The visualization of high resolution video on small mobile devices is still a great challenge today. Most critical are the limited display resolution and different aspect ratios of handheld mobile devices. So far, there is no retargeting algorithm available that guarantees good results for all videos. We introduce a new video retargeting approach that reduces the resolution while preserving as much of the relevant content as possible. A central component of the system selects the most suitable algorithm to adapt a given shot. We have implemented two retargeting algorithms: a region of interest (ROI) based technique, and a fast implementation of seam carving for size adaptation of videos (FSCAV). The ROI-based retargeting detects important regions like faces, objects, text, and contrast-based saliency regions. A rectangular window within the larger frame is selected that defines the visible area of the target video. If several relevant regions are detected, an artificial camera motion (pan, tilt, or zoom) may change the selected view within a shot. For seam carving, we present two extensions: The first reduces the distortion of straight lines (lines may become curved or disconnected); the second avoids jitter in the target video, limits the large memory requirements and computational effort of seam carving, and makes it applicable to video retargeting. In addition, we present a heuristic that estimates the visual quality of the target video. If the quality drops below a threshold, the ROI-based retargeting is used for this shot. User evaluations confirm a very high visual quality of our approach.
The curvature scale space (CSS) technique, which is also part of the MPEG-7 standard is a robust method to describe complex shapes.The central idea is to analyze the curvature of a shape and derive features from inflection points. A major drawback of the CSS method is its poor representation of convex segments: Convex objects cannot be represented at all due to missing inflection points. We have extended the CSS approach to generate feature points for concave and convex segments of a shape. This generic approach is applicable to arbitrary objects. In the experimental results, we evaluate as a comprehensive example the automatic recognition of characters in images and videos.
We describe the design and implementation of an automatic cameraman for lecture recording. A major problem with traditional lecture recordings is that they tend to be boring for the students, especially if only the slides and the audio of the lecturer are presented. In a first step, we determine the tasks a real cameraman would have, in particular with respect to liveliness of the video. We then adapt these tasks to a computer system and show in detail how they can be implemented. In a second step, we describe how our algorithms support the virtual director system into which the automatic cameraman is integrated. We conclude that lecture recordings can be much more lively and interesting using our approach.
This survey introduces the current state of the art in image and video retargeting and describes important ideas and technologies that have influenced the recent work. Retargeting is the process of adapting an image or video from one screen resolution to another to fit different displays, for example, when watching a wide screen movie on a normal television screen or a mobile device. As there has been considerable work done in this field already, this survey provides an overview of the techniques. It is meant to be a starting point for new research in the field. We include explanations of basic terms and operators, as well as the basic workflow of the different methods.
Positioning systems are one of the key elements required by location-based services. This paper presents the design, implementation and analysis of a positioning system called COMPASS which is based on 802.11-compliant network infrastructure and digital compasses. On the mobile device, COMPASS samples the signal strength values of different access points in its communication range and utilizes the orientation of the user to preselect a subset of the training data. The remaining training data is used by a probabilistic positioning algorithm to determine the position of the user. While prior systems show limited accuracy due to blocking effects caused by the human body, we apply digital compasses to detect the orientations of the users so that we can deal with these blocking effects. After a short period of training our COMPASS system achieves an average error distance of less than 1.65 meters in our experimental environment of 312 square meters.
Figure 1. Our new algorithm compared to scaling and regular seam carving with forward energy. In this example, the width is reduced to 60% of the original size. ABSTRACTIn this paper, we propose a new method to adapt the resolution of images to the limited display resolution of mobile devices. We use the seam carving technique to identify and remove less relevant content in images. Seam carving achieves a high adaptation quality for landscape images and distortions caused by the removal of seams are very low compared to other techniques like scaling or cropping. However, if an image depicts objects with straight lines or regular patterns like buildings, the visual quality of the adapted images is much lower. Errors caused by seam carving are especially obvious if straight lines become curved or disconnected. In order to preserve straight lines, our algorithm applies line detection in addition to the normal energy function of seam carving. The energy in the local neighborhood of the intersection point of a seam and a straight line is increased to prevent other seams from removing adjacent pixels. We evaluate our improved seam carving algorithm and compare the results with regular seam carving. In case of landscape images with no straight lines, traditional seam carving and our enhanced approach lead to very similar results. However, in the case of objects with straight lines, the quality of our results is significantly better.
Virtually every video watermarking technology can benefit from comparison with the original content. For non-blind schemes it is fundamental; for others it is an improvement to increase the watermark's signal-to-noise ratio by subtracting the content that is often noise to the detector. A direct frame-by-frame comparison of the videos is not possible due to the fact that illegal copies of videos usually differ significantly from their originals caused by different spatial resolution or frame rates, geometric distortions from capturing, or targeted attacks. In this paper, we present a software tool that enables the semi-automatic temporal and spatial synchronization of frames and pixels of two similar videos. This process is called registration. We put our focus on utilizing human capabilities with the smallest possible effort, to allow a high overall performance and precision of the registration. An efficient graphical user interface supports the users and visualizes the results of all steps. In addition, we specifically distinguish digitally reproduced copies from analog (camcorded) copies in which two or more frames are blended into a new frame.
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