An Autonomous Helicopter with Vision Based Navigation

The paper is devoted to the problem of formalization of software-hardware solutions in designing real-time computer vision systems. The main attention is paid to the methods of implementation of low-level operations that find features (simple elements) in the image input to the system. Algorithmic types of detectors of simple elements in images are analyzed from the point of view of hardware organization in computer vision systems. In this connection, the necessary performance and memory resources are estimated. The capabilities of parallel and pipeline execution of detector algorithms are investigated. A method of using a field programmable gate array and a digital signal processor in solving the problem of image processing in real-time computer vision systems is considered in detail.

TOUCH is a real-time computer vision system that has been developed in order to aid the operation of autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). The algorithm is based on a modified 4-connectivity approach that scans the incoming frames for a target hue range and based upon the largest connected blobs of hue, tracks an object and provides the two dimensional Cartesian coordinates of the desired object. Since few algorithms exist for tracking and recognizing objects in underwater environments, this algorithm provides a way for AUVs and ROVs to acquire, track or recognize objects by using a relatively fast and inexpensive vision system. This paper presents the details on TOUCH, its testing, some areas that warrant improvement and possible course of action for the future.

Computer vision system for automated cell pressure probe operation

Programming a Pipelined Image Processor

This paper presents a data fusion system for a high performance aircraft. At the heart of the system is an extended Kalman filter which fuses data from distributed inertial sensors. The data fusion system provides high quality, high integrity data to both the flight control and navigation systems, and is consequently safety critical. The work also investigates a number of reconfiguration strategies that can be used to reconfigure the Kalman filter in the event of a sensor failure. The performance of this safety critical multi-sensor data fusion system in the event of sensor failures is evaluated through computer simulations, and same preliminary results are included.

The goal of this work is to implement a real-time computer vision system that can run on wearable devices to perform object classification and artwork recognition, to improve the experience of a museum visit through understanding the interests of users. Object classification helps to understand the context of the visit, e.g. differentiating when a visitor is talking with people, or just wandering through the museum, or if he is looking at an exhibit that interests him. Artwork recognition allows to provide automatically information of the observed item or to create a user profile based on what and how long a user has observed artworks.

A real-time data fusion system updating 3D organ shapes using color information from multi-directional cameras

Real-time computer vision systems that observe the actions of people have received increasing attention as they are important for a broad range of applications. Examples are intelligent man-machine interfaces or intelligent rooms that have the capability to understand the actions of their inhabitants. In this paper we present the design of a stereo vision system for the precise and robust real time tracking of human hands and heads that unlike previous systems uses low-cost industry standard components. The system uses color and motion information to find the human. Using previously calibrated cameras the 3D positions of hands and head are calculated. Because of its client-server architecture the system is scalable to an arbitrary number of cameras and application processes. Experimental results for the performance of the system are given.

Multi-sensors data fusion system for wireless sensors networks of factory monitoring via BPN technology

Data fusion has been a trend in the field of imaging and signal/image analysis. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. With this second edition, the authors have been successful in updating us with state-of-the-art methods and techniques in multisensor data fusion. The book involves both algorithms and software tools, and also covers contemporary subjects like smart agents, cognitive aides, and so on. This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems. Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their applications to solve real-world problems. However, to build a sophisticated data fusion system one may need other references for more comprehensive mathematical theories, more rigorous statistical treatments, and more technical details. The cited literature in the book represents appropriate pointers for that purpose. A good example for further reading is the Handbook [1]. As an engineer in biomedical imaging, I would like to underline the relevance of this book to research on systems biomedicine. Given the completion of the Human Genome Project [2] and the momentum of the Physiome Project [3], in 2003 the NIH defined its Roadmap to guide our efforts towards most important biomedical research, including studies on biological building blocks, pathways, and networks using molecular libraries, imaging, bioinformatics and computational biology. Consequently, multi-modality signal sensing and imaging plays a critical role, which often necessarily generates heterogeous sparse/huge datasets that are multi-dimensional, multi-spectral, dynamic, noisy and/or incomplete. The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in particular and other interested engineers in general.

To solve the problem of large accumulative errors of wheel odometer and inertial measurement unit (IMU), which make pose calculation mistakes due to friction coefficient of the ground or bumps and collisions, furthermore, affect path planning and navigation of automated guided vehicle (AGV), an extensible fusion localization method based on multiple sensors is proposed. Based on the traditional extended Kalman filter (EKF), the internal positioning data is used for prediction, while the external positioning data is used for correction. According to the states of different sensors, the adaptive weight method is applied to eliminate the influence of system accumulative error and provide continuous and accurate positioning data. The experimental results show that compared to single-sensor, the fusion localization method has an increase in quantity, which can reach an accuracy within 5~8cm. It can deal with non-line-of-sight (NLOS) landmark occlusion situations, and can navigate in dim light or dark environments. The system ensures the accuracy of fusion data through visual positioning and ensures the stability of the system through the odometer and IMU. New positioning sensors are also possible to be added into the system for data fusion.

One of the fundamental problems in computer vision is the background-foreground segmentation and most of the strategies have severe drawbacks when working with natural images, where there are extreme conditions such as illumination changes combined with sudden background differences or other noise; moreover if the system has to face real-time restrictions. In this case authors focus on a variation of the U-net architecture to obtain the segmentation of the objects (fishes) in every single frame. The U-net has some interesting properties to explore in the case of image segmentation, such as multi-scale parameter combination. The reported preliminary results, working in a context of a multi-target fish tracking are promising, and envisions an approach that could provide a real-time response to long-lasting experiments using HQ video for multi-target tracking in real-time Computer vision systems.

Local scale information extracted from visual data in a bottom-up manner constitutes an important cue for a large number of visual tasks. This article presents a framework for how the computation of such scale descriptors can be performed in real time on a standard computer. The proposed scale selection framework is expressed within a novel type of multi-scale representation, referred to as hybrid multi-scale representation, which aims at integrating and providing variable trade-offs between the relative advantages of pyramids and scale-space representation, in terms of computational efficiency and computational accuracy. Starting from binomial scale-space kernels of different widths, we describe a family pyramid representations, in which the regular pyramid concept and the regular scale-space representation constitute limiting cases. In particular, the steepness of the pyramid as well as the sampling density in the scale direction can be varied. It is shown how the definition of γ-normalized derivative operators underlying the automatic scale selection mechanism can be transferred from a regular scale-space to a hybrid pyramid, and two alternative definitions are studied in detail, referred to as variance normalization and lp-normalization. The computational accuracy of these two schemes is evaluated, and it is shown how the choice of sub-sampling rate provides a trade-off between the computational efficiency and the accuracy of the scale descriptors. Experimental evaluations are presented for both synthetic and real data. In a simplified form, this scale selection mechanism has been running for two years, in a real-time computer vision system.

Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.