Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting)

Determination of the number of colonies (colony forming units, CFU) is a standard method in microbiological analysis to ensure the quality of drinking water. Normally this tedious work is still performed manually. A PC-based method for the automated counting of digitized images of Petri dishes is presented. The method includes highly specific and effective object recognition algorithms that ensure very high detection accuracy. The processing sequence implies internal controls therefore enabling reliable automated evaluations of series of images. Use of the Fuzzy formalism and the high adaptivity of the algorithms lead to an extraordinary user-friendliness. For digitization different devices like flatbed scanners or CCD-cameras can be used. Due to the highly adaptive algorithms samples from the routine standard preparation process in laboratories can be evaluated. The accuracy and quality of the method aim at advancement in objectivity of colony counting and quality control and assurance. The algorithms and the evaluation of the method are presented.     

An IKB defect classification system for automated industrial radiographic inspection

Abstract: This paper presents work undertaken as part of a project concerned with the development of a fully automated industrial radiographic inspection system, based on both conventional image-processing techniques for the detection and analysis of defects in the radiographic image, and intelligent knowledge-based (1KB) techniques for the classification and evaluation of defect data against the quality assurance requirements of the inspection process. In this paper the 1KB defect classification system is presented. This system is based on a hierarchical frame-based knowledge representation and a backward-chaining production rule system. Examples of the frame structures, frame taxonomies and the data-driven procedures, which maintain the knowledge base are given, along with an outline of the defect classification rules and the inference mechanism for dealing with uncertainty by means of confidence factors.     

An automated coding and classification system with supporting database for effective design of manufacturing systems

The philosophy of group technology (GT) is an important concept in the design of flexible manufacturing systems and manufacturing cells. Group technology is a manufacturing philosophy that identifies similar parts and groups them into families. Beside assigning unique codes to these parts, group technology developers intend to take advantage of part similarities during design and manufacturing processes. GT is not the answer to all manufacturing problems, but it is a good management technique with which to standardize efforts and eliminate duplication. Group technology classifies parts by assigning them to different families based on their similarities in: (1) design attributes (physical shape and size), and/or (2) manufacturing attributes (processing sequence). The manufacturing industry today is process focused; departments and sub units are no longer independent but are interdependent. If the product development process is to be optimized, engineering and manufacturing cannot remain independent any more: they must be coordinated. Each sub-system is a critical component within an integrated manufacturing framework. The coding and classification system is the basis of CAPP and the functioning and reliability of CAPP depends on the robustness of the coding system. The proposed coding system is considered superior to the previously proposed coding systems, in that it has the capability to migrate into multiple manufacturing environments. This article presents the design of a coding and classification system and the supporting database for manufacturing processes based on both design and manufacturing attributes of parts. An interface with the spreadsheet will calculate the machine operation costs for various processes. This menu-driven interactive package is implemented using dBASE-IV. Part Family formation is achieved using a KAMCELL package developed in TURBO Pascal.     

An accurate method for automated counting of silver grains in autoradiographs

A grain counting algorithm has been developed for use with an image analysis system interfaced with a microscope. The algorithm consists of a sequence of tests involving thresholding, prospective grain center detection, contrast, and three kinds of artifact detection. The function of each of these parameters is described. This algorithm is accurate to within 4% for those fields most frequently encountered, and to within 9% for all fields examined. Furthermore, it operates very quickly and at relatively low magnification, making it possible to scan large areas efficiently.     

An automated pottery archival and reconstruction system

Motivated by the current requirements of archaeologists, we are developing an automated archival system for archaeological classification and reconstruction of ceramics. Our system uses the profile of an archaeological fragment, which is the cross‐section of the fragment in the direction of the rotational axis of symmetry, to classify and reconstruct it virtually. Ceramic fragments are recorded automatically by a 3D measurement system based on structured (coded) light. The input data for the estimation of the profile is a set of points produced by the acquisition system. By registering the front and the back views of the fragment the profile is computed and measurements like diameter, area percentage of the complete vessel, height and width are derived automatically. We demonstrate the method and give results on synthetic and real data. Copyright © 2003 John Wiley & Sons, Ltd.     

基于细菌觅食的改进蚁群算法

蚁群算法是模仿蚂蚁觅食行为的一种新的仿生学智能优化算法。针对其收敛速度慢和易陷入局部最优的不足,将细菌觅食算法和蚁群算法相结合,提出一种细菌觅食 蚁群算法。在蚁群算法迭代过程中,引入细菌觅食算法的复制操作,以加快算法的收敛速度;引入细菌觅食算法的趋向操作,以增强算法的全局搜索能力。通过经典的旅行商问题和函数优化问题测试表明,细菌觅食 蚁群算法在寻优能力、可靠性、收敛效率和稳定性方面均优于基本蚁群算法及两种改进蚁群算法。     

An automated patient control and data management system

A system developed to be used in the conduct of contact lens research is described. The system provides for data entry and retrieval, graphic image capture and analysis, and query and ASCII interface capabilities. Procedures for accessing the collected data are explained.     

An automated clinical research and data analysis system

A system for the collection and computerized storage, retrieval, and analysis of clinical data, based on collaboration between Research Clinicians and Operational Research workers, is described. This system is operating successfully and provides the clinicians with a powerful aid to research management. It forces them to evaluate closely the data they collect and why they collect it, so enabling them to consider a wide range of factors relevant to the patient's behavior. It is thus possible to detect differences early in a study, evaluate explanations for these differences, and alter treatment accordingly.     

An infra-red source classification system

A passive system for the classification of isolated infra-red signatures is described along with the extraction of contour, spectral and emittance features, obtained in parallel from a starring non-image-forming mosaïc with radial structure.     

An automated vision system for container-code recognition

Automatic container-code recognition is of great importance to the modern container management system. Similar techniques have been proposed for vehicle license plate recognition in past decades. Compared with license plate recognition, automatic container-code recognition faces more challenges due to the severity of nonuniform illumination and invalidation of color information. In this paper, a computer vision based container-code recognition technique is proposed. The system consists of three function modules, namely location, isolation, and character recognition. In location module, we propose a text-line region location algorithm, which takes into account the characteristics of single character as well as the spatial relationship between successive characters. This module locates the text-line regions by using a horizontal high-pass filter and scanline analysis. To resolve nonuniform illumination, a two-step procedure is applied to segment container-code characters, and a projection process is adopted to isolate characters in the isolation module. In character recognition module, the character recognition is achieved by classifying the extracted features, which represent the character image, with trained support vector machines (SVMs). The experimental results demonstrate the efficiency and effectiveness of the proposed technique for practical usage.     

混沌细菌群体趋药算法及应用

为了解决传统细菌群体趋药优化算法的收敛速度较慢问题,提出混沌细菌群体趋药算法。该算法在细菌群体中加入基于Tent映射的混沌搜索,增加了细菌群体中个体的多样性,将寻优过程分成趋药细菌群和混沌细菌群同时进行,提高了细菌群体优化算法的全局收敛能力。典型实例表明,本方法收敛速度快,优于传统细菌群体趋药优化算法。     

一种仿Dijkstra的蚂蚁算法

为了快捷可靠地寻找到满足多媒体QoS保证的路由,结合Dijkstra算法和蚂蚁算法,从源节点开始,在所有满足QoS要求的邻接链路上泛滥寻路蚂蚁,所有中间节点依据一个设定的QoS路由优劣综合评价函数,在到达的所有寻路蚂蚁中选择其最优者复制并继续泛滥,直到最后到达目的节点为止。这样,通过约束条件下的穷举搜索,最后一定可以找到源节点和目的节点间的满足QoS要求的路由。实验证明,该算法具有思路直观、运算量小、强收敛、能自适应网络变动优点。     

一种混合的离散细菌菌落优化算法

为了利用细菌算法解决组合优化问题, 提出了一种混合的离散细菌菌落优化算法。根据现有细菌优化算法, 设计一种新的个体编码方式及进化模式, 通过设计种群的自适应调整因子增强个体活力, 并融合禁忌搜索算法, 克服算法易于陷入过早收敛的不足, 并与其他算法在Taillard标准调度测试问题集上比较实验, 验证了算法的有效性。仿真结果表明, 该算法可以搜索到问题的最优组合, 能够有效避免算法陷入局部最优, 取得了满意的结果。     

基于分类蚁群算法的彩色图像自动分类

为提升图像自动分类算法的通用性和鲁棒性,加快算法收敛速度,针对图像分类的特点,对传统蚁群算法进行改进,引入分类蚁群模型。随机蚂蚁识别统计图像类别,构建类别表,确定聚类中心;智能蚂蚁按相应的搜索前进策略进行分类。相比基本蚁群分类算法,该算法可以在较短的时间内完成图像的自动分类。     

基于密度分类及组合特征的人数估计算法*

为了克服不同人群密度及所采用特征对人数估计的影响,提出了一种基于人群密度分类及组合特征的人数统计算法。该算法包括离线特征组合选取和在线实时估计两个阶段。在离线阶段,选取密度阈值将图像样本分为高、低密度两类,然后通过实验方法选取最优的特征组合。在线估计阶段首先通过分类器将样本分为高、低密度两类,然后利用离线阶段选取的特征组合训练得到高斯模型,并分别对两类样本进行人数估计。实验结果表明,与不分高低密度相比,平均估计误差由10.6%降至8.1%;与目前主流的人数估计算法相比,本文算法的平均估计误差也更小。