Viscous fingering of miscible fluids in an anisotropic radial hele-shaw cell: coexistence of kinetic and surface-tension dendrite morphology types and an exploration of small-scale influences

The evolution of viscous fingering morphology is examined for the case of a system of miscible fluids in an anisotropic radial Hele-Shaw cell. It is shown that dendritic morphologies similar to the kinetic and surface-tension morphology types coexist for this case. The critical role of the means of introducing anisotropy in the Hele-Shaw cell is established, and an explanation of the pattern behavior is offered on the basis of shape discontinuities of the individual elements of the lattice used to induce anisotropy. The ramifications of such an explanation are experimentally verified by demonstrating a clear difference in the morphology evolution in two halves of a single Hele-Shaw cell, one half of which contains square lattice elements, and the other half of which contains circular lattice elements

Shape and the stereo correspondence problem

We examine the implications of shape on the process of finding dense correspondence and half-occlusions for a stereo pair of images. The desired property of the disparity map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities. To zeroth order, piecewise continuity becomes piecewise constancy. Using this approximation, we first discuss an approach for dealing with such a fronto-parallel shapeless world, and the problems involved therein. We then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity. In particular, we emphasize the following geometric fact: a horizontally slanted surface (i.e., having depth variation in the direction of the separation of the two cameras) will appear horizontally stretched in one image as compared to the other image. Thus, while corresponding two images, N pixels on a scanline in one image may correspond to a different number of pixels M in the other image. This leads to three important modifications to existing stereo algorithms: (a) due to unequal sampling, existing intensity matching metrics must be modified, (b) unequal numbers of pixels in the two images must be allowed to correspond to each other, and (c) the uniqueness constraint, which is often used for detecting occlusions, must be changed to an interval uniqueness constraint. We also discuss the asymmetry between vertical and horizontal slant, and the central role of non-horizontal edges in the context of vertical slant. Using experiments, we discuss cases where existing algorithms fail, and how the incorporation of these new constraints provides correct results. 1

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roadmap to the integration of early visual module

View-invariant modeling and recognition of human actions using grammars. Workshop on Dynamical Vision at ICCV’05

In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an automatic keyframe selection process, and are used to automatically construct a probabilistic context-free grammar (PCFG). Given a new single viewpoint video, we can parse it to recognize actions and changes in viewpoint simultaneously. Experimental results are provided. 1

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We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists in general a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo)

The Argus Eye: A New Tool for Robotics

This paper 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 that 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 3D motion estimation are provided

A sensory grammar for inferring behaviors in sensor networks

The ability of a sensor network to parse out observable activities into a set of distinguishable actions is a powerful feature that can potentially enable many applications of sensor networks to everyday life situations. In this paper we introduce a framework that uses a hierarchy of Probabilistic Context Free Grammars (PCFGs) to perform such parsing. The power of the framework comes from the hierarchical organization of grammars that allows the use of simple local sensor measurements for reasoning about more macroscopic behaviors. Our presentation describes how to use a set of phonemes to construct grammars and how to achieve distributed operation using a messaging model. The proposed framework is flexible. It can be mapped to a network hierarchy or can be applied sequentially and across the network to infer behaviors as they unfold in space and time. We demonstrate this functionality by inferring simple motion patterns using a sequence of simple direction vectors obtained from our camera sensor network testbed