Imaging and analysis of traffic scene

. 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.

Directly performing optical analog computations and image processing in space, such as optical differential operations and image edge detection, is a burgeoning area. To avoid the bulkiness and low efficiency of traditional 4 f filtering systems, one can utilize Green's function and metasurfaces for advanced wavefront control. However, some metasurface differentiators can be hindered by issues like polarization sensitivity, restricted bandwidth, low resolution, and the need for additional polarization devices or digital post‐processing, potentially degrading their performance and operation efficiency. In this work, a dual‐polarization Laplace differentiator is engineered to address these issues based on nonlocal hollow metasurface. The optical transfer function (OTF) required by the Laplace operation can be obtained by exciting different quasi‐bound states in the continuum (Q‐BIC) modes with distinct angular dispersion capabilities under p ‐ and s ‐polarized illumination, respectively. This Laplace differentiator not only directly realizes 2D second‐order edge detection in a dual‐polarization channel but also features a numerical aperture (NA) with an upper limit close to 0.42 and a broadband range reaching 165 nm. Such an efficient, high‐quality dual‐polarization and bandwidth image edge detection approach offers powerful imaging techniques for applications in machine vision, microscopic imaging, and image processing.

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.

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.

Massively deployed inside Sony PS3 platforms, the STI Cell Broadband Engine is a multi-core processor with a PowerPC host processor (PPE) and 8 synergic processor engines (SPEs). In this paper, we describe three image processing applications which we implemented on the Cell BE. We report the performance measured on one Cell blade with varying numbers of synergic processor engines enabled, and with varying application parameters. These results were compared to the results obtained on the Cell BE running a single PPE and with all 8 SPEs disabled. The results reveal the superior performance of image processing and SIMD-prone applications on the 9-core Cell BE processor over conventional RISC processors.

In today’s digital world, image processing plays a crucial role in various aspects of human life. From creating visual content for social media to analyzing medical images, powerful tools for image manipulation are in constant demand. The growing requirements for image quality and processing speed make the search for efficient methods to accelerate these processes highly relevant. This paper investigates the development and implementation of a graphics editor that utilizes OpenMP parallel computing to accelerate data processing. This technology enables the efficient distribution of computational tasks across multiple processor cores, significantly improving system performance. The developed software offers a set of popular graphic effects, including negative, grayscale, sepia, blur, sharpening, and posterization. Each effect is implemented in both sequential and parallel modes using OpenMP, allowing for the comparison of different computational approaches. To evaluate the effectiveness of the proposed solution, a series of experiments were conducted on images of various sizes. These experiments involved applying each effect to images ranging from small icons to high-quality photographs. A comparative analysis of the efficiency of sequential and parallel computation methods demonstrated the significant advantages of the latter. The results of the study show a substantial acceleration of image processing when using OpenMP technology. This acceleration is particularly noticeable for computationally intensive effects such as blurring or sharpening, and when working with large images. In some cases, it was possible to achieve a significant increase in processing speed, opening up new possibilities for working with large volumes of graphic data. This research has significant practical value for software developers working on optimizing the performance of graphics editors and other image processing applications. It demonstrates how the application of modern parallel computing technologies can significantly improve the efficiency of working with graphic data, paving the way for the creation of more powerful and faster image processing tools.

A Wavelet Transform (WT) is a mathematical technique that transforms a signal into different frequency components, each analyzed with a resolution that matches its scale. Wavelets are small waves with limited duration and they possess both time and frequency localization, which means they can capture both high-frequency and low-frequency information simultaneously. There are several types of wavelet transforms, and, depending on the application, one may be preferred to the others. For a continuous input signal, the time and scale parameters can be continuous leading to the Continuous Wavelet Transform (CWT) and Discrete wavelets. Image processing is a critical area in modern computing with applications ranging from medical imaging to remote sensing and computer vision. Traditional Fourier analysis has limitations in capturing localized features in images due to its global nature. Wavelet Transform (WT), with its multi-resolution and localized time-frequency representation, provides a powerful alternative for analysing and processing visual information. This paper presents a comprehensive overview of the theory of wavelet transforms, their mathematical foundations, and applications in image processing and signal processing, including compression of images, denoising and feature extraction from images.

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

There is a large amount of sustained research activity in the area of Human-Computer Interaction (HCI). Gesture control is one of hottest in this field. Although gesture control research began with terminals attached to computers in combination with a pointing device, the large scale implementation and utilization of gesture control continues to be infeasible today. As machine vision, image processing, and artificial intelligence algorithms are error prone, gestures will never be interpreted in the same way for all instances of the gesture's appearance, especially due to changes of the ambient light. This latter condition led to the use of near infrared (NIR) illumination such that the recorded scene is not affected by light variations. Since the posture of a gesture may require the processing of overlapping features, reliability is greatly improved through the use of images that contain a third dimension. In this paper, a smart and real-time depth camera based on a new depth generation principle is introduced. A monotonic increasing and decreasing function is used to control the frequency and duty-cycle of the NIR illumination pulses. The adjusted light pulses reflect off of the object of interest and are captured as a series of images. A reconfigurable hardware architecture calculates the depth-map of the visible face of the object in real-time from a number of images. The final depth map is then used for gesture detection, tracking and recognition. A series of tests and measurements will explain how the camera builds the depth map and how it can operate in both near and far ranges. Results on controlling video game consoles and Smart TVs using the above camera will be given.

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.

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.

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.

Digital imaging systems are increasingly popular in various fields such as education, industry, engineering, and healthcare. The ease of use and low cost of these systems contribute to their widespread adoption. However, the main disadvantage of digital imaging is resolution issues. In practical applications that require high resolution, dense sensors are used to obtain robust images. This method, however, increases costs and produces more data and noise due to its density. Additionally, millions of low-resolution but valuable pieces of information are lost. Image processing techniques are used to enhance resolution and preserve high-frequency information. The primary aim of this study is to comprehensively investigate the importance and effectiveness of using partially linear models in image processing applications. Partially linear regression aims to offer a new model for image enhancement without losing high-frequency information. Because many problems encountered in the field of image processing stem from resolution issue, this study aims to understand the effects of resolution on image processing processes and to demonstrate how partially linear models can be used to address these effects. Various comparison methods have been used to evaluate the effectiveness of the proposed method. These methods have been employed to objectively assess the quality difference between images, highlighting the superiority of the proposed method over traditional methods. The study's findings show that partially linear models are a significant tool in image processing applications. Future studies may aim to examine in more detail how these models perform with different types of images and conditions.

ROSUS 2020: Computer image processing and its application in Slovenia 2020. ROSUS 2020–Computer image processing and its application in Slovenia 2020 is a professional conference that, since 2006, has been organised every year by the Institute of Computer Science of the Faculty of Electrical Engineering and Computer Science, University of Maribor. The conference connects researchers in the fields of Image Processing and Machine Vision with users of this knowledge, whereby users are coming from diverse industrial environments, such as Biomedicine, Sport, Entertainment, and related fields. The proceedings of ROSUS 2020 combine scientific articles by dozens of authors, including two invited lectures and several demonstration articles. Contributions represent the latest achievements of Slovenian experts in the fields of Image Processing and Vision, and also highlight trends and novelties in these areas. Great emphasis is on promotion of the economic usefulness of Image Processing and Vision applications in the Slovenian region. Namely, such software, due to high precision, robustness, and exceptional speed in information processing, provides   new   opportunities   for   penetration   on   the   high