Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.

This article describes a method for reducing the shape distortions due to scale-space smoothing that arise in the computation of 3-D shape cues using operators (derivatives) de ned from scale-space representation. More precisely, we are concerned with a general class of methods for deriving 3-D shape cues from 2-D image data based on the estimation of locally linearized deformations of brightness patterns. This class

Researchers in computer vision have primarily studied the problem of visual reconstruction of environmental structure that is plainly visible. In this thesis, the conventional goals of visual reconstruction are generalized to include both visible and occluded forward facing surfaces. This larger fraction of the environment is termed the anterior surfaces. Because multiple anterior surface neighborhoods project onto a single image neighborhood wherever surfaces overlap, surface neighborhoods and image neighborhoods are not guaranteed to be in one-to-one correspondence, as conventional "shape-from" methods assume. The result is that the topology of threedimensional scene structure can no longer be taken for granted, but must be inferred from evidence...

In this paper, we present a robust and computationally efficient technique for estimating the focus of expansion (FOE) of an optical flow field, using fast partial search. For each candidate location on a discrete sampling of the image area, we generate a linear system of equations for determining the remaining unknowns, viz. rotation and inverse depth. We compute the least squares error of the system without actually solving the equations, to generate an error surface that describes the goodness of fit across the hypotheses. Using Fourier techniques, we prove that given an N \Theta N flow field, the FOE can be estimated in O(N 2 log N) operations. Since the resulting system is linear, bounded perturbations in the data lead to bounded errors. We support the theoretical development and proof of our algorithm with experiments on synthetic and real data. Through a series of experiments on synthetic data, we prove the correctness, robustness and operating envelope of our algorithm. We d...

This paper describes an algorithm for recognizing known objects in an unstructured environment (e.g. landmarks) from measurements acquired with a single monochrome television camera mounted on a mobile observer. The approach is based on the concept of an entropy map, which is used to guide the mobile observer along an optimal trajectory that minimizes the ambiguity of recognition as well as the amount of data that must be gathered. Recognition itself is based on the optical flow signatures that result from the camera motion - signatures that are inherently ambiguous due to the confounding of motion, structure and imaging parameters. We show how gaze planning partially alleviates this problem by generating trajectories that maximize discriminability. A sequential Bayes approach is used to handle the remaining ambiguity by accumulating evidence for different object hypotheses over time until a clear assertion can be made. Results from an experimental recognition system using a gantry-mounted television camera are presented to show the effectiveness of the algorithm on a large class of common objects. 1

A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV’s survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms. Key words: Helicopter, optic flow, obstacle-avoidance, urban environment Airborne devices are specific platforms whose control raises distinctive difficulties as compared to ground robots. For instance, they can hardly rely upon usual sensors to navigate, especially if the challenge is to let them move in an urban environment – because infra-red sensors are sensitive to external light

The task of sensor planning for object search is formulated and a strategy for this task is proposed. The searcher is assumed to be a mobile platform equipped with an active camera and a method of calculating depth, like a stereo or laser range finder. The formulation casts sensor planning as an optimization problem: the goal is to maximize the probability of detecting the target with minimum cost. The search region is thus characterized by the probability distribution of the presence of the target. The control of the sensing parameters depends on the current state of the search region and the detecting abilities of the recognition algorithm. In order to efficiently determine the sensing actions over time, the huge space of possible actions is reduced to a finite set of actions that must be considered. The result of each sensing operation is used to update the status of the search space.

Stabilization of gaze is a major functional prerequisite for robots exploring the environment. The main reason for a “steady-image” requirement is to prevent the robot’s own motion to compromise its “visual functions”. In this paper we present an artificial system, the LIRA robot head, capable of controlling its cameras/eyes to stabilize gaze. The system features a stabilization mechanism relying on principles exploited by natural systems: an inertial sensory apparatus and images of space-variant resolution. The inertial device measures angular velocities and linear acceleration along the vertical and horizontal fronto-parallel axes. The space-variant image geometry facilitates real-time computation of optic flow and the extraction of first-order motion parameters. Experiments which describe the performance of the LIRA robot head are presented. The results show that the stabilization mechanism improves the reactivity of the system to changes occurring suddenly at new spotted locations.

This work introduces the space envelope, a shape model based upon a boundary description. Instead of modeling a single object (or solid), a space envelope encloses a volume of empty space. The advantage is that in any given view, there may be any number of objects, this number being difficult to determine from pixel-data alone. However, there is always one, and only one, volume of visible empty space. Once a model has been constructed defining the space envelope, higher-order operations may be applied to reason about the scene's content. For instance, surface geometries and topology could yield insight into the number of visible objects. The enclosed empty volume may also be used for vision-based navigation (known free-space), while the surfaces are used for view correspondences. Algorithms to automatically construct a planar boundary representation (b-rep) space envelope from a range image are presented. Results for testing the algorithms on over 400 images from four different range c...

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