Multiple Sensor Integration/Fusion Through Image Processing: A Review

The motion of three-dimensional objects in an industrial environment is monitored using a sequence of stereo-images taken from the same point of view. Mechanical vibrations of the camera system cause an apparent motion of objects in space, which can be compensated for by determining the relative rotation and translation of images captured at different time instances. Images are partitioned into square sections, and section correspondence is sought between consecutive frames in order to establish the image rotation and translation parameters. Tite segmented image disparity between consecutive frames determines the image area over which actual motion is observed and simplifies the tasks of image segmentation and obtaining the correspondence of object features over time and also in a stereo image pair. The obtained space coordinates of object feature points are combined in order to estimate the object motion parameters and predict its future course. The method is computationally fast and can be used to track fast moving objects in a semi-structured environment.

A three-dimensional (3D) laser scanning system is the main part of reverse engineering. It is a comprehensive technological system, including numerical control (NC), a laser, computer-aided design/manufacturing (CAD/CAM), precision mechanisms, computing image sampling, and image processing. Generally, it consists of a motion control system, an image sampling system, and an image processing system, etc. This paper focuses on introducing control principles, hardware constitutions, and software algorithms of the servo motion system, and analyzes and presents the hardware constitutions, software algorithms, and experimental results of the image sampling and processing system in great detail. Finally, the authors come to the conclusion that the errors all occur within the range ±0.1 mm.

Edge detection is a fundamental step in many machine vision or image processing applications and systems. The importance of edge detection increases as one seeks to increase the level of automation in the image processing system. The paper reports an edge detection operator which is fast in application to grey scale images and is sensitive to 'difficult' edges but not sensitive to artifacts such as scanner interlacing artifacts. The edge detection operator presented is simple but provides good detection of edges and clean and continuous edges across most of the image.

Through continued miniaturization of electronic devices embedded smart cameras are steadily becoming more and more important. The reduction of the camera size increases the spectrum of applications. In industrial applications the range of smart cameras spans from quality monitoring and position tracking to the calibration of production machines. In non-professional applications a distinct boom in action cameras combined with fused sensor information can be observed. However, all of these applications have a common bottleneck: the memory architecture. Most image processing applications are memory-bound tasks. Thus, the amount of time for transferring data with image processing applications decisively affects the application’s entire processing time. Different memory access patterns require different memory configurations and hierarchies. An insufficient match between the image processing application and the memory architecture leads to a poor performance in the image processing system. This can lead to longer processing times, and larger energy consumption rates. This work introduces new methods of classifying image processing applications by using their memory access pattern for mapping on memory architectures. Our work combines a simulation framework the heterogenous memory simulator with a analytical framework the memory analyzer to find bottlenecks inside the image processing application and aids in finding a suitable, application-specific memory configuration in terms of processing time and energy consumption.

The human visual system's ability to extract three-dimensional structure from a two-dimensional source is the key to automatic interpretation of structure from motion.

. The development of an Image Processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. Our major objective is to help IP experts reuse parts of their applications. A first work towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of our interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, we explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system’s implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application.

As modern image and video processing applications handle increasingly higher image resolutions, the buffering requirements between communicating functional modules increase correspondingly. The performance and cost of these applications can change dramatically depending on the implementation methods for FIFO buffers and the data delivery methods between modules. This paper introduces a new FIFO hardware mapping algorithm based on pointer-based token delivery from dataflow semantics for image and video processing applications. This approach significantly improves the performance of dataflow based implementation of image and video processing systems, and allows effective prediction of changes in performance and buffer memory requirements associated with changes in image resolution. Our pointer-based token delivery method allows indirect token delivery between actors by pointers in conjunction with use of a shared memory. Each pointer references a data block stored in the shared memory. In pointer-based token delivery, a buffer can be configured to be implemented as the combination of a small, fast FIFO and a larger, relatively cheap shared memory while providing an attractive trade-off between performance and hardware cost. We present the complete semantics of our pointer-based modeling method, systematic techniques for mapping representations using these semantics into efficient implementations, and experimental results that demonstrate the performance of the proposed pointer-based techniques.

With increasing applications of image processing in manufacturing, it has become necessary to provide industry with powerful as well as flexible means of adapting the technology to the solution of different kinds of tasks at hand. Besides the standard components of an image processing system, the availability ofvarious software tools to fill the gap between design, digitization, measurement and the manufacturing phases should be emphasized. The users could e.g., desire to transfer grabbed images into certain computer graphics data format for further processing. They may want to apply image processing for the measurement of machine parts interactively or automatically, or they may want to convert the pictures processd, e.g. with edge detectors, into some CAD data format, or even to generate a numerical control file to machine the parts directly from the image processing data. With this motivation, two software packages FIPS—Flexible Image Processing System, and IQIS— Integrated QUality Inspection System have been developed for manufacturing and quality inspection and have been used by industry for various purposes from simple image digitization and processing, luminance adjustment, data and file format conversion, generation of control files for numerical control in laser cuffing, to direct control of robot movement, and interactive as well as automatic machine part measurement.

Image segmentation is a very important aspect in the field of image processing. According to certain principles, it will be divided into a number of small regional disjoint. Small region is a sense sets of common attributes of the connectivity pixels. The so-called connectivity which refers to the fact that any two points in the pool existing a connectivity path between all belong to the set. Pulse coupled neural networks was proposed based on the brain simultaneously pulsed release phenomenon of the visual cortex of the cats, monkeys, and other animals, the similar input data can be outbreak synchronized through interrelated neurons. The digital image can be processed by pulse coupled neural networks, the pixels of space based approach and brightness can be made up to a group. Pulse coupled neural networks has a good nature in image processing applications, which can effectively for the various aspects of image processing, and it can be used in the image processing system. Experimental results show that the method can achieve better image segmentation and have a common applicability.

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

In machine vision–based image processing, machine vision products are used to convert the image of an object into image signals and then into digital signals for subsequent processing on a computer. Image processing is widely applicable in research fields such as biomedicine, remote sensing, industrial production, military production, and aerospace. This paper provides a detailed overview of the research status of image processing in the mining field and makes a comparative evaluation of some technologies and research directions. First, the application of image processing in the mining field is discussed in detail in the paper. Second, a literature review is conducted, using keywords and citation counts to determine the overall distribution of the published literature on this subject in terms of journals, countries, institutes, and authors. Finally, we analyze this topic in detail, put forward our ideas and what we learned from our analysis, and provide a summary. The analysis shows that image-processing technology is a hot research topic for future development. In addition, this paper proposes future research challenges and directions. The latest progress, development characteristics, and research prospects discussed in this paper will provide a useful reference for scholars who deeply study image processing in the field of mining.

We develop greedy algorithms to approximate the optimal solution to the multi-fidelity sensor selection problem, which is a cost constrained optimization problem prescribing the placement and number of cheap (low signal-to-noise) and expensive (high signal-to-noise) sensors in an environment or state space. Specifically, we evaluate the composition of cheap and expensive sensors, along with their placement, required to achieve accurate reconstruction of a high-dimensional state. We use the column-pivoted QR decomposition to obtain preliminary sensor positions. How many of each type of sensor to use is highly dependent upon the sensor noise levels, sensor costs, overall cost budget, and the singular value spectrum of the data measured. Such nuances allow us to provide sensor selection recommendations based on computational results for asymptotic regions of parameter space. We also present a systematic exploration of the effects of the number of modes and sensors on reconstruction error when using one type of sensor. Our extensive exploration of multi-fidelity sensor composition as a function of data characteristics is the first of its kind to provide guidelines towards optimal multi-fidelity sensor selection.

On efficient computation of rational {1,2}-pseudo-inverses for multivariable rational matrix-valued functions and their applications

Automation of white light photoelasticity by phase-shifting technique using colour image processing hardware