Translator Disclaimer
11 June 2015 Information fusion performance evaluation for motion imagery data using mutual information: initial study
Author Affiliations +
As technology and internet use grows at an exponential rate, video and imagery data is becoming increasingly important. Various techniques such as Wide Area Motion imagery (WAMI), Full Motion Video (FMV), and Hyperspectral Imaging (HSI) are used to collect motion data and extract relevant information. Detecting and identifying a particular object in imagery data is an important step in understanding visual imagery, such as content-based image retrieval (CBIR). Imagery data is segmented and automatically analyzed and stored in dynamic and robust database. In our system, we seek utilize image fusion methods which require quality metrics. Many Image Fusion (IF) algorithms have been proposed based on different, but only a few metrics, used to evaluate the performance of these algorithms. In this paper, we seek a robust, objective metric to evaluate the performance of IF algorithms which compares the outcome of a given algorithm to ground truth and reports several types of errors. Given the ground truth of a motion imagery data, it will compute detection failure, false alarm, precision and recall metrics, background and foreground regions statistics, as well as split and merge of foreground regions. Using the Structural Similarity Index (SSIM), Mutual Information (MI), and entropy metrics; experimental results demonstrate the effectiveness of the proposed methodology for object detection, activity exploitation, and CBIR.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Samuel M. Grieggs, Michael J. McLaughlin, Soundararajan Ezekiel, and Erik Blasch "Information fusion performance evaluation for motion imagery data using mutual information: initial study", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730A (11 June 2015);


A universal reference-free blurriness measure
Proceedings of SPIE (January 24 2011)
Social image quality
Proceedings of SPIE (January 24 2011)
Research on subjective stereoscopic image quality assessment
Proceedings of SPIE (January 19 2009)
New vistas in image and video quality
Proceedings of SPIE (March 14 2007)

Back to Top