of flames. Each flame class corresponds to a specific operating point of the furnace, and this information can, therefore, be useful not only for monitoring purposes but also for control purposes of the whole furnace. Two different classifiers were designed and compared. One exploits a Bayesian formulation, and the other is based on neural networks. The system was first tested in a laboratory environment, where different operating conditions were created through the variation of the burners geometry, the number of active burners, and the rate of fuel used. In a second stage, the vision system was tested in an industrial environment. The results obtained are presented and discussed.

Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.

Vision systems for various tasks are increasingly being deployed. Although significant effort has gone into improving the algorithms for such tasks, there has been relatively little work on determining optimal sensor configurations. This paper addresses this need. We specifically address and enhance the state-of-the-art in the analysis of scenarios where there are dynamically occuring objects capable of occluding each other. The visibility constraints for such scenarios are analyzed in a multi-camera setting. Also analyzed are other static constraints such as image resolution and field-of-view, and algorithmic requirements such as stereo reconstruction, face detection and background appearance. Theoretical analysis with the proper integration of such visibility and static constraints leads to a generic framework for sensor planning, which can then be customized for a particular task. Our analysis can be applied to a variety of applications, especially those involving randomly occuring objects, and include surveillance and industrial automation. Several examples illustrate the wide applicability of the approach.

for analysis of dynamic objects such as humans is challenging due to occlusions and similarity in the appearance of a person with the background and other people-visual "confusion". Since occlusion and confusion depends on the presence of other people in the scene, it leads to a dependency structure where there are often loops in the resulting Bayesian network. While approaches such as loopy belief propagation can be used for inference, they are computationally expensive and convergence is not guaranteed in many situations. We present a unified approach, COST, that reasons about such dependencies and yields an order for the inference of each person in a group of people and a set of cameras to be used for inferences for a person. Using the probabilistic distribution of the positions and appearances of people, COST performs visibility and confusion analysis for each part of each person and computes the amount of information that can be computed with and without more accurate estimation of the positions of other people. We present an optimization problem to select set of cameras and inference dependencies for each person which attempts to minimize the computational cost under given performance constraints. Results show the efficiency of COST in improving the performance of such systems and reducing the computational resources required.

This paper describes the implementation of behaviour for real-time visual servoing on a mobile robot. The behaviour is a component of a multi-robot cleaning system developed in the context of our investigation into architectures for cooperative systems. An important feature for support of cooperation is the awareness of one robot by another, which this behaviour realises. Robust feature tracking aided by a hardware vision system is described. This forms the basis for range and pose estimation using a 3D projective model. 1. INTRODUCTION As part of our research into behaviour architectures for cooperative multi-robot systems, we are constructing a cleaning system using two autonomous mobile robots [Jung 97]. There are numerous component behaviours implemented to support the basic cleaning task. Among them is the requirement for one robot to be aware of the other. This is realised by visual tracking, and estimating the range and pose, of the robot in its field of view in realtime. Thi...

The development of vehicular electronic vision systems for the automotive market is a growing field, driven in particular by customer demand to increase the safety of vehicles both for drivers, and for other road users, including Vulnerable Road Users (VRUs), such as pedestrians. Close-range automotive camera systems are designed to display the areas in the close vicinity of the vehicle to the driver, typically covering the blind-zones of the vehicle. Customer demand is matched by legislative developments in a number of key automotive markets; for example Europe, Japan and the United States are in the process of introducing legislation to aid in the prevention of fatalities to vulnerable road users, with emphasis on the use of vision systems. In this paper we discuss some of the factors that have promoted the introduction of this legislation. We show also that, by the use of wide-angle camera systems, these legislative requirements can be met.

An approach for designing a hybrid parallel system that can perform different levels of parallelism adaptively is presented. An adaptive parallel computer vision system (APVIS) is proposed to attain this goal. The APVIS is constructed by integrating two different types of parallel architectures, i.e. a multiprocessor based system (MBS) and a memory based processor array (MPA), tightly into a single machine. One important feature in the APVIS is that the programming interface to execute data parallel code onto the MPA is the same as the usual subroutine calling mechanism. Thus the existence of the MPA is transparent to the programmers. This research is to design an underlying base architecture that can be optimally executed for a broad range of vision tasks. A performance model is provided to show the effectiveness of the APVIS. It turns out that the proposed APVIS can provide significant performance improvement and cost effectiveness for highly parallel applications having a mixed s...

An automated vision system, Teratom-Eye, was developed for the identification of three representative tissue types: muscle, gut and neural epithelia which are commonly found in teratomas formed from human embryonic stem cells. Muscle tissue, a common structure was identified with an accuracy of 90.3% with high specificity and sensitivity greater than 90%. Gut epithelia were identified with an accuracy of 87.5% with specificity and sensitivity greater than 80%. Neural epithelia which were the most difficult structures to distinguish gave an accuracy of 47.6%. TeratomEye is therefore useful for the automated identification of differentiated tissues in teratoma sections.

A vision based approach for calculating accurate 3D models of the objects is presented. Generally industrial visual inspection systems capable of accurate 3D depth estimation rely on extra hardware tools like laser scanners or light pattern projectors. These tools improve the accuracy of depth estimation but also make the vision system costly and cumbersome. In the proposed algorithm, depth and dimensional accuracy of the produced 3D depth model depends on the existing reference model instead of the information from extra hardware tools. The proposed algorithm is a simple and cost effective software based approach to achieve accurate 3D depth estimation with minimal hardware involvement. The matching process uses the well-known coarse to fine strategy, involving the calculation of matching points at the coarsest level with consequent refinement up to the finest level. Vector coefficients of the wavelet transform-modulus are used as matching features, where wavelet transform-modulus maxima defines the shift invariant high-level features with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps leading to the creation of accurate depth perception model.

In this paper a new type of parallel computer architecture dedicated to real-time vision is proposed. The proposed architecture is based upon the Beowulf concepti.e. COTS computing nodes interconnected by a high-speed network and running standard, free software tools -but also takes into account the constraints of embedded vision systems, such as "on the fly" processing of video streams, small volume, and low power consumption. Its main originality lies in the presence of two separate communication media: a dedicated bus for fast video i/o and a standard, switched inter-connection network for inter-process communication. Another distinctive point is the use of a highlevel parallel programming model, based upon algorithmic skeletons. In order to assess the validity of the proposal, a first prototype has been built and is described in the paper. It uses G4 motherboards coming from Apple Cube mass market computers, a Fast Ethernet communication network and a FireWire (IEEE-1394) video bus. Results of preliminary benchmarks are presented.

In this paper, a computer vision system, according to digital image processing methods to recognize car license plate is described. Traffic images are captured by a video camera and digitized in to a computer. Vertical and horizontal line scanning is used. The gray scale in horizontal and vertical lines and a treshold help to find the coordinate of the plate.

This paper presents an alternative approach to constructing computer experiment designs based on stochastic process theory, specifically marked point processes with two types. The originality of this method lies in the use of Markov Chain Monte Carlo (MCMC) techniques, combined with the Metropolis-Hastings algorithm and a new dynamic called local shift dynamics. These tools enable the generation of highly flexible and adaptable experimental designs, which can be tailored to a variety of specific objectives according to experimental needs. Special attention has been given to analyzing the convergence of the Markov chain, thus ensuring the robustness and efficiency of the results obtained. Additionally, a comparative study was conducted to position our method relative to other existing computer designs. This comparison highlights the advantages and disadvantages of our approach in terms of modularity and performance.

This paper presents an overview of investigations into the role of computer vision technology in developing safer automobiles. We consider vision systems which can not only look out of the vehicle to detect and track roads, avoid hitting obstacles or pedestrian, but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer vision technology for safer automobiles is presented. We will consider three main components of the system, driver, vehicle, and vehicle surround. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. The paper includes discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent and activity patterns of drivers.

This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computervision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers.

Vision systems employing region segmentation by color are crucial in applications such as object tracking, automated manufacturing and mobile robotics. Traditionally, systems employing realtime color-based segmentation are either implemented in hardware, or as very specific software systems that take advantage of domain knowledge to attain the necessary efficiency. However, we have found that with careful attention to algorithm efficiency fast color image segmentation can be accomplished using commodity image capture and CPU hardware. This paper describes a system capable of tracking several hundred regions of up to 32 colors at 30 Hertz on general purpose commodity hardware. The software system is composed of three main parts; a color threshold classifier, a region merger to calculate connected components, and a separation and sorting system to gather various region features and sort them by size. The algorithms and representations will be described, as well as descriptions of three applications in which it has been used.

This section focus on the three main areas involved on the implementation of the system, and therefore presents some of the most common used methodologies for video/image segmentation and for parallel processing as well as an overview of systems that are able to detect and track players in indoor team sports. Video and inherently image segmentation is the first step and probably the most critical step in any vision system. Video segmentation can be subdivided into temporal segmentation and spatial segmentation. Temporal segmentation corresponds to segmenting the video into meaningful temporal sequences. This kind of segmentation is usually used as the first step of video annotation and tries to segment the video taking into account similarities/dissimilarities between successive frames . On the other hand, spatial segmentation analyses the content of each frame, and divides it into homogeneous regions that correspond to independent objects. The focus of this work is more on spatial segmentation, therefore for a detailed survey on temporal video segmentation please refer to . Spatial video segmentation may be performed using the methodologies that are used for image segmentation and further enhanced using the temporal characteristics of video. In addition, when performing colour analysis there is also the need to choose a colour space. Regarding colour image segmentation, a detailed survey is provided by . As they state, most of the existing colour image segmentation methodologies have their origins on grey scale image segmentation with the addition of a proper colour space choice. The main categories of image segmentation methodologies ) are summarized on Table . Nowadays there is the tendency to apply techniques from different categories in order to achieve better results. A good example of this tendency is the JSEG algorithm which initially clusters colours into several representative classes, afterwards replaces each pixel by their corresponding colour class label and at the end employs a region growing process directly to the class map in order to identify homogeneous regions. On videos, contrary to static images, besides the physical (x and y coordinates) and colour information there is also the time component. Using this property it is possible to segment images based on motion along time. There are two main approaches to perform this task: background subtraction and optical flow. Background subtraction is usually used in situations where a more or less fixed background exists and subdivides the image into foreground and background regions. Several background subtraction techniques have been proposed in literature. The main issue on these methods is to obtain a good estimate of the background. The simplest method to model the background is to use a single static image without objects, however its efficiency is low, because it does not take into account changes such as light effects or shadows that may occur in the background. More robust methods include estimating the background model using a moving average , median or even a mixture of Gaussians .

We describe the vision system for Alvin, the Autonomous Land Vehicle, addressing in particuly the task of road-following. The system builds symbolic descriptions of the road and obstacle boundaries using both video and range sensors. Road segmentation methods are described for video-based road-following, along with approaches to boundary extraction and the transformation of boundaries in the image plane into a vehicle-centered three dimensional scene model. The ALV has performed public roadfollowing demonstrations, traveling distances up to 4.5 Ian at speeds up to 20 h / h r along a paved road, equipped with an RGB video camera with padtilt control and a laser range scanner. In May of 1985. "Alvin". the Autonomous Land Vehicle at Martin Marietta Denver Aerospace, performed its first public road-following demonstration. In the few months leading up to that performance, a basic vision system was developed to locate roads in video imagery and send three dimensional road centerpoints to Alvin's navigation system. Since that first demonstration, VITS (for Vision Task Sequencer) has matured into a more general framework for a mobile robot vision system, incorporating both video and range sensors and extending its road-following capabilities to include obstacle detection and avoidance. A second public demonstration in June 1986 showed the improved road-following ability of the system, allowing the ALV to travel a distance of 4.5 km at speeds up to 10 km/hr, handle variations in road surface, and navigate a sharp, almost hairpin, m e . In October 1986 the initial obstacle avoidance capabilities were demonstiated, as Alvin steered around obstacles while remaining on the road, and vehicle speeds up to 20 km/hr were demonstrated on an obstacle-free portion of road. This paper describes Alvin's vision system and addresses the particular task of video road-following. Video obstacle detection and range-based road following and obstacle avoidance

Colour harmony is typically assessed using a neutral (e.g. grey) background, but little is known about the effect that background colour has on the perception of saliency and harmony. A series of experiments has been conducted to assess the effects of lightness, hue angle and chroma difference on observer perceptions of saliency and harmony for an array of single and dual foreground colour patches viewed against numerous backgrounds. Experiments enabled observers to cluster perceptions of saliency and harmony as a function of variable background colour based on a 5-point semantic-differential scale running from a strongly harmonious to strongly disharmonious continuum. Results indicate that each background colour has the potential to create either high or low saliency and harmony ratings depending on the specific foreground colour/s viewed against it. This suggests that background colour is intimately linked with observer perceptions of saliency and harmony and should be seriously considered in future work as a further dimension and determinant of colour harmony. Results seem to raise questions on already existing 'harmonious' colour combinations that have been proposed to date. With a simple change in background colour, the possibility of creating a disharmonious from a formerly termed harmonious combination seems highly likely. The reverse holds true.

Many industrial tasks require sophisticated vision interpretation, yet demand low cost, high speed, accuracy, and flexibility. To be fully effective, future machine vision systems must be able to handle complex industrial parts. This includes verifying or recognizing incoming parts, and determining the location and orientation of the part within a short cycle time. It is generally required that the cycle time required by the part‐feeding system be less than that of the subsequent processes where parts are fed. To achieve this goal, a Flexible Integrated Vision System (FIVS) has been developed. FIVS offers performance and cost advantages by integrating the imaging sensor, control, illumination, direct digitization, computation, and data communication in a single unit. © 1994 John Wiley & Sons, Inc.

In our previous studies into web design, we found that pens, paper, walls, and tables were often used for explaining, developing, and communicating ideas during the early phases of design. One practice was to produce information architectures using paper on walls. This practice inspired us to work towards The Designers' Outpost, a tangible user interface that combines the affordances of paper and large physical workspaces with the advantages of electronic media to support information design. In this paper, we describe a series of participatory design studies that explored the combination of physical and electronic media in depth. These three studies employed both low-and high-fidelity prototypes. The design teams in our studies encouraged us to support free ink electronic annotations to sitemap pages, versioning of design artifacts, fluid transitions to other tools, and opportunities for collocated and remote collaboration. In parallel, we built a set of prototypes for the underlying vision system. These prototypes led us to difference imagebased recognition algorithms and a two-camera infrastructure: a rear-mounted video camera for capturing movement, and a front-mounted high-resolution camera for capturing ink.

Active Learning Environments with Robotic Tangibles (ALERT) are mixed reality video gaming systems that use sensors, vision systems, and robots to provide an engaging experience that may motivate hitherto underrepresented kinds of learners to become interested in game design, programming, and careers in science, technology, engineering, and mathematics. Through the use of fiducials (i.e., meaningful markers) recognized by robots through computer vision as just-in-time instructions, users engage in spatially-based programming without the encumbrances of traditional procedural programs' syntax and structure. Since humans, robots, and video environments share many inherently spatial qualities, this natural style of physical programming is particularly well suited to fostering playful interactions with mobile robots in dynamic video environments. As these systems broaden the capabilities of video game technology and human-robot interaction (HRI) they are lowering many existing barriers to integrated videorobot game development and programming. Diverse ALERT video game scenarios and applications are enabling a broad range of gamers, learners, and developers to generate and engage in their own physically interactive games.

Malaysia's oil palm industry faces many challenges in sustaining manual oil palm harvesting operations. This work investigates an effective oil palm Fresh Fruit Bunch (FFB) image processing method for robot harvesting automation. This research explores the proposed image processing method that first detects the Fresh Fruit Bunch (FFB) category which involves 6 different categories of FFB growth stages and then detects its 6D pose estimation for harvesting. Next, this research proposes a novel image processing framework that utilises the convolutional neural network deep learning classification and is followed by markerless feature-registration-based oil palm FFB for 6D pose estimation with the public FFB dataset. Furthermore, this work introduced view obstruction to the public FFB dataset as noise for practical robot harvester applications in plantation field operation. Moreover, the experiment results show the proposed model can maintain a high F1 score performance up until 70% of view obstruction before the F1 score performance is reduced.

The availability of sensorial data is a major drawback for mobile robot navigation systems. For a mobile robot under construction at the Department of Electrical Engineering of the Federal University of Espirito Santo, Brazil, an agent-based control system is being implemented, for which ultrasonic and vision systems are being developed. This sensing system is able to provide the information on the type of detected obstacle as well as on the distance from it to the robot. By using agent modeling, it was possible to design an external sensing system with the capability of avoiding the generation of redundant or unnecessary information, as well as to decide if an information is relevant or not.

The quality of the pharmaceutical powder mixing strongly influences the potency and properties of the tablets. Furthermore, in order to optimize powder mixing processes, fast and accurate methods are sought for determining the concentration of powder mixtures. This work describes an optical method based on an image processing system to determine concentration of powders in a mixture that can be sensed by its gray-level values. The methodology was developed utilizing univariate and multivariate gray image analysis. The effect of difference in mean gray value (DMGV) on the precision and accuracy of the vision system was determined. The results that were obtained from the vision system were compared to those obtained with near-infrared spectroscopy. The multivariate gray image analysis methodology using frequency of each gray-level value (U) of gray scale as the independent variables obtained the best predictions. The vision system produced high precision with a standard deviation of about 1.7% w/w and a 2.6% relative standard deviation. The accuracy indicated by the root mean standard error of prediction (RMSEP) was around 1.7% w/w. The mixtures analyzed with the vision system and the near-infrared spectrometer yielded similar results. Analysis of all results confirms that the vision system is an adequate technology for determination of powder mixture concentrations.