Jeffrey W. McCandless

Detection of obstacles in the flight path of an aircraft

The National Aeronautics and Space Administration (NASA), along with members of the aircraft industry, recently developed technologies for a new supersonic aircraft. One of the technological areas considered for this aircraft is the use of video cameras and image-processing equipment to aid the pilot in detecting other aircraft in the sky. The detection techniques should provide high detection probability for obstacles that can vary from subpixel to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. Approaches are described here to detect airborne obstacles on collision course and crossing trajectories in video images captured from an airborne aircraft. In both cases the approaches consist of an image-processing stage to identify possible obstacles followed by a tracking stage to distinguish between true obstacles and image clutter, based on their behavior. For collision course object detection, the image-processing stage uses morphological filter to remove large-sized clutter. To remove the remaining small-sized clutter, differences in the behavior of image translation and expansion of the corresponding features is used in the tracking stage. For crossing object detection, the image-processing stage uses low-stop filter and image differencing to separate stationary background clutter. The remaining clutter is removed in the tracking stage by assuming that the genuine object has a large signal strength, as well as a significant and consistent motion over a number of frames. The crossing object detection algorithm was implemented on a pipelined architecture from DataCube and runs in real time. Both algorithms have been successfully tested on flight tests conducted by NASA.

Performance characterization of the dynamic programming obstacle detection algorithm

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.

Real-time obstacle detection system for high speed civil transport supersonic aircraft

The High Speed Civil Transport (HSCT) supersonic commercial aircraft under development by National Aeronautics and Space Administration (NASA) and its partners is expected to include an eXternal Visibility System (XVS) to aid the pilots limited view through their cockpit windows. XVS obtains video images using high resolution digital cameras mounted on the aircraft and directed outside the aircraft. The images captured by the XVS provide an opportunity for automatic computer analysis in real-time to alert pilots of potential hazards in the flight path. The system is useful to help pilots make decisions and avoid air collision. In this paper, we describe the design, implementation, and evaluation of such a computer vision system. Using this system, real-time image data was recently obtained successfully from night tests conducted at NASA Langley Research Center. The system successfully detected and tracked translating objects in real-time during the night test. The system is described in detail so that other researchers can easily replicate the work.

Detection of aircraft in video sequences using a predictive optical flow algorithm

This paper presents a computer vision algorithm that segre- gates spurious optical flow artifacts to detect a moving object. The algo- rithm consists of six steps. First, the pixels in each image are shifted to compensate for camera rotation. Second, the images are smoothed with a spatiotemporal Gaussian filter. Third, the optical flow is computed with a gradient-based technique. Fourth, optical flow vectors with small mag- nitudes are discarded. Fifth, vectors with similar locations, magnitudes, and directions are clustered together using a spatial consistency test. Sixth, similar optical flow vectors are extended temporally to make pre- dictions about future optical flow locations, magnitudes, and directions in subsequent frames. The actual optical flow vectors that are consistent with those predictions are associated with a moving object. This algo- rithm was tested on images obtained with a video camera mounted be- low the nose of a Boeing 737. The camera recorded two sequences containing a second flying aircraft. The algorithm detected the aircraft in 82% of the frames from the first sequence and 78% of the frames from the second sequence. In each sequence, the false-alarm rate was zero. These results illustrate the effectiveness of using a comprehensive pre- dictive technique when detecting moving objects.

Influence of Head Motion on the Judged Distance of Monocularly Presented Virtual Objects

Human subjects localized a monocularly viewed, space-stabilized virtual object presented on a head-mounted, see-through display. They either kept their head stationary or rocked it laterally to produce motion parallax. Their distance estimates had less variability in a head moving condition than in a head stationary condition, but in general were much less precise and much less accurate than comparable stereo-based localizations.

Real-Time Implementation of Obstacle Detection Algorithms on a Datacube MaxPCI Architecture

The high-speed civil transport (HSCT) aircraft has been designed with limited cockpit visibility. To handle this, the National Aeronautics and Space Administration (NASA) has proposed an external visibility system (XVS) to aid pilots in overcoming this lack of visibility. XVS obtains video images using high-resolution cameras mounted on and directed outside the aircraft. Images captured by the XVS enable automatic computer analysis in real-time, and thereby alert pilots about potential flight path hazards. Thus, the system is useful in helping pilots avoid air collisions. In this study, a system was configured to capture image sequences from an on-board high-resolution digital camera at a live video rate, record the images into a high-speed disk array through a fiber channel, and process the images using a Datacube MaxPCI machine with multiple pipelined processors to perform real-time obstacle detection. In this paper, we describe the design, implementation, and evaluation of this computer vision system. Using this system, real-time obstacle detection was performed and digital image data were obtained successfully in flight tests conducted at NASA Langley Research Center in January and September 1999. The system is described in detail so that other researchers can easily replicate the work.

Gene therapy for epilepsy

Epilepsy is a devastating disorder which affects about 1.5–3 million people in the United States (about 0.5–1.0% of the population), and as many as 50 million worldwide. Antiepileptic drugs (AEDs) can successfully treat about 2/3 of epileptic patients (Nadkarni et al. 2005). Thus, many patients are left with intractable seizures, with surgery as a possible last resort.