Visualizing and segmenting large volumetric data sets

Current systems for segmenting and visualizing volumetric data sets characteristically require the user to possess a technical sophistication in volume visualization techniques, thus restricting the potential audience of users. As large volumetric data sets become more common, segmentation and visualization tools need to deemphasize the technical aspects of visualization and let users exploit their content knowledge of the data set. This proves especially critical in an educational setting. In anatomical education, data sets such as the Visible Human Project provide significant learning opportunities, but students must have tools that let them apply, refine, and build on their anatomical knowledge without technical obstacles. I describe a software environment that uses immersive virtual reality technology to let users immediately apply their expert knowledge to exploring and visualizing volumetric data sets     

Sorting unorganized photo sets for urban reconstruction

In spite of advanced acquisition technology, consumer cameras remain an attractive means for capturing 3D data. For reconstructing buildings it is easy to obtain large numbers of photos representing complete, all-around coverage of a building; however, such large photos collections are often unordered and unorganized, with unknown viewpoints. We present a method for reconstructing piecewise planar building models based on a near-linear time process that sorts such unorganized collections, quickly creating an image graph, an initial pose for each camera, and a piecewise-planar facade model. Our sorting technique first estimates single-view, piecewise planar geometry from each photo, then merges these single-view models together in an analysis phase that reasons about the global scene geometry. A key contribution of our technique is to perform this reasoning based on a number of typical constraints of buildings. This sorting process results in a piecewise planar model of the scene, a set of good initial camera poses, and a correspondence between photos. This information is useful in itself as an approximate scene model, but also represents a good initialization for structure from motion and multi-view stereo techniques from which refined models can be derived, at greatly reduced computational cost compared to prior techniques.     

An on-line learning method for face association in personal photo collection

Due to the widespread use of cameras, it is very common to collect thousands of personal photos. A proper organization is needed to make the collection usable and to enable an easy photo retrieval. In this paper, we present a method to organize personal photo collections based on “who” is in the picture. Our method consists in detecting the faces in the photo sequence and arranging them in groups corresponding to the probable identities. This problem can be conveniently modeled as a multi-target visual tracking where a set of on-line trained classifiers is used to represent the identity models. In contrast to other works where clustering methods are used, our method relies on a probabilistic framework; it does not require any prior information about the number of different identities in the photo album. To enable future comparison, we present experimental results on a public dataset and on a photo collection generated from a public face dataset.     

Semantic analysis and retrieval in personal and social photo collections

Semantic understanding of images has been an important topic in the research community for a long time as it is an important prerequisite to build meaningful retrieval systems which are accessible by both users and automatic reasoning algorithms. Recently, especially with the growing trend to share photos online, the social aspect of image retrieval becomes more and more prevalent and image retrieval more and more focusses specifically on photos and their special characteristics, especially on information outside the image itself. Researchers are starting to explore how and why photos are shot, shared and used and try to incorporate this additional knowledge to aid image analysis and retrieval. Several survey papers have been written in the past reviewing works in the general field of image analysis and retrieval. However, the social aspect of image retrieval and the focus on digital photos has not sufficiently been addressed in these works. In this article we give an overview over the current research field of semantic photo understanding, annotation and retrieval. We review over 160 contributions in the field and identify trending topics and implications for future directions of research.     

From PhotoWork to PhotoUse: exploring personal digital photo activities

People accumulate large collections of digital photos, which they use for individual, social, and utilitarian purposes. In order to provide suitable technologies for enjoying our expanding photo collections, it is essential to understand how and to what purpose these collections are used. Contextual interviews with 12 participants in their homes explored the use of digital photos, incorporating new photo activities that are offered by new technologies. Based on the qualitative analysis of the collected data, we give an overview of current photo activities, which we term PhotoUse. We introduce a model of PhotoUse, which emphasises the purpose of photo activities rather than the tools to support them. We argue for the use of our model to design tools to support the user’s individual and social goals pertaining to PhotoUse.     

改进的OTSU射线图像分割算法

为提高对工业X射线图像的分割效果,提出一种改进的OTSU图像分割算法.分析了经典OTSU算法存在的不足,将类内方差也作为计算最佳阈值的一个因素考虑,从而提出了一种改进的OSTU阈值选择函数.在阈值分割过程中,采用局部递归法逐次进行分割.采用目标背景面积差和类内方差的比值作为递归结束的条件.通过与最大熵法、经典OT-SU法、局部递归OTSU法进行实验对比,证明本算法对于射线图像是一种优秀的阈值分割算法.     

An algorithm for segmenting cytological images

An adaptive algorithm for automatic segmentation of objects in cytological images is described. The algorithm is based on the well-known seeded region growing method (SGR), is robust to noise, and can handle low contrast images. The algorithm allows for automatic adjustment of its parameter values and initialization of cluster growth. Oleg L. Konevsky. Born 1973. Received master’s degree in engineering from Novgorod State University in 1995 and candidate’s degree (Eng.) from St. Petersburg State Technical University in 1998. Since 2000, an associate professor at the Information Technologies and Systems Department, Novgorod State University. Scientific interests: image segmentation, mathematical morphology, and neural networks. Author of nearly 30 papers in the field of pattern recognition and image analysis. Member of IEEE, IEEE Computer Society, and IEEE Signal Processing Society. Yurii V. Stepanets. Born 1980. Received master’s degree in engineering from Novgorod State University in 2002. Currently, post-graduate student at the same university. Scientific interests: image segmentation, artificial intelligence, and neural networks. Author of ten papers in the field of pattern recognition and image analysis.     

An algorithm for segmenting juxtaposed objects

This work is an attempt to develop an algorithm for segmentation of juxtaposed objects arising in the recognition of certain scenes. The approach is based on matrix manipulation. The algorithm is implemented in two parts. The first part is to find the significant points of concavity. In the second part, the direction of the paths of segmentation is found and the paths are grown into the pattern until complete segmentation is achieved. The algorithm is illustrated by considering examples from biomedical imagery.     

Human interactions and personal space in collaborative virtual environments

As humans start to spend more time in collaborative virtual environments (CVEs) it becomes important to study their interactions in such environments. One aspect of such interactions is personal space. To begin to address this, we have conducted empirical investigations in a non immersive virtual environment: an experiment to investigate the influence on personal space of avatar gender, and an observational study to further explore the existence of personal space. Experimental results give some evidence to suggest that avatar gender has an influence on personal space although the participants did not register high personal space invasion anxiety, contrary to what one might expect from personal space invasion in the physical world. The observational study suggests that personal space does exist in CVEs, as the users tend to maintain, in a similar way to the physical world, a distance when they are interacting with each other. Our studies provide an improved understanding of personal space in CVEs and the results can be used to further enhance the usability of these environments.     

传感器特性曲线的自适应分段最佳拟合及应用

为了减小传感器的非线性误差问题,提出了一种传感器的非线性传输曲线的自适应分段最佳逼近拟合方法, 介绍了该方法的数学模型和应用实例,实验结果表明, 应用本方法, 使传感系统的非线性误差大大减小.     

Time versus space trade-offs for rendezvous in trees

Two identical (anonymous) mobile agents start from arbitrary nodes of an unknown tree and have to meet at some node. Agents move in synchronous rounds: in each round an agent can either stay at the current node or move to one of its neighbors. We consider deterministic algorithms for this rendezvous task. The main result of this paper is a tight trade-off between the optimal time of completing rendezvous and the size of memory of the agents. For agents with $k$ memory bits, we show that optimal rendezvous time is $\Theta (n+n^2/k)$ in $n$ -node trees. More precisely, if $k \ge c\log n$ , for some constant $c$ , we design agents accomplishing rendezvous in arbitrary trees of size $n$ (unknown to the agents) in time $O(n+n^2/k)$ , starting with arbitrary delay. We also show that no pair of agents can accomplish rendezvous in time $o(n+n^2/k)$ , even in the class of lines of known length and even with simultaneous start. Finally, we prove that at least logarithmic memory is necessary for rendezvous, even for agents starting simultaneously in a $n$ -node line.     

Oriented connectivity-based method for segmenting solar loops

A method based on oriented connectivity that can automatically segment arc-like structures (solar loops) from intensity images of the Sun's corona is introduced. The method is a constructive approach that uses model-guided processing to enable extraction of credible loop structures. Since the solar loops are vestiges of the solar magnetic field, the model-guided processing exploits external estimates of this field's local orientations that are derived from a physical magnetic field model. Empirical studies of the method's effectiveness are also presented. The oriented connectivity-based method is the first automatic method for the segmentation of solar loops.     

传感器特性曲线的自适应分段最佳拟合及应用

为了减小传感器的非线性误差问题 ,提出了一种传感器的非线性传输曲线的自适应分段最佳逼近拟合方法 ,介绍了该方法的数学模型和应用实例 ,实验结果表明 ,应用本方法 ,使传感系统的非线性误差大大减小     

An incremental space to visualize dynamic data sets

In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. We have developed an inherently incremental technique (incBoard) that maintains a coherent disposition of elements from a dynamic multidimensional data set on a 2D grid as the set changes. Here, we introduce a novel layout that uses pairwise similarity from grid neighbors, as defined in incBoard, to reposition elements on the visual space, free from constraints imposed by the grid. The board continues to be updated and can be displayed alongside the new space. As similar items are placed together, while dissimilar neighbors are moved apart, it supports users in the identification of clusters and subsets of related elements. Densely populated areas identified in the incSpace can be efficiently explored with the corresponding incBoard visualization, which is not susceptible to occlusion. The solution remains inherently incremental and maintains a coherent disposition of elements, even for fully renewed sets. The algorithm considers relative positions for the initial placement of elements, and raw dissimilarity to fine tune the visualization. It has low computational cost, with complexity depending only on the size of the currently viewed subset, V. Thus, a data set of size N can be sequentially displayed in O(N) time, reaching O(N ²) only if the complete set is simultaneously displayed.