基于均匀空间的颜色分级方法

自动视觉检测是机器视觉的一个重要研究领域，而颜色分级是自动视觉检测中的一个典型问题，在陶瓷、木材等行业应用广泛。为了实现快速自动分级，根据人类视觉特性，提出了一种基于均匀颜色空间的表面颜色分级方法。该方法首先将数据从RGB颜色空间转换到CIE 1976 L^ a^ 均匀颜色空间；然后在CIE 1976 L^ a^ 空间用RWM(radius weighted mean)方法提取主导颜色(dominant colors，DC)，再以此作为颜色特征，提出了一种新的颜色距离度量——映射色差，并分析了它与平均色差的关系；最后以映射色差为距离度量，采用最小距离分类器来进行颜色分级。实验结果说明该方法是有效的。     

3-D face recognition method based on optimum 3-D image measurement technology

Most face recognition systems employ 2-D color or gray-scale images. However, face recognition based on 2-D images is adversely affected by 3-D movement, variable lighting, and the use of cosmetics. 3-D image measurement technology has the potential to overcome these limitations of face recognition based on 2-D images since it can perform geometric analysis. We propose a method that is capable of recognizing a person from a 3-D facial image obtained using a 3-D shape measurement system by employing a technique that optimizes the intensity-modulation pattern projection. This face recognition method is based on the iterative closest point algorithm. It is robust to changes in reflectivity and color. Since the 3-D facial information can be registered, this method can estimate rotations and translations to compensate for different positions or directions. In order to prove the validity of the proposed technique, a verification experiment was conducted which used 105 sample 3-D images obtained from 15 subjects. It achieved a detection rate of 96% when heads were turned at an angle of 20° or less relative to the camera.     

基于曲线的纹理映射

本文提出一种纹理映射的新方法。二维平面中的一条曲线与三维曲面上的一条曲线相对应，两条曲线上的点再相对应。当对应曲线足够多时，就可获理完成纹理映射所需的足够信息。     

Fingerprint-Quality Index Using Gradient Components

Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.      

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

Three-Dimensional and Stereoscopic Graphics for Scientific Data Display and Analysis

Many scientists and technicians rarely venture beyond 2-D plots in their computer graphics. Since an additional dimension is so helpful?and accessible?3-D should be exploited.     

Signal Detection Theory Approach to the Multiple Parallel Moving Targets Problem

The problem of detection and line location estimation of multiple, parallel, dim, moving targets, such as the ones typically encountered when a geostationary satellite is tracking targets, is studied under the framework of signal detection theory. Part I of the paper considers two-dimensional data (or one frame of an image) and Part II considers an additional third dimension representing time. Optimal processors are derived for varying degrees of uncertainty in the data for the detection of parallel targets. The uncertainties include uncertainty in the knowledge of orientation, location, number, and direction of arrival of the targets. Performance of the optimal processors is presented in the form of Receiver Operating Characteristic (ROC) curves and compared, in Part I, with the Hough transform. The optimal 2-D processors perform better than the Hough transform under all cases of uncertainties. Likelihood-ratio-based optimal estimation algorithms resolve the location of targets under severe noise conditions. In Part II, ROCs for the optimal 3-D processors are compared with both 2-D optimal processors and the Hough transform that use the projected data. Simulation results indicate that substantial gains in performance can be achieved by processing the 3-D data directly instead of first projecting and optimally processing in 2-D. It is observed that the computational burden in optimally processing the 3-D data sequentially is comparable to the conventional techniques involving projection and the Hough transform.     

Escaping RGBland: Selecting colors for statistical graphics

Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines)—e.g., as in bar plots, pie charts, mosaic displays or heatmaps—it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Based on the perceptually-based Hue-Chroma-Luminance (HCL) color space suitable color palettes are derived for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).     

Using eigencolor normalization for illumination-invariant color object recognition

Color is one of salient features for color object recognition, however, the colors of object images sensitively depend on scene illumination. To overcome the lighting dependency problem, a color constancy or color normalization method has to be used. This paper presents a color image normalization method, called eigencolor normalization, which consists of two phases as follows. First, the compacting method, which was originally used for compensating the adverse effect due to shape distortion for 2-D planar objects, is exploited for 3-D color space to make the color distribution less correlated and more compact. Second, the compact color image is further normalized by rotating the histogram to align with the reference axis computed. Consequently, the object colors are transformed into a new color space, called eigencolor space, which reflects the inherent colors of the object and is more invariant to illumination changes. Experimental results show that our eigencolor normalization method is superior to other existing color constancy or color normalization schemes on achieving more accurate color object recognition.     

Analysis and control of the jump modes behavior of 2-D singular systems—Part I: Structural stability

This paper considers the problem of structural stability of 2-D singular systems. Firstly, some properties of structural stability of 2-D general singular systems are presented. Sufficient and necessary conditions for the structural stability of the 2-D singular systems are given. Then, by extending the Lyapunov approach for the structural stability of 1-D continuous singular systems to the discrete case, a generalized Lyapunov equation approach to the analysis of the structural stability of 2-D singular Roesser models (2-D SRM) is proposed. The existence of a solution to the generalized Lyapunov equation gives a sufficient condition for the structural stability of the 2-D SRM.     

Multimodality visualization of medical volume data

New developments in 3-D volume acquisitions are creating a rapidly increasing demand for integrating multimodality 3-D visualization. In order to accomplish routine clinical multimodality visualization, many issues have to be dealt with, such as techniques for accurate spatial registration, integrated representation, suitable graphical user interfaces, and obtaining adequate rendering speeds. The aim of this experience paper is 2-fold. First, it presents various results from our research on multimodality visualization/registration. Second, this paper explicitly addresses practical problems and findings related to software development and multimodality registration/visualization. We hope that this will give colleagues a better understanding in some of these issues based on our experience, including notably our mistakes.     

Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction

This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally, and perform 3-D surface reconstruction better than some existing approaches.     

Periodic Smoothing Spline Surface and Its Application to Dynamic Contour Modeling of Wet Material Objects

This paper considers a problem of optimal design of periodic smoothing spline surfaces employing normalized uniform B-splines as the basis functions. The surface can be periodic in either of two variables or in both. The expressions for optimal solutions are concise and are readily solved numerically. These periodic surfaces can be used to construct closed or semiclosed surfaces in the 3-D space. Assuming that the data are obtained by sampling some surface with noises, we present convergent properties of optimal spline surface when the number of data becomes infinity. The results are applied to the problem of modeling contour of wet material objects with deforming motion. The effectiveness is examined by numerical and experimental studies.      

Three-dimensional digitization of highly reflective and transparent objects using multi-wavelength range sensing

Digitizing specular and transparent objects pose significant problems using traditional 3-D scanning techniques due to the reflection and refraction that interfere with the optical scanning process used for triangulation. In this paper, we present how one can digitize those difficult objects by modifying a commercial 3-D acquisition system with an interchangeable ultraviolet and infrared light source. Experimental results show that the proposed technique can generate accurate 3-D models of these optically challenging objects without major modifications to the 3-D scanner. The results were obtained without preprocessing or multi-view manipulations. The precision of the 3-D measurements is evaluated relative to the visible spectrum acquisition obtained by painting the test objects with matte paint to suppress optical difficulties. Results shows that wavelength changes in the 3-D acquisition system do not change the scanner precision but solve many of the issues that specular and transparent objects poses.     

Development of a low-cost interface for coupling a microcomputer with a fluorescence spectrophotometer. Computer-assisted fluorimetry

A simple interface has been developed for combination of a Perkin-Elmer MPF-43 spectrofluorimeter with a low-cost microcomputer. Hardware and software designs of the interface for the Commodore 64 microcomputer are described in detail. The interface allows passive data transfer and the software provides for subsequent mathematical treatment.Recent fluorescence techniques such as conventional or synchronous derivative fluorimetry are possible with the instrument. Excitation-emission matrices can also be generated as 3-D fluorescence spectra or contour plots. Sections through the contour plots at different paths allow the application of new techniques such as variable angle synchronous scanning fluorimetry and non-linear variable angle synchronous scanning flourimetry.     

Advice for those bitten by the startup bug [IT business]

The author provides some advice on starting up a software or IT business. He considers issues such as market research, business planning, names and trademarks, taxation, finance, salaries and employee recruitment     

Representing three-dimensional structures for visual recognition

Three-dimensional (3-D) geometrical models provide the best representations for 3-D objects. Not all representation schemes are suitable, however, for computer-based visual recognition. This survey analyses the historical development of recognition-oriented models from points and lines, to surfaces and volumes. It also considers those aspects of the models that successfully promoted recognition, and suggests likely areas for future development.     

The Layout Design Language: A Technique for Generating Layout Plans

The work described in this paper arose from a requirement for a system to support the production of models for training simulator visual systems and other visualisation applications. To reduce the labour involved in generating such models, it was decided to develop a technique whereby models could be generated by a high-leuel language. For these models there is a requirement to generate 2-D plans showing, for example, the locations of roads and buildings, subsequently 3-D models of the buildings are placed at the defined locations. The 2-D plans can be created through the use of the Layout Design Language (LD-Language). LD-Language has been formulated particularly for designing urban area street plans. The primitive elements of the LD-Language, plots, are assembled into patterns which can be further combined into a layout plan, with some support provided by the system. The work reported has successfully demonstrated that a complex layout plan can be developed using this language.     

Structural indexing: efficient 3-D object recognition

The authors present an approach for the recognition of multiple 3-D object models from three 3-D scene data. The approach uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves, respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a table and provide the essential mechanism for fast retrieval and matching. The issues of robustness and stability of the features are addressed in detail. The acquisition of the 3-D models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3-D curves. The authors present results with the current system (3-D object recognition based on super segments) and discuss further extensions