Algorithms for Fuzzy Segmentation

Fuzzy segmentation is an effective way of segmenting out objects in pictures containing both random noise and shading. This is illustrated both on mathematically created pictures and on some obtained from medical imaging. A theory of fuzzy segmentation is presented. To perform fuzzy segmentation, a ‘connectedness map’ needs to be produced. It is demonstrated that greedy algorithms for creating such a connectedness map are faster than the previously used dynamic programming technique. Once the connectedness map is created, segmentation is completed by a simple thresholding of the connectedness map. This approach is efficacious in instances where simple thresholding of the original picture fails. Received: 22 October 1998?Received in revised form: 22 November 1998?Accepted: 2 December 1998     

A new visual cryptography with multi-level encoding

Visual secret sharing (VSS) is a visual cryptography scheme which decodes secret messages into several enlarged shares, and distributes them to different participants. The participants can recover the secret messages by stacking their shares, and then secret message can be revealed by human visual sensitivity. Afterward some researchers start to research size invariant scheme, and apply to encode grayscale images such as scenic photos or pictures, not only binary messages. Owing to the gray values distribution of pictures are different, extreme distribution may cause blurred revealed image. In this paper, we proposed a size invariant VSS scheme which is suitable for different distribution of image's gray values. Experiment results show that the reconstructed images of our method, for brighter, darker, and normal images, have clearer and higher contrast, and without apparent artifact and unexpected contour.     

Threshold selection using quadtrees

A simple class of piecewise constant approximations to an image is constructed as follows: start with the entire image, subdivide it into quadrants if its gray level standard deviation is high, and repeat the process for each quadrant. This yields a decomposition of the image into blocks, each having low standard deviation, so that each of them can be approximated by a constant value, namely, its mean. The histogram of this approximated image tends to have sharper peaks than that of the original image since the block averaging reduces the variability of the gray levels within homogeneous regions. A possible way of further improving the histogram is based on the fact that small blocks tend to occur near region borders; thus, suppressing these blocks should tend to deepen the valleys on the histogram, making threshold selection (to separate regions of different types) easier. Conversely, the histogram of the small blocks only represents a population of pixels near region borders, and if there are only two types of regions (e.g., objects and background), the mean of this histogram should be a useful threshold for separating them; but in practice, this method is not very reliable since background fluctuations also give rise to border pixels.     

GPS coordinates estimation and camera calibration from solar shadows

In this paper, we discuss the issue of camera parameter estimation (intrinsic and extrinsic parameters), along with estimation of the geo-location of the camera by using only the shadow trajectories. By observing stationary objects over a period of time, it is shown that only six points on the trajectories formed by tracking the shadows of the objects are sufficient to estimate the horizon line of the ground plane. This line is used along with the extracted vertical vanishing point to calibrate the stationary camera. The method requires as few as two shadow casting objects in the scene and a set of six or more points on the shadow trajectories of these objects. Once camera intrinsic parameters are recovered, we present a novel application where one can accurately determine the geo-location of the camera up to a longitude ambiguity using only three points from these shadow trajectories without using any GPS or other special instruments. We consider possible cases where this ambiguity can also be removed if additional information is available. Our method does not require any knowledge of the date or the time when the images are taken, and recovers the date of acquisition directly from the images. We demonstrate the accuracy of our technique for both steps of calibration and geo-temporal localization using synthetic and real data.     

基于模糊连接度的近邻传播聚类图像分割方法

针对现有近邻传播聚类图像分割方法分割精度低的问题,提出一种基于模糊连接度的邻近传播聚类(FCAP)图像分割算法。针对传统模糊连接度算法不能得出任意点对间模糊连接度的不足,结合最大生成树提出了全模糊连接度算法。FCAP算法先使用Normalized Cut超像素技术进行超像素分割,这些超像素可以看作数据点以及它们之间的模糊连接度;然后使用所提出的全模糊连接度算法计算超像素间的模糊连接度,根据模糊连接度和空间信息计算超像素的相似度;最后使用近邻传播(AP)聚类算法完成分割。实验结果表明,FCAP算法明显优于超像素处理后直接使用AP聚类算法进行分割的方法,并且优于无监督图像分割方法。     

Remarks on the recognizability of topological components by three-dimensional automata

It is conjectured that the three-dimensional pattern processing has its own difficulties not arising in two-dimensional case. One of these difficulties occurs in recognizing topological properties of three-dimensional patterns because the three-dimensional neighborhood is more complicated than two-dimensional case. Generally a property or relationship is topological only if it is preserved when an arbitrary “rubber-sheet” distortion is applied to the pictures. For example, adjacency and connectedness are topological; area, elongatedness, convexity, and straightness are not. In recent years, there have been many interesting papers on digital topological properties. For example, an interlocking component was defined as a new topological property in three-dimensional digital pictures, and it was proved that no one marker automaton can recognize interlocking components in a three-dimensional digital picture. In this paper, we deal with recognizability of topological components by three-dimensional Turing machines, and investigate some properties. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A formal framework for image indexing with triples: Toward a concept-based image retrieval

This paper provides a formal specification for concept-based image retrieval using triples. To effectively manage a vast amount of images, we may need an image retrieval system capable of indexing and searching images based on the characteristics of their content. However, such a content-based image retrieval technique alone may not satisfy user queries if retrieved images turn out to be relevant only when they are conceptually related with the queries. In this paper, we develop an image retrieval mechanism to extract semantics of images based on triples. The semantics can be captured by deriving concepts from its constituent objects and spatial relationships between them. The concepts are basically composite objects formed from the aggregation of the constituents. In our mechanism, all the spatial relationships between objects including the concepts are uniformly represented by triples, which are used for indexing images as well as capturing their semantics. We also develop a query evaluation for supporting the concept-based image retrieval. ©1999 John Wiley & Sons, Inc.     

Real-Time Updating of 2D Map for Autonomous Robot Locomotion Based on Distinction Between Static and Semi-Static Objects

Autonomous mobile robots are increasingly being used in complex 2D environments such as factories, warehouses, and offices. For such environments, we propose a real-time technique for updating an environmental map for a robot’s self-localization using a bearing-range sensor in situations where a basis map can be preliminarily prepared. These environments include many semi-static objects such as cardboard boxes, and the locations of these objects change frequently. Therefore, the self-localization needs to reflect the changes in both the existence and position of semi-static objects in the map in real-time. However, if the robot uses a traditional technique that updates all objects and if it keeps updating the map for a long period, static objects such as walls will move slightly on the map due to errors of both measurement and self-localization, and the map will be distorted. Therefore, our technique distinguishes between static and semi-static objects on the map, and it defines the changeability of the occupancy probability of every spatial grid in order to update the map without changing the occupancy probabilities of grids around static objects. By doing so, we prevented the map from being distorted. In addition, by estimating the grids’ statuses during two observations of the same grids and by changing the probabilities of the objects’ fixedness based on the statuses, our technique can robustly distinguish the objects on the map even if the timing of observing grids is irregular.     

Switching components and the ambiguity problem in the reconstruction of pictures from their projections

The information in a digital picture may be compressed by summing gray levels over known paths, or rays, of the picture. A projection is an ordered set of ray sums. Sets of projections may be used to store pictures in compressed form. Given a projection set, reconstruction is achieved by operating on the projection set to produce either the original picture or an approximation to it (called a similar picture). The latter is due to the ambiguity of the reconstruction technique inherent in the data compression.

This paper describes the ambiguity problem resulting in picture reconstruction from projection sets. The concept of switching components, a key factor in the ambiguity, is discussed. Switching components are groups of picture points which can be changed in a certain way (therefore changing the original picture to a “similar” picture) without affecting the projection set.

A technique is developed that addresses itself first to unambiguous areas of the picture in reconstruction. This technique is used along with more traditional methods for the doubtful areas.     

Camera calibration with one-dimensional objects

Camera calibration has been studied extensively in computer vision and photogrammetry and the proposed techniques in the literature include those using 3D apparatus (two or three planes orthogonal to each other or a plane undergoing a pure translation, etc.), 2D objects (planar patterns undergoing unknown motions), and 0D features (self-calibration using unknown scene points). Yet, this paper proposes a new calibration technique using 1D objects (points aligned on a line), thus filling the missing dimension in calibration. In particular, we show that camera calibration is not possible with free-moving 1D objects, but can be solved if one point is fixed. A closed-form solution is developed if six or more observations of such a 1D object are made. For higher accuracy, a nonlinear technique based on the maximum likelihood criterion is then used to refine the estimate. Singularities have also been studied. Besides the theoretical aspect, the proposed technique is also important in practice especially when calibrating multiple cameras mounted apart from each other, where the calibration objects are required to be visible simultaneously.     

On connectivity properties of grayscale pictures

A grayscale digital picture is called “connected” if it has only one connected component of constant gray level that is maximal, i.e., not adjacent to any component of higher gray level. This note establishes some equivalent conditions for connectedness, and also defines a grayscale generalization of the genus in terms of sums of local property values.     

基于地形学标定的人脸眼睛定位

根据眼睛区域灰度固有特征,从灰度图像地形学标定的角度出发,找到眼睛点的候选集合.然后对平滑后的图像进行数学形态学的膨胀操作,以精简候选集合.最后用投影法对精简后的眼睛对进行验证,从而达到眼睛对精确定位的目的.通过不同质量的人脸数据库进行实验,该方法具有很好的光照鲁棒性,取得了较为满意的定位效果.     

The analytic hierarchy process: a note on an approach to sensitivity which preserves rank order

Within the framework of the AHP as it applies to multicriteria decisions, it is frequently the case that decision makers are certain about the rank order of the objects for a particular pairwise comparison matrix but uncertain about the precise numerical weights that the AHP produces for that matrix. This uncertainty translates directly into uncertainty about whether the best alternative obtained from the AHP is actually the best alternative. However, if the weights of an AHP pairwise comparison matrix can be varied in a way that preserves the rank order of the objects, and at the same time, this perturbation does not result in the best alternative changing, then the decision maker is typically much more confident about what the AHP recommends. In this paper, I detail a simple approach to sensitivity within the AHP which preserves the rank order of the objects.Scope and purposeIn the author's experience with AHP as a multicriteria decision tool, it is frequently the case that decision makers (DMs) are quite certain about the rank order of the objects for a particular pairwise comparison matrix (PCM) but uncertain about the precise numerical weights that the AHP produces for that matrix. This uncertainty translates directly into uncertainty about whether the best alternative obtained from the AHP is actually the best alternative. However, if the weights of a PCM can be varied in a way that preserves the rank order of the objects for that matrix, and at the same time, this perturbation does not result in the best alternative overall changing, then the DM is typically much more confident about what the AHP recommends. In this paper I detail such an approach to sensitivity for the AHP.     

Automatic analysis of moving images

Cine film and videotape are used to record a variety of natural processes in biology, medicine, meteorology, etc. This paper describes a system which detects and tracks moving objects from these records to obtain meaningful measures of their movements, such as linear and angular velocities. Features of the system are as follows. 1) In order to detect moving objects that are usually blurred, temporal differences of gray values (differences between consecutive frames) are used to separate moving objects from stationary objects, in addition to spatial differences of gray values. 2) The results of previous frames are used to guide feature extraction process of the next frame so that efficient processing of moving pictures which consists of a large number of frames is possible. 3) Uncertain parts in the current frame, such as occluded objects, are deduced using information of previous frames. 4) Misinterpreted or unknown parts in previous frames are reanalyzed using the results of later frames where those parts could be found.     

基于图割和模糊连接度的交互式舰船红外图像分割方法

针对舰船红外图像分割中的低对比度、边缘模糊和目标灰度不均匀问题, 提出了基于图割和模糊连接度的交互式图像分割方法. 交互方式为矩形笔刷, 选择目标和背景种子点. 分割方法为基于图割的图像分割方法, 引入模糊连接度来计算图割的似然能, 给出了模糊连接度权重的自动确定方法, 提出了基于直方图分解的高斯混合模型(Gaussian mixture model, GMM)成分个数和参数估计方法. 仿真结果表明, 新方法可实现各种复杂环境下舰船红外图像目标的有效分割.     

Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images

In this paper, a new method in order to achieve the geometrical and topological definition of extracted road networks is presented. Starting from a raster binary image where a road network is depicted, this algorithm seeks the automatic raster – vector conversion based on skeleton extraction and graph theory and using GIS database if it is available. The last goal of this method is to provide a numerical structured file which includes the geometric definition for all roads as well as the topologic relations between them. The applied technique comprises six steps. In the first step, the quality of the binary image is improved through a noise cleaning process. In the second step, parallel edges of road network are smoothed by means a generalization process. In the third step, skeleton is extracted applying a known and efficient method which some years ago was published. The fourth step consists in constructing the graph and generating the different cartographic objects which compose the road network. In this phase, GIS information can be used in order to improve the result. In the fifth step, objects are numerically adjusted by means of polynomial adjustment in the opened objects case, and using a reiterative polygonal adjustment in the sharp objects case. In the last step, mathematical morphology is applied to validate topologically the geometrical adjustment. For it, the junction nodes are analyzed for changing automatically their coordinates in order to achieve a topologically correct road network vectorization. Finally, objects are structured according to cartographic criteria and a numerical file with the vectorized road network is provided. Experimental results show the validity of this approach.     

基于包围盒与空间分解的碰撞检测算法

本文提出了一种基于包围盒方法与空间分解方法相结合的碰撞检测算法,用于解决变形体的碰撞检测问题。该算法首先用包围盒来快速判断物体之间是否相交,如果相交则进一步用空间分解法来定位相交的区域,在此阶段用哈希表的数据结构来保存物体的几何信息。与其他碰撞检测算法相比较,本算法不仅能够较大地节省空间,而且时间复杂度也比较低。除此之外,本算法不仅能够找出发生碰撞的基本几何元素对,而且还能够精确地找出碰撞点。     

基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。