利用信息融合方法的三维特征提取

本文提出了一种基于信息融合的物体三维特征的提取方法，该方法利用两幅互相配准的三维测距图像和灰度图像，来提取多面体的三维特征。首先，通过分析灰度图像中的灰度变化及测距图像中的测距值变化，分别求取各自图像中物体的特征点及特征边；然后，利用两配准图像之间的对应关系，求得所有特征点、面与多边形在三维测距图像中的三维表示；接着，通过分析三维测距图像中所测得的各候选平面上特定点与边处的曲率及法向，验证候选平面     

Management of multidimensional discrete data

Spatial database management involves two main categories of data: vector and raster data. The former has received a lot of in-depth investigation; the latter still lacks a sound framework. Current DBMSs either regard raster data as pure byte sequences where the DBMS has no knowledge about the underlying semantics, or they do not complement array structures with storage mechanisms suitable for huge arrays, or they are designed as specialized systems with sophisticated imaging functionality, but no general database capabilities (e.g., a query language). Many types of array data will require database support in the future, notably 2-D images, audio data and general signal-time series (1-D), animations (3-D), static or time-variant voxel fields (3-D and 4-D), and the ISO/IEC PIKS (Programmer's Imaging Kernel System) BasicImage type (5-D). In this article, we propose a comprehensive support ofmultidimensional discrete data (MDD) in databases, including operations on arrays of arbitrary size over arbitrary data types. A set of requirements is developed, a small set of language constructs is proposed (based on a formal algebraic semantics), and a novel MDD architecture is outlined to provide the basis for efficient MDD query evaluation.     

A semantic modeling approach for image retrieval by content

We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach.     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

Bandpass filters using periodically loaded combline resonators

A novel combline filter is proposed for cellular‐radio base stations. The Q‐factor is significantly improved. The eigenvalue equation is expressed with the single‐team approximation in the gap region of the combline resonator. A two‐pole combline filter is designed. The calculation, simulation, and experiment results are presented and are in good agreement. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Empirical comparison of image retrieval color similarity methods with human judgment

It is important to verify assumptions and methods of image retrieval against actual human behavior. A study was conducted to compare similarity methods of color histograms against human assessment of similarity. The similarity methods tested include basic histogram intersection, center histogram matching, locality histogram matching, and size-weighted histogram matching. 161 subjects participated in the empirical study. The findings, based on Spearman correlation analysis, showed that both the basic histogram intersection method and size-weighted histogram are very close to human assessment of similarity (Spearman correlation coefficient of 0.915). The other two are not close to human judgment on similarity. This study illustrates an alternative approach to evaluating matching algorithms. Unlike the usual measures of recall and precision, this approach emphasizes human validation. Fewer images are required with the use of statistical testing.     

喷墨打印相纸及打印图像质量的分析

喷墨打印相纸随着数码相机的发展近几年在国内发展迅速。针对目前产品质量没有测量标准的现状,提出喷墨相纸的质量指标主要为4个,即白度、光泽度、油墨吸收性和防水性。给出了相应的测量方法。并指出相纸上油墨点的面积是衡量质量的第一重要指标,其次是点的圆度。另外对图像输出的质量介绍了一种科学的评价方法,即图像的逼真性分析。说明图像的打印墨点直径不应小于方形像素的对角线长,图像才能真实再现。     

A simple approximation algorithm for the weighted matching problem

We present a linear time approximation algorithm with a performance ratio of 1/2 for finding a maximum weight matching in an arbitrary graph. Such a result is already known and is due to Preis [STACS'99, Lecture Notes in Comput. Sci., Vol. 1563, 1999, pp. 259-269]. Our algorithm uses a new approach which is much simpler than the one given by Preis and needs no amortized analysis for its running time.     

Shift-or string matching with super-alphabets

Given a text T[1…n] and a pattern P[1…m] over some alphabet Σ of size σ, we want to find all the (exact) occurrences of P in T. The well-known shift-or algorithm solves this problem in time O(n⌈m/w⌉), where w is the number of bits in machine word, using bit-parallelism. We show how to extend the bit-parallelism in another direction, using super-alphabets. This gives a speed-up by a factor s, where s is the number of characters processed simultaneously. The algorithm is implemented, and we show that it works well in practice too. The result is the fastest known algorithm for exact string matching for short patterns and small alphabets.     

The Logarithmic Image Processing Model: Connections with Human Brightness Perception and Contrast Estimators

The logarithmic image processing (LIP) model is amathematical framework based on abstract linear mathematicswhich provides a set of specific algebraic and functionaloperations that can be applied to the processing of intensityimages valued in a bounded range. The LIP model has been provedto be physically justified in the setting of transmitted lightand to be consistent with several laws and characteristics ofthe human visual system. Successful application examples havealso been reported in several image processing areas, e.g.,image enhancement, image restoration, three-dimensional imagereconstruction, edge detection and image segmentation.The aim of this article is to show that the LIP model is atractable mathematical framework for image processing which isconsistent with several laws and characteristics of humanbrightness perception. This is a survey article in the sensethat it presents (almost) previously published results in arevised, refined and self-contained form. First, an introductionto the LIP model is exposed. Emphasis will be especially placedon the initial motivation and goal, and on the scope of themodel. Then, an introductory summary of mathematicalfundamentals of the LIP model is detailed. Next, the articleaims at surveying the connections of the LIP model with severallaws and characteristics of human brightness perception, namelythe brightness scale inversion, saturation characteristic, Weber'sand Fechner's laws, and the psychophysical contrast notion. Finally,it is shown that the LIP model is a powerful and tractable framework for handling the contrast notion. This is done througha survey of several LIP-model-based contrast estimators associated with special subparts (point, pair of points,boundary, region) of intensity images, that are justified bothfrom a physical and mathematical point of view.