We present an algorithm to compute the connected components of upper or lower level sets from level lines. The running time is O (n + |τ|), where n is the size of the image and |τ| is the number of level lines.

We introduce an optimal topdown algorithm for computing and representing level line trees of 2-D intensity images. The running time of the algorithm is O(n + t), where n is the size of the input image and t is the total length of all level lines. The properties of level line trees are also investigated. The efficiency of the algorithm is illustrated by experiments on images of different sizes and scenes.

Existing data analysis techniques have difficulty in handling multidimensional data. Multidimensional data has been a challenge for data analysis because of the inherent sparsity of the points. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of distribution of data. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering, and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multidimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus generating condensed, widely-separated clusters. Following data shrinking, clusters are detected by finding the connected components of dense cells (and evaluated by their compactness). The data-shrinking and cluster-detection steps are conducted on a sequence of grids with different cell sizes. The clusters detected at these scales are compared by a cluster-wise evaluation measurement, and the best clusters are selected as the final result. The experimental results show that this approach can effectively and efficiently detect clusters in both low and high-dimensional spaces.

Over-segmentation could be relieved by adopting a divisive image segmentation model. This also requires the binary classification of whether a segmented region corresponds to a single semantic object. In this paper, we propose a model to address this classification problem, by detecting if a region contains both "background" and "foreground" regions. When "background" and "foreground" both present, the region is considered to have multiple objects, otherwise it corresponds to a single object. We implement the model based on certain image features of the region that effectively tell the difference between "background" and "foreground". Experiments show that our model can effectively perform the classification tasks.

Visualization enables us to find structures, features, patterns and relationship in a dataset by presenting the data in various graphical forms with possible interactions. Recent development of DNA microarray technology can be used to measure the expression levels of thousands of genes simultaneously. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analysis and visualize these fast growing data. In this paper, we present VizCluster, an interactive visualization approach to cluster analysis, and its application on microarray data. VizCluster combines the merits of both high dimensional scatter-plot and parallel coordinates. Integrated with useful features, it can give a simple, fast, intuitive and yet powerful view of the data set. VizCluster supports three major analyzing modes: cluster/class discovery, class prediction, and class assessment. Its primary applications are the classification of samples on microarray datasets. The experiments are based on gene expression data from a study of multiple sclerosis and leukemia patients.

We present a new flow and congestion control scheme, PLUS (Probe-Loss Utilization Streaming protocol), for distributed multimedia presentation systems. This scheme utilizes probing of the network situation and an effective adjustment mechanism to data loss to support multimedia presentations. The proposed scheme is also designed to scale with increasing number of PLUS-based streaming traffic and to live in harmony with TCP-based traffic. The novelty of the PLUS protocol is that it utilizes the knowledge of its future bottleneck bandwidth in probing the current network situation. This can be achieved by a priori knowledge of the multimedia data before a presentation is requested by a client. Compression schemes like MPEG introduce dependencies on media units. I frames are needed to successfully decode P and B frames, and P frames are needed to decode B frames. A loss of an I or P frame automatically eliminates dependent media units. Our probing scheme increases the successful transmission of critical I and P packets without the overhead of error-correction-schemes. Probing is done using B-frame packets. The advantage is that we use data packets as probe packets. With the PLUS protocol we address the need to avoid congestion rather than react to it. Experiments demonstrate the effectiveness of the approach in utilizing network resources and decreasing loss ratios.

Advances in multimedia computing offer new approaches to support online access to information from a variety of sources such as video, audio, images, and transparencies. The key to success in providing distributed real time retrieval, is the control of network delay and congestion to keep up the desired quality of service (QoS). We present a new distributed multimedia database environment, called NetMedia. This environment can support controlled delivery of video, audio, and text data, across Internet. NetMedia can be the base of various distributed multimedia applications. As an example we present NetMedia-Virtual-Classroom, an asynchronous distance learning tool, which uses the advantages of the NetMedia framework.