Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016

A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

Multi-Scale 3D Scene Flow from Binocular Stereo Sequences

Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108

Real-time Visual Flow Algorithms for Robotic Applications

Vision offers important sensor cues to modern robotic platforms. Applications such as control of aerial vehicles, visual servoing, simultaneous localization and mapping, navigation and more recently, learning, are examples where visual information is fundamental to accomplish tasks. However, the use of computer vision algorithms carries the computational cost of extracting useful information from the stream of raw pixel data. The most sophisticated algorithms use complex mathematical formulations leading typically to computationally expensive, and consequently, slow implementations. Even with modern computing resources, high-speed and high-resolution video feed can only be used for basic image processing operations. For a vision algorithm to be integrated on a robotic system, the output of the algorithm should be provided in real time, that is, at least at the same frequency as the control logic of the robot. With robotic vehicles becoming more dynamic and ubiquitous, this places higher requirements to the vision processing pipeline. This thesis addresses the problem of estimating dense visual flow information in real time. The contributions of this work are threefold. First, it introduces a new filtering algorithm for the estimation of dense optical flow at frame rates as fast as 800 Hz for 640x480 image resolution. The algorithm follows a update-prediction architecture to estimate dense optical flow fields incrementally over time. A fundamental component of the algorithm is the modeling of the spatio-temporal evolution of the optical flow field by means of partial differential equations. Numerical predictors can implement such PDEs to propagate current estimation of flow forward in time. Experimental validation of the algorithm is provided using high-speed ground truth image dataset as well as real-life video data at 300 Hz. The second contribution is a new type of visual flow named structure flow. Mathematically, structure flow is the three-dimensional scene flow scaled by the inverse depth at each pixel in the image. Intuitively, it is the complete velocity field associated with image motion, including both optical flow and scale-change or apparent divergence of the image. Analogously to optic flow, structure flow provides a robotic vehicle with perception of the motion of the environment as seen by the camera. However, structure flow encodes the full 3D image motion of the scene whereas optic flow only encodes the component on the image plane. An algorithm to estimate structure flow from image and depth measurements is proposed based on the same filtering idea used to estimate optical flow. The final contribution is the spherepix data structure for processing spherical images. This data structure is the numerical back-end used for the real-time implementation of the structure flow filter. It consists of a set of overlapping patches covering the surface of the sphere. Each individual patch approximately holds properties such as orthogonality and equidistance of points, thus allowing efficient implementations of low-level classical 2D convolution based image processing routines such as Gaussian filters and numerical derivatives. These algorithms are implemented on GPU hardware and can be integrated to future Robotic Embedded Vision systems to provide fast visual information to robotic vehicles

08291 Abstracts Collection -- Statistical and Geometrical Approaches to Visual Motion Analysis

From 13.07.2008 to 18.07.2008, the Dagstuhl Seminar 08291 ``Statistical and Geometrical Approaches to Visual Motion Analysis\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

Novel Motion Anchoring Strategies for Wavelet-based Highly Scalable Video Compression

This thesis investigates new motion anchoring strategies that are targeted at wavelet-based highly scalable video compression (WSVC). We depart from two practices that are deeply ingrained in existing video compression systems. Instead of the commonly used block motion, which has poor scalability attributes, we employ piecewise-smooth motion together with a highly scalable motion boundary description. The combination of this more “physical” motion description together with motion discontinuity information allows us to change the conventional strategy of anchoring motion at target frames to anchoring motion at reference frames, which improves motion inference across time. In the proposed reference-based motion anchoring strategies, motion fields are mapped from reference to target frames, where they serve as prediction references; during this mapping process, disoccluded regions are readily discovered. Observing that motion discontinuities displace with foreground objects, we propose motion-discontinuity driven motion mapping operations that handle traditionally challenging regions around moving objects. The reference-based motion anchoring exposes an intricate connection between temporal frame interpolation (TFI) and video compression. When employed in a compression system, all anchoring strategies explored in this thesis perform TFI once all residual information is quantized to zero at a given temporal level. The interpolation performance is evaluated on both natural and synthetic sequences, where we show favourable comparisons with state-of-the-art TFI schemes. We explore three reference-based motion anchoring strategies. In the first one, the motion anchoring is “flipped” with respect to a hierarchical B-frame structure. We develop an analytical model to determine the weights of the different spatio-temporal subbands, and assess the suitability and benefits of this reference-based WSVC for (highly scalable) video compression. Reduced motion coding cost and improved frame prediction, especially around moving objects, result in improved rate-distortion performance compared to a target-based WSVC. As the thesis evolves, the motion anchoring is progressively simplified to one where all motion is anchored at one base frame; this central motion organization facilitates the incorporation of higher-order motion models, which improve the prediction performance in regions following motion with non-constant velocity

Visual Odometry for Quantitative Bronchoscopy Using Optical Flow

Optical Flow, the extraction of motion from a sequence of images or a video stream, has been extensively researched since the late 1970s, but has been applied to the solution of few practical problems. To date, the main applications have been within fields such as robotics, motion compensation in video, and 3D reconstruction. In this paper we present the initial stages of a project to extract valuable information on the size and structure of the lungs using only the visual information provided by a bronchoscope during a typical procedure. The initial implementation provides a realtime estimation of the motion of the bronchoscope through the patients airway, as well as a simple means for the estimation of the cross sectional area of the airway

A Tomographic-PIV Investigation of Vapor-Induced Flow Structures in Confined Jet Impingement Boiling

Tomographic particle image velocimetry (PIV) is used to study the effect of confinement gap height on the liquid flow characteristics in jet impingement boiling. This first application of tomographic PIV to flow boiling is significant given the complexity of confined two-phase jet impingement. A jet of subcooled wa- ter at a Reynolds number of 5,0 0 0 impinges onto a circular heat source undergoing boiling heat transfer at a constant heat input. Confinement gap heights of 8, 4, and 2 jet diameters are investigated. A visual hull method is used to reconstruct the time-varying regions of the vapor in the flow. The vapor motion is found to govern the liquid flow pattern and turbulence generation in the confinement gap. Time-averaged velocities and regions of turbulent kinetic energy in the liquid are highest for a confinement gap height of 8 jet diameters, with lower velocity magnitude and turbulence being observed for the smaller spac- ings. Coherent vortical structures identified with the λ2 -criterion are found to occur most frequently near the moving vapor interface. The most intense regions of turbulent kinetic energy do not coincide with the location of coherent structures within the flow. Irrotational velocity fluctuations in the liquid phase caused by vapor bubble pinch-offare the primary cause of the high turbulent kinetic energy measured in these regions. At a gap height of H / d = 2 the vapor plume is constrained as it grows from the heat source, causing bulk flow oscillations in the downstream region of the confinement gap

Segmentation based variational model for accurate optical flow estimation.

Chen, Jianing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 47-54).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Related Work --- p.3Chapter 1.3 --- Thesis Organization --- p.5Chapter 2 --- Review on Optical Flow Estimation --- p.6Chapter 2.1 --- Variational Model --- p.6Chapter 2.1.1 --- Basic Assumptions and Constraints --- p.6Chapter 2.1.2 --- More General Energy Functional --- p.9Chapter 2.2 --- Discontinuity Preserving Techniques --- p.9Chapter 2.2.1 --- Data Term Robustification --- p.10Chapter 2.2.2 --- Diffusion Based Regularization --- p.11Chapter 2.2.3 --- Segmentation --- p.15Chapter 2.3 --- Chapter Summary --- p.15Chapter 3 --- Segmentation Based Optical Flow Estimation --- p.17Chapter 3.1 --- Initial Flow --- p.17Chapter 3.2 --- Color-Motion Segmentation --- p.19Chapter 3.3 --- Parametric Flow Estimating Incorporating Segmentation --- p.21Chapter 3.4 --- Confidence Map Construction --- p.24Chapter 3.4.1 --- Occlusion detection --- p.24Chapter 3.4.2 --- Pixel-wise motion coherence --- p.24Chapter 3.4.3 --- Segment-wise model confidence --- p.26Chapter 3.5 --- Final Combined Variational Model --- p.28Chapter 3.6 --- Chapter Summary --- p.28Chapter 4 --- Experiment Results --- p.30Chapter 4.1 --- Quantitative Evaluation --- p.30Chapter 4.2 --- Warping Results --- p.34Chapter 4.3 --- Chapter Summary --- p.35Chapter 5 --- Application - Single Image Animation --- p.37Chapter 5.1 --- Introduction --- p.37Chapter 5.2 --- Approach --- p.38Chapter 5.2.1 --- Pre-Process Stage --- p.39Chapter 5.2.2 --- Coordinate Transform --- p.39Chapter 5.2.3 --- Motion Field Transfer --- p.41Chapter 5.2.4 --- Motion Editing and Apply --- p.41Chapter 5.2.5 --- Gradient-domain composition --- p.42Chapter 5.3 --- Experiments --- p.43Chapter 5.3.1 --- Active Motion Transfer --- p.43Chapter 5.3.2 --- Animate Stationary Temporal Dynamics --- p.44Chapter 5.4 --- Chapter Summary --- p.45Chapter 6 --- Conclusion --- p.46Bibliography --- p.4