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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include realtime, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify & other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

egomotion↗
NASA NTRS2004

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include real-time, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identity other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

egomotion↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Observations of atmospheric 3D winds are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we compare state-of-the-art convolutional neural networks for optical flow in a physics-guided deep learning framework for predicting AMVs. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

Geostationary↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Atmospheric 3D winds in the horizontal and vertical directions are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we use a state-of-the-art convolutional neural network for optical flow (FlowNetS) in a physics-guided deep learning framework for predicting AMVs in the horizontal direction. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations (DYAMOND) for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

geostationary↗
NASA NTRS2004

Real-time Detection of Moving Objects from Moving Vehicles Using Dense Stereo and Optical Flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time. dense stereo system to include realtime. dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop. computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

moving object detection↗
NASA NTRS2013

4DoF Drift Free Navigation Using Inertial Cues and Optical Flow

In this paper, we describe a novel approach in fusing optical flow with inertial cues (3D acceleration and 3D angular velocities) in order to navigate a Micro Aerial Vehicle (MAV) drift free in 4DoF and metric velocity. Our approach only requires two consecutive images with a minimum of three feature matches. It does not require any (point) map nor any type of feature history. Thus it is an inherently failsafe approach that is immune to map and feature-track failures. With these minimal requirements we show in real experiments that the system is able to navigate drift free in all angles including yaw, in one metric position axis, and in 3D metric velocity. Furthermore, it is a power-on-and-go system able to online self-calibrate the inertial biases, the visual scale and the full 6DoF extrinsic transformation parameters between camera and IMU.

reconnissance↗
NASA NTRS1993

Use of optical edge and optical flow rate: Information in the perception and control of ground velocity

Previous research (Denton, 1980; Larish & Flach, 1990; Owen, Wolpert, & Warren, 1984; Awe & Johnson, 1989) suggests that people will use one or both of two optical variables as information for the perception and/or regulation of ground velocity. The first variable, global optical flow rate (FR), reflects the angular optical speed, and is proportional to the observer's ground velocity scaled in altitude units, i.e. velocity divided by altitude. Therefore, flow rate is a reliable indicator of ground velocity only under the condition of constant altitude. The second variable, optical edge rate (ER), reflects ground velocity as the frequency at which the optically specified ground edges pass across some optical region. Therefore, edge rate is an accurate indicator of ground velocity when altitude varies, but not when texture density varies. Since both flow rate and edge rate can vary independently of forward ground velocity, accurate ground-velocity control may depend upon using the best optical variable.

Johnson, Walter W.↗
NASA NTRS1989

Synthetic perspective optical flow: Influence on pilot control tasks

One approach used to better understand the impact of visual flow on control tasks has been to use synthetic perspective flow patterns. Such patterns are the result of apparent motion across a grid or random dot display. Unfortunately, the optical flow so generated is based on a subset of the flow information that exists in the real world. The danger is that the resulting optical motions may not generate the visual flow patterns useful for actual flight control. Researchers conducted a series of studies directed at understanding the characteristics of synthetic perspective flow that support various pilot tasks. In the first of these, they examined the control of altitude over various perspective grid textures (Johnson et al., 1987). Another set of studies was directed at studying the head tracking of targets moving in a 3-D coordinate system. These studies, parametric in nature, utilized both impoverished and complex virtual worlds represented by simple perspective grids at one extreme, and computer-generated terrain at the other. These studies are part of an applied visual research program directed at understanding the design principles required for the development of instruments displaying spatial orientation information. The experiments also highlight the need for modeling the impact of spatial displays on pilot control tasks.

Bennett, C. Thomas↗
NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗
NASA NTRS1993

Extracting heading and temporal range from optic flow: Human performance issues

Pilots are able to extract information about their vehicle motion and environmental structure from dynamic transformations in the out-the-window scene. In this presentation, we focus on the information in the optic flow which specifies vehicle heading and distance to objects in the environment, scaled to a temporal metric. In particular, we are concerned with modeling how the human operators extract the necessary information, and what factors impact their ability to utilize the critical information. In general, the psychophysical data suggest that the human visual system is fairly robust to degradations in the visual display, e.g., reduced contrast and resolution or restricted field of view. However, extraneous motion flow, i.e., introduced by sensor rotation, greatly compromises human performance. The implications of these models and data for enhanced/synthetic vision systems are discussed.

Kaiser, Mary K.↗
DOE OSTI2024

Optical Flow Diagnostics of Counter Fluidization of Gravity-Driven Moving Packed Bed for a CSP Receiver Section Featuring Staggered Array of Cylindrical Pins

Particle fluidized beds have the potential to improve the efficiency of heat transfer in concentrated solar receiver furnace for use in next generation concentrated solar power (CSP) plants. This study presents an experimental investigation on the flow characterization of vertically downward moving packed bed with counter fluidization through an array of jets. To control the bubble size and its distribution in the bubbling fluidized bed, an array of cylindrical pin fins was arranged uniformly across the test article. The flow visualization was performed on the surface of transparent glass coated with electrically conductive materials for electrostatic dissipation purposes. The image acquisition was carried out via high-speed camera at a frequency of ~ 1kHz. The acquired images were analyzed in pairs with the help of a modern optical flow algorithm capable of calculating the movement of dense particle flow in the fluidized bed by tracking the light intensity of each predefined window of the frames. A comparison of fluidized beds with plane and pin-finned channels revealed distinct bubble behavior. Pin-finned channels were found to produce a larger number of small-sized bubbles, while plane channels generated fewer but larger bubbles at any given instant. The presence of pin fins was observed to reduce bubble size by preventing bubble merging and splitting larger bubbles when they encountered a pin.

concentrating solar power↗
NASA NTRS1998

Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

Non-NASA Center↗
DOE OSTI2024

High throughput automated characterization of enamel microstructure using synchrotron tomography and optical flow imaging

The remarkable damage-tolerance of enamel has been attributed to its hierarchical microstructure and the organized bands of decussated rods. A thorough characterization of the microscale rod evolution within the enamel is needed to elucidate this complex structure. While prior efforts in this area have made use of single particle tracking to track a single rod evolution to various degrees of success, such a process can be both computationally and labor intensive, limited to the evolution path of a single rod, and is therefore prone to error from potentially tracking outliers. Particle image velocimetry (PIV) is a well-established algorithm to derive field information from image sequences for processes that are time-dependent, such as fluid flows and structural deformation. In this work, we demonstrate the use of PIV in extracting the full-field microstructural distribution of rods within the enamel. Further, enamel samples from a wild African lion were analyzed using high-energy synchrotron X-ray micro-tomography. Results from the PIV analysis provide sufficient full-field information to reconstruct the growth of individual rods that can potentially enable rapid analysis of complex microstructures from high resolution synchrotron datasets. Such information can serve as a template for designing damage-tolerant bioinspired structures for advanced manufacturing.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Time-to-Passage Judgments in Nonconstant Optical Flow Fields

The time until an approaching object will pass an observer (time to passage, or TTP) is optically specified by a global flow field even in the absence of local expansion or size cues. Kaiser and Mowafy have demonstrated that observers are in fact sensitive to this global flow information. The present studies investigate two factors that are usually ignored in work related to TTP: (1) non-constant motion functions and (2) concomitant eye rotation. Non-constant velocities violate an assumption of some TTP derivations, and eye rotations may complicate heading extraction. Such factors have practical significance, for example, in the case of a pilot accelerating an aircraft or executing a roll. In our studies, a flow field of constant-sized stars was presented monocularly on a large screen. TIP judgments had to be made on the basis of one target star. The flow field varied in its acceleration pattern and its roll component. Observers did not appear to utilize acceleration information. In particular, TTP with decelerating motion were consistently underestimated. TTP judgments were fairly robust with respect to roll, even when roll axis and track vector were decoupled. However, substantial decoupling between heading and track vector led to a decrement in performance, in both the presence and the absence of roll.

Kaiser, Mary K.↗
NASA NTRS1992

Enhanced orbital gyrocompassing by the optical flow sensed by an Earth-pointing camera

A new method for improving the orbital gyrocompassing process involving the attitude angle estimation of an earth-pointing satellite in low-Earth orbit uses an electro-optical sensor for direct measurement of the satellite azimuth angle. Simulations have shown that this additional measurement drastically reduces the estimator convergence time, especially when the sun sensor is rendered ineffective, e.g., by high solar elevations. The azimuth-sensing method is based on estimation of the image shift between successive picture frames of an on-board, Earth-pointing, charge-coupled device (CCD) full-matrix camera. The shift-estimation algorithm is based on minimizing a cost function which expresses mean-squared differences in brightness patterns of selected areas of the two frames. An extensive evaluation program with a computer-controlled 2-axis light table and actual satellite images has demonstrated high robustness for a wide range of variation of parameters including image texture content; camera focal length; sampling rate; and number of pixels processed. It was shown to be possible to estimate the azimuth angle within 0.1-0.2 degrees, for a suitably chosen parameter set.

Topaz, Leora↗
NASA NTRS1988

Perceived change in orientation from optic flow in the central visual field

The effects of internal depth within a simulation display on perceived changes in orientation have been studied. Subjects monocularly viewed displays simulating observer motion within a volume of randomly positioned points through a window which limited the field of view to 15 deg. Changes in perceived spatial orientation were measured by changes in posture. The extent of internal depth within the display, the presence or absence of visual information specifying change in orientation, and the frequency of motion supplied by the display were examined. It was found that increased sway occurred at frequencies equal to or below 0.375 Hz when motion at these frequencies was displayed. The extent of internal depth had no effect on the perception of changing orientation.

Dyre, Brian P.↗
NASA NTRS1990

Simple technique for optical flow estimation

The theoretical principles, design, and implementation of an image-motion detection system with possible robotics and autonomous-navigation applications are discussed. The operation of a simple one-dimensional second-derivative edge-motion detector is explained; its extension to a two-dimensional motion sensor based on the Laplacian of a Gaussian (Marr and Hildreth, 1980) is outlined; a center-surround array of 13 of these sensors (12 in a circle around a central sensor) is proposed; and processing techniques to overcome the aperture problem are examined. Simple video images involving unidirectional motion are then analyzed using a prototype version of this design; the sensors employed are based on a 15 x 15-pixel mask made up of 13 7 x 7-pixel kernels. Accurate measurements of image velocity are obtained, as required for navigation through complex environments. It is pointed out that the procedure is noniterative and well suited to implementation as a parallel network.

Perrone, John A.↗