1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.

Head Pose Estimation in Computer Vision: A Survey

L'A. passe en revue les techniques de visualisation utilisees pour representer de facon cartographique la structure de domaine des disciplines scientifiques, et pour soutenir la recherche d'information et la classification. Un bref historique montre que la visualisation des domaines de connaissances s'enracine dans des disciplines telles que la scientometrie, la bibliometrie et l'analyse de citations, ainsi que la visualisation scientifique. L'A. analyse les principales etapes du processus de visualisation des domaines de connaissances : unites d'analyse, mesures, similarites entre unites. Differentes techniques couramment utilisees pour l'analyse et la visualisation des connaissances sont passees en revue : techniques de reduction de la dimensionnalite, analyse par clusters, configuration spatiale, visualisation et conception d'interaction. Differentes approches sont appliquees pour engendrer et comparer diverses representations cartographiques de la recherche sur la visualisation des domaines de connaissances. Ces cartes mettent en valeur les relations entre l'analyse de citations, la bibliometrie, la semantique et la visualisation de l'information. Augmenter l'accessibilite de la visualisation des domaines aupres des non-experts, appliquer la visualisation des domaines de connaissances pour mieux repondre a des questions pragmatiques, favoriser la collaboration et la diffusion des resultats entre chercheurs, developper des algorithmes plus robustes, comptent parmi les directions de recherche les plus prometteuses.

https://cns.iu.edu/docs/publications/2003-borner-arist.pdf

Visualizing knowledge domains

This paper introduces a new sensor architecture for making interactive surfaces that are sensitive to human hand and finger gestures. This sensor recognizes multiple hand positions and shapes and calculates the distance between the hand and the surface by using capacitive sensing and a mesh-shaped antenna. In contrast to camera-based gesture recognition systems, all sensing elements can be integrated within the surface, and this method does not suffer from lighting and occlusion problems. This paper describes the sensor architecture, as well as two working prototype systems: a table-size system and a tablet-size system. It also describes several interaction techniques that would be difficult to perform without using this architecture

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SmartSkin: an infrastructure for freehand manipulation on interactive surfaces

We present a new focus+context (fisheye) technique for visualizing and manipulating large hierarchies. Our technique assigns more display space to a portion of the hierarchy while still embedding it in the context of the entire hierarchy. The essence of this scheme is to lay out the hierarchy in a uniform way on a hyperbolic plane an d map this plane onto a circular display region. This supports a smooth blending between focus and context, as well as continuous redirection of the focus. We have developed effective procedures for manipulating the focus using pointer clicks as well as interactive dragging, and for smoothly an imating transitions across such manipulation. A laboratory experiment comparing the hyperbolic browser with a conventional hierarchy browser was conducted.

/pdf/a-focus-context-technique-based-on-hyperbolic-geometry-for-f0ntnof99s.pdf

A focus+context technique based on hyperbolic geometry for visualizing large hierarchies

Augmented desk interfaces and other virtual reality systems depend on accurate, real-time hand and fingertip tracking for seamless integration between real objects and associated digital information. We introduce a method for discerning fingertip locations in image frames and measuring fingertip trajectories across image frames. We also propose a mechanism for combining direct manipulation and symbolic gestures based on multiple fingertip motions. Our method uses a filtering technique, in addition to detecting fingertips in each image frame, to predict fingertip locations in successive image frames and to examine the correspondences between the predicted locations and detected fingertips. This lets us obtain multiple complex fingertip trajectories in real time and improves fingertip tracking. This method can track multiple fingertips reliably even on a complex background under changing lighting conditions without invasive devices or color markers.

Real-time fingertip tracking and gesture recognition

This article describes a design and implementation of an augmented desk system, named EnhancedDesk, which smoothly integrates paper and digital information on a desk. The system provides users an intelligent environment that automatically retrieves and displays digital information corresponding to the real objects (e.g., books) on the desk by using computer vision. The system also provides users direct manipulation of digital information by using the users’ own hands and fingers for more natural and more intuitive interaction. Based on the experiments with our first prototype system, some critical issues on augmented desk systems were identified when trying to pursue rapid and fine recognition of hands and fingers. To overcome these issues, we developed a novel method for realtime finger tracking on an augmented desk system by introducing a infrared camera, pattern matching with normalized correlation, and a pan-tilt camera. We then show an interface prototype on EnhancedDesk. It is an application to a computer-supported learning environment, named Interactive Textbook. The system shows how effective the integration of paper and digital information is and how natural and intuitive direct manipulation of digital information with users’ hands and fingers is.

Integrating Paper and Digital Information on EnhancedDesk : A Method for Realtime Finger Tracking on an Augmented Desk System

We introduce a fast and robust method for tracking positions of the centers and the fingertips of both right and left hands. Our method makes use of infrared camera images for reliable detection of a user's hands, and uses a template matching strategy for finding fingertips. This method is an essential part of our augmented desk interface in which a user can, with natural hand gestures, simultaneously manipulate both physical objects and electronically projected objects on a desk, e.g., a textbook and related WWW pages. Previous tracking methods which are typically based on color segmentation or background subtraction simply do not perform well in this type of application because an observed color of human skin and image backgrounds may change significantly due to protection of various objects onto a desk. In contrast, our proposed method was shown to be effective even in such a challenging situation through demonstration in our augmented desk interface. This paper describes the details of our tracking method as well as typical applications in our augmented desk interface.

Fast tracking of hands and fingertips in infrared images for augmented desk interface

Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems

We propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems. Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique. Our gesture recognition system is particularly advantageous for human-computer interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.

Hideki Koike

Papers

Real-time fingertip tracking and gesture recognition

Integrating Paper and Digital Information on EnhancedDesk : A Method for Realtime Finger Tracking on an Augmented Desk System

Fast tracking of hands and fingertips in infrared images for augmented desk interface

Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems

Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems