基于O型密封圈尺寸的检测方法

以机械密封中任选的一种。型密封圈为研究对象,使用高清摄像机获取其图像,采用数字图像处理技术对图像进行分析,提取出密封圈的几何特征,即密封圈的内径和外径,再使用有效的图像测量方法检测该O型密封圈的缺陷以及提取其尺寸。文章具体设计讨论了密封圈直径检测的方法,为工业上对密封圈缺陷及直径的实时检测提供了依据。以上图像的处理均在Matlab7．0的环境下实现。     

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks.     

Detection of pre-defined boundaries between hydrothermal alteration zones using rotation-variant template matching

A new method for the detection of pre-defined boundaries in single-band image data that uses a rotation-variant template matching (RTM) algorithm is presented. This algorithm matches a miniature image of a pre-defined boundary to image data at various orientations. The image pixels that match boundary criteria are reported in output imagery together with the rotation angle of the template. The method is applied to identify boundaries between hydrothermal alteration zones in processed airborne hyperspectral imagery, based on the presence of white mica minerals. Results show that boundaries identified with RTM are relatively free of noise and more coherent than those identified with, for instance, image slicing techniques. Identified boundaries can be used for image segmentation. The output of the RTM algorithm also provides information on the type of boundary, whether it is crisp or gradual. This information can be used to better characterize mineral variation in the alteration halo associated with fossil hydrothermal systems.     

Formulation of parallel image processing tasks

An increasing awareness of the need for high speed parallel processing systems for image analysis has stimulated a great deal of interest in the study of such systems. These studies have focussed primarily on specific algorithms and while they demonstrated the utility of such an approach, few general principles have evolved. As a result, it is still uncertain how one may go about addressing a given application. This paper first presents techniques for formulating parallel image processing tasks by focussing on one or more components of an image processing environment. Then a parallel processing model is proposed which specifies the interaction among tasks formulated in this manner. The techniques and model enable one to determine constraints on architectural features required to achieve predefined performance levels and compare and contrast different formulations.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

A method for combining complementary techniques for document image segmentation

Image segmentation is a major task of handwritten document image processing. Many of the proposed techniques for image segmentation are complementary in the sense that each of them using a different approach can solve different difficult problems such as overlapping, touching components, influence of author or font style etc. In this paper, a combination method of different segmentation techniques is presented. Our goal is to exploit the segmentation results of complementary techniques and specific features of the initial image so as to generate improved segmentation results. Experimental results on line segmentation methods for handwritten documents demonstrate the effectiveness of the proposed combination method.     

3D计算中高速图形转换器算法设计

在图像处理过程中的分析图像的特征,需要达成特征提取,为后续的图像处理和分析。在目标图像的提取过程中,本文选定了特征角点特征。角点定位准确的图像匹配在三维重建中起着重要的作用。角落像素相对于外在的像素灰度值的突变或凸形状的像素点较为集中。本文分析了三个角点提取方法:苏珊角点检测方法,哈里斯角点检测方法,多尺度结合苏珊算法改进,亚像素角点检测方法,以便满足系统对精度的要求。     

Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

In the last two decades, we have seen an amazing development of image processing techniques targeted for medical applications. We propose multi-GPU-based parallel real-time algorithms for segmentation and shape-based object detection, aiming at accelerating two medical image processing methods: automated blood detection in wireless capsule endoscopy (WCE) images and automated bright lesion detection in retinal fundus images. In the former method we identified segmentation and object detection as being responsible for consuming most of the global processing time. While in the latter, as segmentation was not used, shape-based object detection was the compute-intensive task identified. Experimental results show that the accelerated method running on multi-GPU systems for blood detection in WCE images is on average 265 times faster than the original CPU version and is able to process 344 frames per second. By applying the multi-GPU framework for bright lesion detection in fundus images we are able to process 62 frames per second with a speedup average 667 times faster than the equivalent CPU version.     

基于图像处理的家蚕吐丝管轮廓检测方法研究

作为自然微系统,家蚕在吐丝过程中表现出良好的微流动特性,其吐丝管结构与吐丝机理分析是研制仿生微通道和微流体系统的关键。以五龄家蚕为实验对象,通过石蜡切片和显微观察技术获得家蚕吐丝管断面切片及其图像;在Matlab和UG等软件中,运用图像处理技术对家蚕吐丝管切片图像进行滤波、分割、边缘提取、轮廓拟合与修复等操作,提取五龄家蚕吐丝管断面轮廓的显微图像;利用Image-Pro Plus6.0软件进行家蚕吐丝管轮廓尺寸测量。结果表明,图像处理技术可用于家蚕吐丝管轮廓检测,能够满足精度要求,为家蚕吐丝管的三维重构和内部微流动特性检测等后续工作奠定基础。     

An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis

The real-time vehicle detection from a traffic scene is the major process in image processing based traffic data collection and analysis techniques. The most common algorithm used for real-time vehicle detection is based on background differencing and thresholding operations. The efficiency of this method of image detection is heavily dependent on the background updating and threshold selection techniques. In this paper, a new background updating and a dynamic threshold selection technique is presented. An alternative image detection technique used in image processing is based on edge detection techniques. However, an edge detector extracts the edges of the objects of a scene irrespective of whether it belongs to the background details or the objects. Therefore, to separate these two, extra information is required. We have developed a new image detection method based on background differencing and edge detection techniques, which separates the objects from their backgrounds and works well under various lighting and weather conditions. This image detection technique together with other techniques for calculating traffic parameters e.g. counting number of vehicles, works in real-time on an 80386-based microcomputer operating at a clock speed of 33 MHz.     

彩色图像分割方法综述

由于彩色图像提供了比灰度图像更为丰富的信息，因此彩色图像处理正受到人们越来越多的关注。彩色图像分割是彩色图像处理的重要问题，彩色图像分割可以看成是灰度图像分割技术在各种颜色空间上的应用，为了使该领域的研究人员对当前各种彩色图像分割方法有较全面的了解，因此对各种彩色图像分割方法进行了系统论述，即先对各种颜色空间进行简单介绍，然后对直方图阈值法、特征空间聚类、基于区域的方法、边缘检测、模糊方法、神经元网络、基于物理模型方法等主要的彩色图像分割技术进行综述，并比较了它们的优缺点，通过比较发现模糊技术由于能很好地表达和处理不确定性问题，因此在彩色图像分割领域会有更广阔的应用前景。     

Illumination invariant extraction for face recognition using neighboring wavelet coefficients

The features of a face can change drastically as the illumination changes. In contrast to pose position and expression, illumination changes present a much greater challenge to face recognition. In this paper, we propose a novel wavelet based approach that considers the correlation of neighboring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. Our method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based NeighShrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. More importantly, by having different processes, a simple processing algorithm with low time complexity can be applied to the testing image. This leads to an easy application to real face recognition systems. Experimental results on Yale face database B and CMU PIE Face Database show that excellent recognition rates can be achieved by the proposed method.     

Human detection using a mobile platform and novel features derived from a visual saliency mechanism

Human detection is a key ability to an increasing number of applications that operates in human inhabited environments or needs to interact with a human user. Currently, most successful approaches to human detection are based on background substraction techniques that apply only to the case of static cameras or cameras with highly constrained motions. Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera. In this work, we present a new computer vision algorithm designed to operate with moving cameras and to detect humans in different poses under partial or complete view of the human body. We follow a standard pattern recognition approach based on four main steps: (i) preprocessing to achieve color constancy and stereo pair calibration, (ii) segmentation using depth continuity information, (iii) feature extraction based on visual saliency, and (iv) classification using a neural network. The main novelty of our approach lies in the feature extraction step, where we propose novel features derived from a visual saliency mechanism. In contrast to previous works, we do not use a pyramidal decomposition to run the saliency algorithm, but we implement this at the original image resolution using the so-called integral image. Our results indicate that our method: (i) outperforms state-of-the-art techniques for human detection based on face detectors, (ii) outperforms state-of-the-art techniques for complete human body detection based on different set of visual features, and (iii) operates in real time onboard a mobile platform, such as a mobile robot (15 fps).     

Intelligent and vision-based fire detection systems: A survey

Fire is one of the main disasters in the world. A fire detection system should detect fires in various environments (e.g., buildings, forests, and rural areas) in the shortest time in order to reduce financial losses and humanistic disasters. Fire sensors are, in fact, complementary to conventional point sensors (e.g., smoke and heat detectors), which provide people the early warnings of fire occurrences. Cameras combined with image processing techniques detect fire occurrences more quickly than point sensors. Moreover, they provide the size, growth, and direction of fires more easily than their conventional detectors. This paper, initially, presents a glance view on the main features of various environments including buildings, forests, and mines that should be considered in the design of fire detection systems. Afterwards, it describes some of the intelligent and vision-based fire detection systems that have been presented by researchers in the last decade. These systems are categorized, in this paper, into two groups: intelligent detection systems for forest fires and intelligent fire detection systems for all of the environments. They use various intelligent techniques (e.g., convolutional neural networks, color models, and fuzzy logic) to detect fire occurrences with a high accuracy in various environments. Performances of the fire detection systems are compared to each other in terms of detection rate, precision, true-positive rate, false-positive rate, etc. under different evaluation scenarios.     

A cellular automata model for edge relaxation

An edge detection method based on a fuzzy cellular automata model which serves as the relaxation labeling process constraint is described. An initial estimate of edge locations is made and the remaining ambiguities are resolved by thinning and enhancing the edges through several iterations. An efficient fixed step algorithm is presented and its performance is evaluated for different noise level images. The method is useful for the detection of linear image features in three-dimensional robot vision systems.     

A new framework for remote sensing image super-resolution: Sparse representation-based method by processing dictionaries with multi-type features

Remote sensing images play an important role in many practical applications, however, due to the physical limitations of remote sensing devices, it is difficult to obtain images at an expecting high resolution level. Acquiring high-resolution(HR) images from the original low-resolution(LR) ones with super-resolution(SR) methods has always been an attractive proposition in embedded systems including various kinds of tablet PC and smart phone. SR methods based on sparse representation have been successfully used in processing remote sensing images, however, they have two major problems in common. First, they use only one type of image features to represent the low resolution(LR) images. However, one single type of features cannot accurately represent an image due to the diverse structures of the image, as a result, artifacts would be produced simultaneously. Second, many dictionary learning methods try to build a universal dictionary with only one single type of features. However, apparently, a dictionary with a single type of features is not enough to capture the different structures of a remote sensing image, without any doubt, the resultant image would turn out to be a poor one. To overcome the problems above, we propose a new framework for remote sensing image super resolution: sparse representation-based SR method by processing dictionaries with multi-type features. First, in order to represent the remote sensing image more accurately, different types of features are extracted from images. Second, to achieve a better performance, various dictionaries with multi-type features are learned to capture the essential structures of the image. Then, it’s proposed to adaptively control the weights of the high resolution(HR) patches obtained by different dictionaries. Numerous experiments validate that this proposed framework brings better results in terms of both objective quantitation and visual perception than other compared algorithms.     

机器视觉表面缺陷检测综述

目的 工业产品的表面缺陷对产品的美观度、舒适度和使用性能等带来不良影响,所以生产企业对产品的表面缺陷进行检测以便及时发现并加以控制。机器视觉的检测方法可以很大程度上克服人工检测方法的抽检率低、准确性不高、实时性差、效率低、劳动强度大等弊端,在现代工业中得到越来越广泛的研究和应用。方法 以机器视觉表面缺陷检测为研究对象,在广泛调研相关文献和发展成果的基础上,对基于机器视觉在表面缺陷检测领域的应用进行了综述。分析了典型机器视觉表面缺陷检测系统的工作原理和基本结构,阐述了表面缺陷视觉检测的研究现状、现有视觉软件和硬件平台,综述了机器视觉检测所涉及到的图像预处理算法、图像分割算法、图像特征提取及其选择算法、图像识别等相关理论和算法研究,并对每种主要方法的基本思想、特点和存在的局限性进行了总结,对未来可能的发展方向进行展望。结果 机器视觉表面缺陷检测系统中,图像处理和分析算法是重要内容,算法各有优缺点和其适应范围。如何提高算法的准确性、实时性和鲁棒性,一直是研究者们努力的方向。结论 机器视觉是对人类视觉的模拟,机器视觉表面检测涉及众多学科和理论,如何使检测进一步向自动化和智能化方向发展,还需要更深入的研究。     

An image representation of infrastructure based on non-classical receptive field

Biological vision systems have become highly optimized over millions of years of evolution, developing complex neural structures to represent and process stimuli. Moreover, biological systems of vision are typically far more efficient than current human-made machine vision systems. The present report describes a non-task-dependent image representation schema that simulates the early phase of a biological neural vision mechanism. We designed a neural model involving multiple types of computational units to simulate ganglion cells and their non-classical receptive fields, local feedback control circuits and receptive field dynamic self-adjustment mechanisms in the retina. We found that, beyond the pixel level, our model was able to represent images self-adaptively and rapidly. A series of statistical analyses revealed that this model not only produces compact and abstract approximations of images, but also retains their primary visual features. In addition, the improved representation was found to substantially facilitate contour detection and image segmentation. We propose that this improvement arose because ganglion cells can resize their receptive fields, enabling multi-scale analysis functionality, a neighborhood referring function and a localized synthesis function. The ganglion cell layer is the starting point of subsequent diverse visual processing. The universality of this cell type and its functional mechanisms suggests that it will be useful for designing image processing algorithms in future.     

INTELLIGOLD - AN EXPERT SYSTEM FOR GOLD PLANT PROCESS DESIGN

Gold mining projects are a rare opportunity in the minerals industry. They require relatively small capital and give high profitability and fast return on investment compared with other mineral projects. To expand or maintain gold production, continuous development of new deposits and fast implementation of new mining sites are needed. Process design is one of the major issues. As simple and easily extractable ores are almost all exhausted, there is a need for a consistent approach to deal with increasing complexity and decreasing or stagnant gold prices. Process design must consider ore genesis, mineralogical characteristics, ore behavior in available metallurgical processes, linkage with the mining method, environmental impact, and economic issues. The type of work and environment involved makes this application ideal for using AI tools such as expert systems, fuzzy logic, and neural networks. This paper presents Intelligold, an expert system for project development teams to use at the preliminary evaluation and conceptual project stages. Information and knowledge from geology mineralogy, processing, and economics are organized, and recommendations on process options and estimated costs and revenue are given. The "knowledge-building" method is described, together with implementation and verification. Success in building this system suggests application to other ores such as copper and complex base metals.