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Computational Perception Laboratory

Computational Perception Laboratory

Visual systemWe are interested in understanding the information processing performed by biological sensory systems. Currently we are broadly interested in the mid-level vision problems of scene segmentation and region grouping. We are currently working on the problem of how the early visual system combines multiple cues to detect boundaries between image regions. We make use of psychophysical and machine learning approaches in the hopes of providing insights into both basic sensory function and computer vision applications. Research is currently funded by an NIH R-15 AREA Grant: NIH-R15-EY032732-01

Vision Science

Combination of first- and second-order cues for boundary segmentation

first- and second-order boundary cues

Natural boundaries are defined by a number of cues, including differences in luminance, color and texture (DiMattina, Fox, & Lewicki 2012), all of which are integrated in perceptual tasks. However, the exact mathematical rules for cue combination for boundary segmentation, when boundaries are defined by first-order and second-order cues, remain little explored. We are working on studying how cues are combined for boundary segmentation using artificial boundaries combining luminance, texture and color cues, in the hope of better understanding the mechanisms of natural boundary segmentation. This work is a collaboration with Curtis Baker and Fred Kingdom at McGill University.


Behavioral decoding of deep neural networks for edge detection/classification

Texture NetworkPrevious computational work has shown that deep neural networks trained on object recognition tasks learn intermediate layer representations with nonlinear "receptive field" properties similar to those observed in visual areas V2 and V4. I am currently extending this line of inquiry by analyzing the performance of such networks on mid-level vision tasks like boundary segmentation and shadow detection, and training behavioral decoding models based on these networks to predict human psychophysical observations.

Other Interests

Cognition & Perception

In addition to these main interests in computational vision, I also work in a couple of other areas of Cognitive/Behavioral Science collaboratively with experts in those fields. I have worked with Dr. Nate Pipitone (Evolutionary Psychology/Behavioral Neuroscience) on describing the visual characteristics of stimuli inducing Trypophobia (the fear of holes and bumps).

Statistical Machine Learning 

I have a strong interest in modeling data and developing better ways to collect data in experiments. One very useful tool for doing so are neural networks, but a longstanding problem with these methods are questions of architecture optimization and model selection. I am working in my spare time on developing novel methodologies for simultaneously estimating architecture and parameters of neural models.

Journal Articles and Invited Reviews

DiMattina, C., Burnham, J.J., Guner, B.N., & Yerxa, H.B. (2022). Distinguishing shadows from surface boundaries using local achromatic cues. PLoS Computational Biology 18(9): e1010473.  [code] [data]

Pipitone, R.N., DiMattina, C., Martin, E.R., Pavela Banai, I., Bellmore, K.N., DeAngelis, M. (2022). Evaluating the 'skin-disease-avoidance' and 'dangerous-animal' frameworks for understanding Trypophobia. Cognition & Emotion, DOI: 10.1080/02699931.2022.2071236

DiMattina, C. (2022). Luminance texture boundaries and luminance step boundaries are segmented using different mechanisms. Vision Research 190: 107968

DiMattina, C., & Baker, C.L., Jr. (2021). Segmenting surface boundaries using luminance cues. Scientific Reports 11: 10074. 

Pipitone, R.N., & DiMattina, C. (2020). Object clusters or spectral energy? Assessing the relative contributions of image phase and amplitude spectra to trypophobia. Frontiers in Psychology (Perception Science) 11: 1847.

DiMattina, C., Baker, C.L. Jr. (2019).  Modeling second-order boundary perception: A machine learning approach. PLoS Computational Biology 15(3): e1006829.

DiMattina, C., & Zhang, K. (2017). Adaptive stimulus optimization. In: Encyclopedia of Computational Neuroscience (2nd Ed.). Springer.

DiMattina, C. (2016). Comparing models of contrast gain using psychophysical experiments. Journal of Vision 16(9): 1.

DiMattina, C. (2015). Fast adaptive estimation of multidimensional psychometric functions. Journal of Vision 15(9): 5.

DiMattina, C., & Zhang, K. (2013). Adaptive stimulus optimization for sensory systems neuroscience. Frontiers in Neural Circuits 7: 101.

DiMattina, C., Fox, S.A., & Lewicki, M.S. (2012). Detecting natural occlusion boundaries using local cues. Journal of Vision 12(13): 15.

DiMattina, C., & Zhang, K. (2011). Active data collection for efficient estimation and comparison of nonlinear neural models. Neural Computation 23(9): 2242-2288.

DiMattina, C., & Zhang, K. (2010). How to modify a neural network gradually without changing its input-output functionality. Neural Computation 22(1): 1-47.

DiMattina, C., & Zhang, K. (2008). How optimal stimuli for sensory neurons are constrained by network architecture. Neural Computation 20 (3), 668-708.

DiMattina, C., & Wang, X. (2006). Virtual vocalization stimuli for investigating neural representations of species-specific vocalizations. Journal of Neurophysiology 95(2): 1244-1262.

Kotak, V.C., DiMattina, C., & Sanes D.H. (2001). GABAB and Trk Receptor Signaling Mediates Long-Lasting Inhibitory Synaptic Depression. Journal of Neurophysiology 86(1): 536-540.

Image Databases

Occlusion Databases: OSET1, OSET2

Shadow Database: SHAD

If you use OSET1 please cite DiMattina et al. (2012). 

For use of SHAD or OSET2, please cite DiMattina et al. (2022).


To view more information about Dr. DiMattina, please visit his University Directory listing.