Metric Induced Morphological Operators on Intuitionistic Fuzzy Hypergraphs

This textbook collects a series of research papers in the area of Image Processing and Communications which not only introduce a summary of current technology but also give an outlook of potential feature problems in this area.The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in image processing and communications.The book is divided into two parts. Part I deals with image processing. A comprehensive survey of different methodsof image processing, computer visionis also presented. Part II deals with the telecommunications networks and computer networks. Applications in these areas are considered.In conclusion, the edited book comprises papers on diverse aspects of image processingand communications systems. There are theoretical aspects as well as application papers.

Mathematical morphology is a theory with applications in image processing and analysis. In a supervised approach to mathematical morphology, pixel values are ranked according to sets of foreground and background elements specified a priori by the user. In this paper, we introduce a supervised fuzzy color-based approach to color mathematical morphology that provides an elegant alternative to the support vector machine-based approach developed by Velasco-Forero and Angulo. Briefly, color elements are ranked according to the degree of truth of the proposition “the considered color is a foreground color but it is not a background color” in the new supervised color morphological approach. Furthermore, the vagueness and uncertainty inherent to the description of colors by humans can be naturally incorporated in the new approach using the concept of fuzzy colors.

. 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.

A class of processors which perform local neighborhood or cellular operations has well-documented applications in image processing. The architectural feature which unites these processors is their use of special-purpose hardware to compute image transformations based both on the values and on the spatial relationship of pixels in the input image. Typical operations include 3 x 3 convolution and mathematical morphology. Processing elements which compute cellular operations have been incorporated in a variety of system architectures. These range from single-processor, recirculating buffer systems to systems which have thousands of processors in a four-way interconnected mesh. Another feature which unites these processors is that they operate on images in the iconic domain. A new class of non-iconic processor is introduced here which uses image encoding schemes to reduce the number of operations required for certain classes of operations by between one and two orders of magnitude.

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.

Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.

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 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.

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

According to the characteristics of pavement crack images, this paper introduce mathematical morphology into the applications of road image analysis and processing, in addition propose a new method combined with the mathematical morphology to detect the road crack image. Experimental results show that the method can not only effectively suppress noise, but also clearly and accurately extract the edge of the image, and solve the problem of image edge's damage caused by the removal of noise. Enhance the connectivity of the fractures, sequentially further improve the system's detection accuracy.

The growing volumes of information available today provide opportunities but also challenges for social scientists. This paper presents textual network analysis—a network analysis procedure that transforms any given text into a visual map of words co‐occurring together. It aims at scholars and students who are not familiar with network analysis, showing step‐by‐step how to use this approach and highlighting its advantages and applications. It demonstrates how to identify the main themes appearing in the text as well as to detect its biases and frames. Researchers can use this procedure as a grounded content analysis to formulate theories or as a basis to test existing hypotheses. The second part of the paper presents two studies that applied textual network analysis: (a) to identify the main themes raised by elite newspapers on the “fake news” discourse and (b) to map the topics related to China on Twitter. Both examples show how textual network analysis can be relevant for communication, international relations, and political science scholars as well as for practitioners, wishing to understand the prevailing discourses and tailor their messages more effectively.

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.

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.

미디어 담론(media discourse)은 행위자들의 행동과 결과에 영향을 미친다는 점에서 중요한 의의를 가진다. 특히, 네트워크 분석(network analysis)을 이용한 미디어 담론 분석은 다양한 분야에서 논쟁이 되고 있는 사회적인 이슈의 구조적 특성을 나타내기 위해 사용되어 왔다. 하지만, 문화예술연구에서 네트워크 분석(network analysis)은 비교적 새로운 분석 방법으로, 최근 들어 문화예술분야의 연구 동향을 나타내기 위한 분석 방법으로 사용되고 있다. 본 논문은 네트워크 분석(network analysis)을 통해 미디어 담론(media discourse)의 지형을 나타내기 위한 사례로서, 한국 미술계의 위작(僞作), 대작(代作)사건과 관련된 미디어 담론(meida discourse)을 ‘프레임(frame)’을 중심으로 분석한다. 구체적으로는 한국 미술계의 위작(僞作), 대작(代作)논란을 다룬 사설·칼럼 83개를 대상으로 11개의 프레임(frame)을 부여하고, 11개의 프레임(frame)들을 대상으로 네트워크 분석(network analysis) – 1)11개 프레임(frame)간의 관계를 파악하기 위한 중심성(degree centrality, igenvector centrality), 등위성 분석(cluster) 2) 미디어 매체(media)의 이데올로기적 성향(ideological tendency)별로 11개 프레임(frame)에 대한 연결 중심성 분석(degree centrality analysis) –을 실시하였다. 이를 통해 미디어 매체(media)가 미술계의 위작(僞作), 대작(代作)에 대해 전체적으로 각 사건들에 대한 사실·갈등 양상을 나열하거나, 각 사건의 행위 주체의 책임을 묻는 등의 프레임(frame)을 중점으로 담론(discourse)을 형성하였음을 알 수 있었다. 이와 더불어 미디어 매체(media)의 이데올로기적 성향(ideological tendency)에 따라 한국 미술계의 위작(僞作), 대작(代作)논란을 바라보는 시각에 차이가 존재함을 알 수 있었다. 이와 같은 분석결과를 통해, 본 연구는 문화예술분야에서 비교적 새로운 분석 방법인 프레임 분석(frame analysis)을 통해 국내 미술계의 위작(僞作)·대작(代作)논란에 대한 미디어 담론(media discourse)을 파악하고, 이를 네트워크 지도(network map)를 통해 시각화하였다는 시사점을 가진다. 또한, 11개의 프레임(frame)을 대상으로 실시한 네트워크 분석(network analysis)을 통해 미술품 위작(僞作)·대작(代作)을 둘러싼 미술계 외부와 내부의 견해차를 확인함으로써, 향후 정부 부처가 이와 관련한 정책을 도출하는 과정에서 본 논문이 실증적 자료를 제공하는 것에 그 의의가 있다.Prior research suggests that media discourse affects the behavior and outcomes of relevant actors. In particular, media discourse analysis using text network analysis has been increasingly used to capture the structural properties of contentious social issues across various fields. The use of this method, however, has not been very active among researchers of arts and culture. In this paper, we present an analysis of media discourse related to art forgery in the Korean visual art sector as a test case to depict the topography of media discourse through text network analysis. We focus on three major art forgery scandals (e.g., Lee Woo Hwan, Chun Kyung ja, Jo Young Nam) and identify 8 major frames (conflict, blame attribution, context description, sensation, government intervention / institutional measures, market economy, power, morality) used in 83 opinion columns in the Korean daily newspapers as well as arts magazines about those instances from 1991 to 2017. We examine how theses frames that reflect the perspectives on art forgery are connected to one another so as to better make sense of how media discourses about art forgery in the Korean visual art sector are constituted.

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.