Locating content in existing video archives is both a time and bandwidth consuming process since users might have to download and manually watch large portions of superfluous videos. In this paper, we present two novel prototypes using an Internet based video composition and streaming system with a keyword-based search interface that collects, converts, analyses, indexes, and ranks video content. At user requests, the system can automatically sequence out portions of single videos or aggregate content from multiple videos to produce a single, personalized video stream on-the-fly.

We conjecture that composition of video events from various sources into personalized video playouts will become an important part of next generation streaming systems. Here, video search is a key component since it enables users to retrieve candidate video events based on their interests. One of the main challenges, however, is to analyze the videos in order to correctly identify the various events used to annotate and index the video data. Key problems with current video analysis solutions include that they 1) are complex and therefore require a lot of processing time resulting in large delays; 2) that they can only identify a limited set of events; and 3) that they are still too inaccurate, both giving false positives and failing to find all events. In our Davvi prototype, we therefore extract metadata for our video search engine by combining existing automatic video analysis tools with currently untapped textual information available in the Internet. This provides an end-user experience where textual query results can be combined dynamically into seamless, highly personalized video playouts using an adaptive torrent-like HTTP streaming solution.

In this demo, we present a video-enabled enterprise search platform (vESP), an application prototype that enhance a widely deployed commercial enterprise search engine with video streaming. The idea is that for example in a large enterprise, like Microsoft, there exists a lot of information in form of presentations with corresponding video. Using our enhancements, a user can select and combine slides from different presentations generating a new slide deck dynamically and the corresponding video clips are concatenated and presented vis-a-vis the slides on-the-fly. The prototype is evaluated using a data set from Microsoft, and our initial user surveys indicate that the opportunity to enrich the search results with corresponding video is embraced by potential users

In this paper, we present how to provide a novel and potentially disruptive multimedia service by modifying a widely deployed commercial enterprise search engine. The idea is to transparently integrate rich multimedia data with traditional textual-oriented query results. This includes that the search engine automatically discovers and extracts relevant scenes from a large knowledge repository of existing videos and produces a new, customized video of events matching the user query. To evaluate our prototype, we have performed experiments using a data set from a knowledge repository in Microsoft consisting of PowerPoint presentations with corresponding videos. Our initial results demonstrate that such integration can be implemented efficiently, and that potential users prefer to have the opportunity to enrich the search results with corresponding video.

In this demo, we present DAVVI, a prototype of the next generation multimedia entertainment platform. It delivers multi-quality video content in a torrent-similar way like known systems from Move Networks, Microsoft and Apple do. However, it also provides a brand new, personalized user experience. Through applied search, personalization and recommendation technologies, end-users can efficiently search and retrieve highlights and combine arbitrary events in a customized manner using drag and drop. The created playlists of video segments are then delivered back to the system to improve future search and recommendation results. Here, we demonstrate this system using a soccer example.

The theory of active contours models the problem of contour recovery as an energy minimization process. The computational solutions based on dynamic programming require that the energy associated with a contour candidate can be decomposed into an integral of local energy contributions. In this paper we propose a grammatical framework that can model different local energy models and a set of allowable transitions between these models. The grammatical encodings are utilized to represent a priori knowledge about the shape of the object and the associated signatures in the underlying images. The variability encountered in numerical experiments is addressed with the energy minimization procedure which is embedded in the grammatical framework. We propose an algorithmic solution that combines a nondeterministic version of the Knuth-Morris-Pratt algorithm for string matching with a time-delayed discrete dynamic programming algorithm for energy minimization. The numerical experiments address practical problems encountered in contour recovery such as noise robustness and occlusion.

This paper presents an approach to volume rendering based on real-time and interactive data reduction prior to volume visualization. The underlying hardware design of a PCI based search engine is introduced including the architecture of a full custom VLSI chip that based on combinations of general range queries performs a real-time classification/opacity assignment of the multi-spectral voxel data. Various image preprocessing techniques are presented. These techniques are used to enhance the potential of the subsequent real-time data extraction, In particular, we describe how the real-time data extraction facility can be utilized to develop interactive inspection procedures for 3D imagery. The numerical experiments include 3D ultrasonics and 3D MRI studies.

We consider the problem of partitioning a sequence of n real numbers into p intervals such that the cost of the most expensive interval, measured with a cost function f is minimized. This problem is of importance for the scheduling of jobs both in parallel and pipelined environments. We develop a straightforward and practical dynamic programming algorithm that solves this problem in time O(p(n-p)), which is an improvement of a factor of log p compared to the previous best algorithm. A number of variants of the problem are also considered.>

An algorithm which identifies discontinuities in noisy data is presented. The signal is modelled as step edges with additive normally distributed noise present. Using finite mixture analysis a variable number of distributions are identified together with the location of the respective edges separating them. The problem is solved using a dynamic programming approach which ensures globally optimal edge positions according to the signal model of a finite mixture of normal distributions. The computational complexity is of order MN/sup 2/ where M is the number of discontinuities in the mixture and N is the number of data points in the signal. The algorithm is tested on a range of signals and yields as accurate edge positions as a corresponding square error method. Among applications for this algorithm is edge detection in medical images and examples from ultrasound imaging are included.>

This paper explores the potential of multispectral analysis of density slices extracted by automatic object recognition algorithms in volumetric data sets. The object recognition algorithm is utilized to define the object surface. Each point x on the object surface is hence associated with the set of measurements in the underlying volumetric data set that is within the region of interest and projected onto x. This variable size attribute vector is converted to a fixed size attribute vector by transforming the measurements into the associated cumulative distribution vector. The fixed size attribute vectors represent a multispectral image over the manifold defined by the object surface. This image is further processed with multispectral techniques such as the Karhunen-Loeve transform and visualized as a coloring of the 3-dimensional object surface. Our numerical experiments include 3-dimensional ultrasonic catheter studies of plaque formation in arteries, MRI studies of the brain, and evaluation of customized femoral hip prostheses based on CT imaging.>

The authors explore the application of volume rendering in medical ultrasonic imaging. Several volume rendering methods have been developed for X-ray computed tomography (X-CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Limited research has been done on applications of volume rendering techniques in medical ultrasound imaging because of a general lack of adequate equipment for 3D acquisitions. Severe noise sources and other limitations in the imaging system make volume rendering of ultrasonic data a challenge compared to rendering of MRI and X-CT data. Rendering algorithms that rely on an initial classification of the data into different tissue categories have been developed for high quality X-CT and MR-data. So far, there is a lack of general and reliable methods for tissue classification in ultrasonic imaging. The authors focus on volume rendering methods which are not dependent on any classification into different tissue categories. Instead, features are extracted from the original 3D data-set, and projected onto the view plane. The authors found that some of these methods may give clinically useful information which is very difficult to get from ordinary 2D ultrasonic images, and in some cases renderings with very fine structural details. The authors have applied the methods to 3D ultrasound images from fetal examinations. The methods are now in use as clinical tools at the National Center of Fetal Medicine in Trondheim, Norway.

Different standard rendering methods applied to 4-D medical ultrasound data are discussed. In particular, maximum value projection, sum of values projection, transparent gray level gradient shading, and surface shading have been tested. Due to the fact that ultrasound data suffer from a low signal to noise ratio, image processing and image analysis are used to enhance and classify the volumetric data set.>