17608350
End-user place annotation on mobile devices: a comparative study
Advances in Location-Based Services (LBS) are opening opportunities for using the location of people, places, and things to augment or streamline interaction. While computers work with physical locations like latitude and longitude directly, people usually think and speak in terms of places, which adds personal, environmental and social meaning to a location. To address this conceptual mismatch, location-aware applications must incorporate the notion of place to achieve their full potential. In this paper, we investigate four techniques for collecting end-user place annotations interactively using cell phones. The results from a usability study suggest that while all the four methods receive similar preference ratings in understandability, the "photo memo plus offline editing" method is the most favorite approach in ease of use. In addition, users indicated their desire to adopt more than one place annotation method in location-aware applications.
207156320
Livenotes: a system for cooperative and augmented note-taking in lectures
We describe Livenotes, a shared whiteboard system and educational practice that uses wireless communication and tablet computing to support real-time conversations within small groups of students during lectures, independent of class size. We present an interface design that enables group members to interact with one another by taking lecture notes cooperatively, as well as to augment student note-taking by providing instructor slides in the background to annotate over. Livenotes was designed to facilitate more efficient, stimulating modes of learning that other collaborative approaches do not. We report how the system impacts cooperative learning in an undergraduate class and how students interacted with background slides in the workspace. We conclude with directions for improving the system and learning practice.
46167609
On solving the face recognition problem with one training sample per subject
The lack of adequate training samples and the considerable variations observed in the available image collections due to aging, illumination and pose variations are the two key technical barriers that appearance-based face recognition solutions have to overcome. It is a well-documented fact that their performance deteriorates rapidly when the number of training samples is smaller than the dimensionality of the image space. This is especially true for face recognition applications where only one training sample per subject is available. In this paper, a recognition framework based on the concept of the so-called generic learning is introduced as an attempt to boost the performance of traditional appearance-based recognition solutions in the one training sample application scenario. Different from contemporary approaches, the proposed solution learns the intrinsic properties of the subjects to be recognized using a generic training database which consists of images from subjects other than those under consideration. Many state-of-the-art face recognition solutions can be readily integrated in the proposed framework. A novel multi-learner framework is also proposed to further boost recognition performance. Extensive experimentation reported in the paper suggests that the proposed framework provides a comprehensive solution and achieves lower error recognition rate when considered in the context of one training sample face recognition problem.
1942508
Interactive video cutout
We present an interactive system for efficiently extracting foreground objects from a video. We extend previous min-cut based image segmentation techniques to the domain of video with four new contributions. We provide a novel painting-based user interface that allows users to easily indicate the foreground object across space and time. We introduce a hierarchical mean-shift preprocess in order to minimize the number of nodes that min-cut must operate on. Within the min-cut we also define new local cost functions to augment the global costs defined in earlier work. Finally, we extend 2D alpha matting methods designed for images to work with 3D video volumes. We demonstrate that our matting approach preserves smoothness across both space and time. Our interactive video cutout system allows users to quickly extract foreground objects from video sequences for use in a variety of applications including compositing onto new backgrounds and NPR cartoon style rendering.
550060
Automatic categorization of figures in scientific documents
Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for real- world use. Our tools can be integrated into a scientific-document digital library
12301513
A Comparative Study of Skin-Color Models
In this paper, we report the results of a comparative study on skin-color models generally used for facial region location. These include two 2D Gaussian models developed in normalized RGB and HSV color spaces respectively, a 1D lookup table model of hue histogram, and an adaptive 3D threshold box model. Also, we present a new model – called “adaptive hue lookup table”. The model is developed by introducing the so-called “Continuously Adaptive Mean Shift” (Camshift) technique into a traditional hue lookup table method. With the introduction of Camshift, the lookup table is able to adaptively adjust its parameters to fit the illumination conditions of different test images. In the experiments reported here, we compare the proposed method with the four typical skin-color filters in the scenarios of different human races and illuminations. The obtained results indicate that the proposed method reaches the best balance between false detection and detect rate.
31426414
An Enhanced DHCP-based Infrastructure for Intranet Management