Accession Number : ADA615951


Title :   Cloud-Induced Uncertainty for Visual Navigation


Descriptive Note : Master's thesis


Corporate Author : AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT


Personal Author(s) : Gutierrez, Alyssa N


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a615951.pdf


Report Date : 26 Dec 2014


Pagination or Media Count : 118


Abstract : This research addresses the numerical distortion of features due to the presence of clouds in an image. The research aims to quantify the probability of a mismatch between two features in a single image, which will describe the likelihood that a visual navigation system incorrectly tracks a feature throughout an image sequence, leading to position miscalculations. First, an algorithm is developed for calculating transparency of clouds in images at the pixel level. The algorithm determines transparency based on the distance between each pixel color and the average pixel color of the clouds. The algorithm is used to create a dataset of cloudy aerial images. Matching features are then detected between the original and cloudy images, which allows a direct comparison between features with and without clouds. The transparency values are used to segment the detected features into three categories, based on whether the features are located in the regions without clouds, along edges of clouds, or with clouds. The error between features on the cloudy and cloud-free images is determined, and used as a basis for generating a synthetic dataset with statistically similar properties. Lastly, Monte Carlo techniques are used to nd the probability of mismatching.


Descriptors :   *CLOUDS , *NAVIGATION COMPUTERS , *REMOTELY PILOTED VEHICLES , *UNCERTAINTY , AUTONOMOUS NAVIGATION , COMPUTER VISION , DATA BASES , FEATURE EXTRACTION , GAUSSIAN NOISE , IMAGE PROCESSING , INERTIAL NAVIGATION , INTERFERENCE , MAN COMPUTER INTERFACE , MONTE CARLO METHOD , PATTERN RECOGNITION , PROBABILITY DENSITY FUNCTIONS , THESES , VECTOR ANALYSIS


Subject Categories : Pilotless Aircraft
      Statistics and Probability
      Cybernetics
      Air Navigation and Guidance


Distribution Statement : APPROVED FOR PUBLIC RELEASE