Dartmouth logo Dartmouth College Computer Science
Technical Report series
CS home
TR home
TR search TR listserv
By author: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
By number: 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986

Out of the Depths: Image Statistics of Space, Water, and the Minuscule World
Nimit S. Dhulekar
Dartmouth TR2011-678

Abstract: In images of natural scenes, a consistent relationship exists between spectral power and spatial frequency. The power spectrum falls off with a form 1/f^p as spatial frequency f increases, with values of p approximately equal to 2. To quantify the extent to which this statistical characteristic is exhibited by other classes of images, we examined astronomical, underwater, and microscale images. It was found that this property holds for all three categories of images, although the value of p varies in the range 1.76 to 2.37. The second statistical characteristic computed was the angular spread of the power spectrum. This metric is a means to verify whether the image categories investigated tend to display more power in the horizontal and vertical orientations, akin to natural images. It was found that these image categories have primarily isotropic spectral signatures with a much reduced anisotropy as compared to natural images. Along similar lines, we introduce a new measure called the anisotropy index which compares the power in the horizontal and vertical orientations with power in oblique orientations. The statistics thus presented are for the ensemble power spectrum. We also construct 4 classifiers to distinguish between natural images and astronomical, microscale, and underwater images. The k-nearest neighbor classifier with Mahalanobis distance had the best accuracy of 70.5% on the training set and 66.9% on the test set, for correctly identifying natural images. From these classifiers, we can not only view the confusion in classification among the investigated image categories, but also the difference in statistics as compared to natural images. These classifiers also make it possible to verify that the images in a particular class display statistics similar to that of the ensemble image.

Note: M.S. Thesis. Advisor: Richard H. Granger.


PDF PDF (732KB)

Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]

Or copy and paste:
   Nimit S. Dhulekar, "Out of the Depths: Image Statistics of Space, Water, and the Minuscule World." Dartmouth Computer Science Technical Report TR2011-678, September 2010.


Notify me about new tech reports.

Search the technical reports.

To receive paper copy of a report, by mail, send your address and the TR number to reports AT cs.dartmouth.edu


Copyright notice: The documents contained in this server are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Technical reports collection maintained by David Kotz.