Highly accurate real-time image steganalysis based on GPU

With the development of steganography, it is required to build high-dimensional feature spaces to detect those sophisticated steganographic schemes. However, the huge time cost prevents the practical deployment of high-dimensional features for steganalysis. SRM and DCTR are important steganalysis feature sets in spatial domain and JPEG domain, respectively. It is necessary to accelerate the extraction of DCTR and SRM to make them more usable in practice, especially for some real-time applications. In this paper, both DCTR and SRM are implemented on the GPU device to exploit the parallel power of the GPU and some optimization methods are presented. For implementation of DCTR, we first utilize the separability and symmetry of two-dimensional discrete cosine transform in decompression and convolution. Then, in order to make phase-aware histograms favorable for parallel GPU processing, we convert them into ordinary 256-dimensional histograms. For SRM, in computing residuals, we specify the computation sequence and spilt the inseparable two-dimensional kernel into several row vectors. When computing the four-dimensional co-occurrences, we convert them into one-dimensional histograms which are more suitable for parallel computing. The experimental results show that the proposed methods can greatly accelerate the extraction of DCTR and SRM, especially for images of large size. Our methods can be applied to the real-time steganalysis system.     

一种适用于GPU图像处理算法的合并存储结构

大多数图像处理算法都可利用GPU进行加速以达到更好的执行性能,但数据传输操作与核函数执行之间的调度策略问题仍是桎梏加速性能进一步提升的主要瓶颈。为了解决这个问题,通常采用GPU任务流将核函数执行与数据传输操作进行重叠,以隐藏部分数据传输与核函数执行耗时。但是,由于CUDA编程模型的特性以及GPU硬件资源的限制,在某些情况下,即使创建较多的任务流用于任务重叠,每个流上仍会存在串行执行的任务,导致加速效果无法进一步提升。因此,考虑利用CSS将待处理图像进行合并从而将单个流中的算子核函数及数据传输操作进行合并,以减少数据传输操作和核函数执行的固定代价及调用间隙。通过实验结果可知,提出的CSS结构不仅能在单流的情况下提高GPU图像处理算法执行性能,在多流的情况下其加速性能也得到了进一步提升,具有较好的实用性及可扩展性,适用于包含较多算子操作或较小尺寸图像批量处理的情况。此外,提出的方法对图像处理算法的GPU加速提供了新的研究思路。     

面向对象有限元并行计算框架PANDA

为提高我国工程数值模拟软件的开发能力,面向武器工程大规模并行计算需求,基于层次化、模块化及面向对象的现代软件设计技术,分析探讨有限元并行计算框架的设计方法,形成初步的基础开发框架PANDA 0.1版;详细描述PANDA框架的层次结构设计,并探讨该基础开发框架设计中的底层网格数据结构设计、有限元共性管理模块及并行策略等部分关键技术;简要介绍PANDA框架的扩展应用方法.研究表明：采用框架技术进行结构力学有限元程序开发,具有高效、快速和便捷等优点.     

A real-time digitizer for stereo image processing

This paper discusses the design and implementation of a stereo image digitizer for use in digital image processing applications. The system operates in real time with respect to the standard television refresh rate and it possesses several signal sampling, routing, and transformation options which can be controlled from an external digital computer. Results obtained with a prototype unit are illustrated and compared using precision test patterns and natural scenes.     

Special issue on real-time color image processing

Journal of Real-Time Image Processing -     

Object oriented framework to develop automated warehousing systems based on step

This paper deals with developing automated warehousing systems, from the best physcial structure design to the control and managing code generation and installation rules. An integrated object oriented modeling methodology and a number of tools, have been developed in order to automate the design process as much as possible, and to take advantage of the common features of this kind of installation, and also allowing the needed customization. Finally, the design process of a real warehouse project is presented. This system is now working at the ACL company in Valladolid (Spain), which feeds the Palencia (Spain) RENAULT car-assembly plant with car seat covers in a JIT environment.     

Embedded multi-spectral image processing for real-time medical application

The newly introduced Kubelka–Munk Genetic Algorithm (KMGA) is a promising technique for the assessment of skin lesions from multi-spectral images. Using five skin parameter maps such as concentration or epidermis/dermis thickness, this method combines the Kubelka–Munk Light–Tissue interaction model and Genetic Algorithm optimization process to produce a quantitative measure of cutaneous tissue. Up to the present, variant improved KMGA implementations have been successfully realized using the recent parallel computing techniques. However, all these achievements are based on the multi-core CPUs. This results in a quite high cost and low practicability for the hardware equipment of the clinical system. Fortunately, Embedded Systems (ES) applications have made great progress in recent years, and many highly effective image processing devices, such as DSPs (Digital Signal Processor) and FPGAs (Field Programmable Gate Array), have been made available to engineers at a very convenient price. Nevertheless, today’s embedded devices have as well the advantages of high speed, high embedability, low power consumption, more flexibility, etc. Thus, we focus our researches on the embedded KMGA application development. In this paper, we realize the CPU-to-FPGA transplantation of KMGA within a special High-Level Synthesis (HLS) SW/HW Co-design framework. Moreover, several optimizations are made on the algorithm and source code to improve the performances of the final implementation. Compared with CPUs, intensive experiments demonstrate that the proposed approaches can effectively improve the performances of KMGA method both in terms of efficiency and accuracy.     

An immune oriented multi-agent system for biological image processing

In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.     

Autoencoder-based image processing framework for object appearance modifications

Neural Computing and Applications - The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified...     

A novel framework for real-time diseased coral reef image classification

Multimedia Tools and Applications - The new challenge in image processing is in processing submarine coral reef images. The coral reef disease classification from such submarine coral reef images...     

A PS processing framework for long-term and real-time GB-SAR monitoring

Based on the permanent scatterer (PS) technique, deformation measurements were taken in ground-based synthetic aperture radar (GB-SAR) applications. To realize long-term and real-time deformation monitoring, a PS processing framework is proposed. Firstly, a group selection framework is introduced. A certain number of GB-SAR images are selected as a group and different PS sets are selected. Secondly, a fluctuation factor is defined to detect abnormal images. Some images with abnormal amplitude information should not be used for deformation analysis. Thirdly, an adaptive threshold determination method to select the PSs is proposed. The PS numbers in different PS sets can be highly stable. Lastly, a real-time processing framework is introduced which can be applied for on-site monitoring. The proposed processing framework was also used to monitor an open-pit mine for about 11 days. Experimental results validated the effectiveness and reliability of the framework for long-term and real-time GB-SAR monitoring.     

VLSI architecture for real-time image and video processing systems

This paper discusses the challenges of the design of real-time image and video processing systems and reviews some practical design approaches for these systems.