We suggest the use of extended landmarks, such as shorelines, creeks, tree lines, and railroads, as well as roads for autonomous navigation of an unmanned air vehicle (UAV). In particular, we recommend the use of shorelines, because of their common availability, their ease of detection, and their significance in terms of events happening along them. Monitoring coastlines and waterways from low flying UAVs has many applications for military and civilian use. We report the development of a vision system that has enabled a prototype UAV to follow shorelines autonomously (without requiring maps or GPS). Using a near-infrared sensor the vision system distinguishes water from land (irrespective of water's color) and issues commands to the autopilot to follow the coastline or the riverbank. One insight of this problem is that the control algorithm could be integrated deeply with the vision system. This has the benefit of delaying smoothing/regularization so that it could occur in the context of the control coordinate system rather than the image or ground coordinate system. The algorithm itself is simple, but it possibly points the way to future algorithms which could more closely couple image processing and control. Furthermore, the experience gained in this work may be of value in the development of vision systems for following other types of paths.

City-scale tracking of all objects visible in a camera network or aerial video surveillance is an important tool in surveillance and traffic monitoring. We propose a framework for human guided tracking based on explicitly considering the context surrounding the urban multi-vehicle tracking problem. This framework is based on a standard (but state of the art) probabilistic tracking model. Our contribution is to explicitly detail where human annotation of the scene (e.g. “this is a lane”), a track (e.g. “this track is bad”), or a pair of tracks (e.g. “these two tracks are confused”) can be naturally integrated within the probabilistic tracking framework. For an early prototype system, we offer results and examples from a dense urban traffic camera network tracking, querying data with thousands of vehicles over 30 minutes.

In Brief Although it's been implemented in countless nursing departments, shared governance lacks a standardized model. What, exactly, is it? And what isn't it?

Autonomous Unmanned Aerial Vehicle (UAV) guidance without resorting to preprogrammed GPS signals promises to increase the flexibility in tasking for single or multiple UAVs. This paper reports on a guidance and autopilot system which successfully navigated along a curved shoreline. The project demonstrated that the near infrared (NIR) spectrum is particularly good for navigating on shorelines, since the land and water are easily distinguishable in these wavelengths. Also presented is a new control technique which is able to find control parameters in the image space. This overcomes the need for specialized edge extraction and path generation. Attitude control was maintained by the use of matched long wave infrared thermopiles.

This dissertation introduces new mathematical constraints that enable us, for the first time, to investigate the correspondence problem using texture rather than point and lines. These three multilinear constraints are formulated on parallel equidistant lines embedded in a plane. We choose these sets of parallel lines as proxies for Fourier harmonics embedded on a plane as a sort of “texture atom”. From these texture atoms we can build up arbitrarily textured surfaces in the world. If we decompose these textures in a Fourier sense rather than as points and lines, we use these new constraints rather than the standard multifocal constraints such as the epipolar or trifocal. We propose some mechanisms for a possible feedback solution to the correspondence problem. As the major application of these constraints, we describe a multicamera calibration system written in C and MATLAB which will be made available to the public. We describe the operation of the program and give some preliminary results.

network, arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system's own motion and, consequently, the estimation of shape models from the individual cameras. The rea-son is, inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations, which pro-vide additional advantages for segmentation. Algorithms for the calibration of the system and the three-dimensional (3-D) motion estimation are provided.

We investigate the camera geometry of lines parallel in the world. In particular, we formalize the known rotational constraints and add new linear constraints on camera position. The constraints on camera position do not require the cameras to be viewing the same lines, thus providing applications for occluded scenes and calibration of cameras for which fields of view do not intersect. The constraints can also be viewed as constraints of camera geometry with planar patch coordinate systems, and provide a way to investigate texture in a deeper way than has been done to date.

This article describes an imaging system that has been designed to facilitate robotic tasks of motion. The system consists of a number of cameras in a network, arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system's own motion and, consequently, the estimation of shape models from the individual cameras. The reason is, inherent ambiguities of confusion between translation and rotation disappear. Pairs of cameras may also be arranged in multiple stereo configurations, which provide additional advantages for segmentation. Algorithms for the calibration of the system and the three-dimensional (3-D) motion estimation are provided.