Almon David Ing

I am a graduate researcher affiliated with the Department of Psychology and the Center for Perceptual Systems at the University of Texas at Austin.  I expect to receive my PhD in Psychology sometime before May of 2009.

My research interests are computational neuroscience, vision, perception, and categorization modeling.  My graduate advisor is Bill Geisler.  Bill has trained me to study natural image statistics and to run psychophysical experiments.  Earlier in my graduate career I was trained by Todd Maddox to study category learning and to run psychophysical experiments.  I began my career as an undergraduate at UCSB where I studied computational neuroscience, categorization, and working memory with Greg Ashby.

Perceptual systems have evolved in response to physical properties of natural environments. The human visual system (like other primate visual systems) has evolved to navigate through foliage-rich environments which are dominated by leaves. So if we want to understand the visual systems of primates (including humans), we should probably understand how properties of foliage-rich images can be exploited to solve the Leaf Segmentation Problem.

On purely computational grounds, the Leaf Segmentation Problem is one of the most difficult image segmentation problems in existence.  I believe the solution to this problem will robustly solve most other image segmentation problems.  Therefore, finding the problem's solution will be a major achievement on our way to finding a general solution to the Image Segmentation Problem.

I study natural scene statistics by studying the Leaf Segmentation Problem. This involves measuring how properties of foliage-rich images can be exploited to achieve optimal performance in many tasks related to image segmentation.

ObjectParser Software & Databases

The ObjectParser software application allows a user to segment images quickly and accurately because the user can easily pan and zoom the image during segmentation.  The segmentation data can be easily analyzed because it is saved in a text format (XML-based).  Since many researchers conduct analyses using Matlab, I provide Matlab code for reading the data files.

I designed ObjectParser as a way to collect the ground-truth associated with any kind of image segmentation problem.  In a nutshell, the software allows a user to easily parse objects from images by defining polygons to demarcate object surfaces.  The user can fully specify the nature of any occlusion (including the ability to specify foreground-background information).  The user can also trace and classify surface contours (e.g. surface markings, sharp shadow boundaries).

Multivariate Categorization Library

A major proportion of problems in natural scene statistics fall under the heading of categorization problems.  It is essential to compute ideal categorization performance in order to solve these problems.  An ideal categorization system should be able to estimate the probability of category membership given an uncategorized stimulus.  Determining exactly how an ideal system should do this is extremely complicated.

I developed the Multivariate Categorization Library to make determining ideal categorization performance easier.  Currently I am distributing this library as a collection of Matlab functions.  The library makes it easy to fit and plot hyperquadric decision functions and exemplar (kernel density) decision functions alongside the original multivariate data.  Simple examples are provided to make it easy for a user to learn how the library works.

Presentations & Publications

Ing, A.D.  (in preparation)  PhD Dissertation.

Ing, A.D.  (in preparation)  Multivarite Categorization Analysis.

Ing, A.D. & Geisler, W.S. (in preparation)  Contour classification statistics for leaf segmentation.

Ing, A.D. & Geisler, W.S. (in preparation)  Foreground-background statistics for leaf segmentation.

Ing, A.D. & Geisler, W.S. (in preparation)  Patch pair statistics for leaf segmentation.

Ing, A.D. & Geisler, W.S.  (2008)  Natural categorization statistics.  Lecture to Cognition & Perception area given October 3, 2008.   [pptx]

Ing, A.D., & Geisler, W.S. (2008)  Natural image statistics and the problem of leaf segmentation.  NETI 2008 Workshop Poster.  [pdf]

Ing, A.D., & Geisler, W.S. (2008) Patch pair statistics for leaf segmentation [Abstract]. Journal of Vision, 8(6):69, 69a, http://journalofvision.org/8/6/69/, doi:10.1167/8.6.69. [pptx]

Ing, A.D., & Geisler, W.S. (2006) Ribbon analysis of contours in natural images [Abstract]. Journal of Vision, 6(6):103, 103a, http://journalofvision.org/6/6/103/, doi:10.1167/6.6.103. [ppt]

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