10792862
A presentation authoring tool for media devices distributed environments
Many conference rooms are now equipped with multiple multi-media devices, such as plasma displays and surrounding speakers, to enhance presentation quality. However, most existing presentation authoring tools are based on the one-display-and-one-speaker assumption, which makes it difficult to organize and playback a presentation dispatched to multiple devices, thus hinders users from taking full advantage of additional multimedia devices. In this paper, we propose and implement a tool to facilitate authoring and playback of a multi-channel presentation in a media devices distributed environment. The tool, named PreAuthor, provides an intuitive and visual way to author a multi-channel presentation by dragging and dropping "hyper-slides" on corresponding visual representations of various devices. PreAuthor supports "hyper-slide" synchronization among various output devices during preview and playback. It also offers multiple options for the presenter to view the presentation in a rendered image sequence, live video, 3D VRML model, or real environment.
564702
A hidden environment model for constructing indoor radio maps
Constructing indoor radio maps plays an important role in many services and applications, such as wireless base station planning. We propose a hybrid approach to constructing indoor radio maps by developing a novel indoor signal propagation model, called the hidden environment model (HEM). The model is hybrid because it combines on-site measurements with a number of different types of calculations. As part of this model, we introduce the idea of an environment factor matrix (EFM). The EFM represents a model of the environmental features that affect radio attenuation. We also develop a lazy sampling algorithm to help generate the EFM. The goal of the lazy sampling algorithm is to balance the number of measurements that need to be taken with our model's accuracy. The goal is to minimize the measurement workload while maintaining satisfactory accuracy. We evaluate our model by comparing the radio maps calculated from the model to a radio map obtained by exhaustive measurements. The results show that our hidden environment model achieves good accuracy.
5415099
Face Annotation for Family Photo Album Management
In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.
17170240
The Z-Transformation graph for an outerplane bipartite graph has a Hamilton path
The Z-transformation graph of perfect matchings of a plane bipartite graph G has the perfect matchings of G as vertices, two perfect matchings being adjacent if their symmetric difference forms a cycle that is the boundary of an interior face of G. In this note, we prove that the Z-transformation graph of perfect matchings of an outerplane bipartite graph has a Hamilton path.
1131614
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.
214819
Scalable search-based image annotation of personal images
With the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation (SBIA) algorithm that is analogous to Web page search. First, content-based image retrieval (CBIR) technology is used to retrieve a set of visually similar images from a large-scale Web image set. Then, a text-based keyword search (TBKS) technique is used to obtain a ranked list of candidate annotations for each retrieved image. Finally, a fusion algorithm is used to combine the ranked lists into the final annotation list. The application of both efficient search technologies and Web-scale image set guarantees the scalability of the proposed algorithm. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.
7692801
Semi-quantitative Simulation Modeling of Software Engineering Process
Software process simulation models hold out the promise of improving project planning and control. However, purely quantitative models require a very detailed understanding of the software process, i.e. process knowledge represented quantitatively. When such data is lacking, quantitative models impose severe constraints, restricting the model's value. In contrast, qualitative models display all possible behaviors but only in qualitative terms. This paper illustrates the value and flexibility of semi-quantitative modeling by developing a model of the software staffing process and comparing it with other quantitative staffing models. We show that the semi-quantitative model provides more insights into the staffing process and more confidence in the outcomes than the quantitative models by achieving a tradeoff between quantitative and qualitative simulation. In particular, the semi-quantitative simulation produces a set of possible outcomes with the ranges of real numeric values. The semi-quantitative model allows us to determine the solution boundaries for specific scenarios under the conditions of limited knowledge.