Abstract not found

An increasing number of computer vision applications require on-line processing of data streams, preferably in real-time. This trend is fueled by the mainstream availability of low cost imaging devices, and the steady increase in computing power. To meet these requirements, applications should manipulate data streams in concurrent processing environments, taking into consideration scheduling, planning and synchronization issues. Those can be solved in specialized systems using ad hoc designs and implementations, that sacrifice flexibility and generality for performance. Instead, we propose a generic, extensible, modular software architecture. The cornerstone of this architecture is the Flow Scheduling Framework (FSF), an extensible set of classes that provide basic synchronization functionality and control mechanisms to develop data-stream processing components. Applications are built in a data-flow programming model, as the specification of data streams flowing through processing nodes, where they can undergo various manipulations. We describe the details of the FSF data and processing model that supports stream synchronization in a concurrent processing framework. We demonstrate the power of our architecture for video processing with a real-time video stream segmentation application. We also show dramatic throughput improvement over sequential execution models with a port of the pyramidal Lukas-Kanade feature tracker demonstration application from the Intel Open Computer Vision library. 1

Previous methods for estimating the motion of an observer through a static scene require that image velocities can be measured. For the case of motion through a cluttered 3D scene, however, measuring optical flow is problematic because of the high density of depth discontinuities. This paper introduces a method for estimating motion through a cluttered 3D scene that does not measure velocities at individual points. Instead the method measures a distribution of velocities over local image regions. We show that motion through a cluttered scene produces a bowtie pattern in the power spectra of local image regions. We show how to estimate the parameters of the bowtie for different image regions and how to use these parameters to estimate observer motion. We demonstrate our method on synthetic and real data sequences. 1.

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or depth coherence can be assumed. Previous methods for measuring optical snow have used a wedge filter in a spatiotemporal frequency domain. The PCA method is also based on the spatiotemporal frequency domain analysis, but examines a different geometry property of the spectrum. We compare the results of the PCA method to the previous methods using both real and synthetic sequences. 1

Abstract — We propose an approach for vision-based navigation of underwater robots that relies on the use video mosaics of the sea bottom as environmental representations for navigation. We present a methodology for building high quality video mosaics of the sea bottom, in a fully automatic manner, that ensures global spatial coherency. During navigation, a set of efficient visual routines are used for the fast and accurate localization of the underwater vehicle with respect to the mosaic. These visual routines were developed taking into account the operating requirements of real-time position sensing, error bounding and computational load. A visual servoing controller, based on the vehicle kinematics, is used to drive the vehicle along a computed trajectory, specified in the mosaic, while maintaining constant altitude. The trajectory towards a goal point is generated online to avoid undefined areas in the mosaic. We have conducted a large set of sea trials, under realistic operating conditions. This paper demonstrates that, without resorting to additional sensors, visual information can be used to create environment representations of the sea bottom (mosaics) and support long runs of navigation in a robust manner. Index Terms — Underwater computer vision, video mosaics, visual servoing, trajectory reconstruction, uncertainty estimation