Morphological and wavelet transforms for object detection and image processing

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

Liquid crystal polarization-converting devices for edge and corner extractions of images using optical wavelet transforms

Optical properties of radially polarized light generated by liquid crystal (LC) polarization-converting devices with two LC molecular orientations (+/-180 degrees and +/-90 degrees twist modes) are measured and the applicability to optical wavelet transforms (WTs) are discussed. It is found that the radially polarized lights in both the +/-180 degrees and +/-90 degrees twist modes can be regarded as wavelets. Based on the principle of WT, feature extractions of a simple binary image are carried out by applying the LC polarization-converting device to a shadow-casting system. Unique feature extraction properties of the shadow-casting system using the LC polarization-converting device and a LC microlens array are discussed. It is experimentally confirmed that edge and corner features are successfully extracted using the LC polarization-converting device with +/-180 degrees and +/-90 degrees twist modes, respectively.

Detection filters and algorithm fusion for ATR

Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region. We consider new detection algorithms and the fusion of their outputs to reduce the probability of false alarm P(FA) while maintaining high probability of detection P(D). Emphasis is given to detecting obscured targets in infrared imagery.