Jacobus J Barnard
Associate Director, Faculty Affairs-SISTA
Associate Professor, BIO5 Institute
Associate Professor, Electrical and Computer Engineering
Professor, Cognitive Science - GIDP
Professor, Computer Science
Professor, Genetics - GIDP
Professor, Statistics-GIDP
Primary Department
(520) 621-6613
Research Interest
Kobus Barnard, PhD, is an associate professor in the recently formed University of Arizona School of Information: Science, Technology, and Arts (SISTA), created to foster computational approaches across disciplines in both research and education. He also has University of Arizona appointments with Computer Science, ECE, Statistics, Cognitive Sciences, and BIO5. He leads the Interdisciplinary Visual Intelligence Lab (IVILAB) currently housed in SISTA. Research in the IVILAB revolves around building top-down statistical models that link theory and semantics to data. Such models support going from data to knowledge using Bayesian inference. Much of this work is in the context of inferring semantics and geometric form from image and video. For example, in collaboration with multiple researchers, the IVILAB has applied this approach to problems in computer vision (e.g., tracking people in 3D from video, understanding 3D scenes from images, and learning models of object structure) and biological image understanding (e.g., tracking pollen tubes growing in vitro, inferring the morphology of neurons grown in culture, extracting 3D structure of filamentous fungi from the genus Alternaria from brightfield microscopy image stacks, and extracting 3D structure of Arabidopsis plants). An additional IVILAB research project, Semantically Linked Instructional Content (SLIC) is on improving access to educational video through searching and browsing.Dr. Barnard holds an NSF CAREER grant, and has received support from three additional NSF grants, the DARPA Mind’s eye program, ONR, the Arizona Biomedical Research Commission (ABRC), and a BIO5 seed grant. He was supported by NSERC (Canada) during graduate and post-graduate studies (NSERC A, B and PDF). His work on computational color constancy was awarded the Governor General’s gold medal for the best dissertation across disciplines at SFU. He has published over 80 papers, including one awarded best paper on cognitive computer vision in 2002.


Reed, R. G., Barnard, K., & Butler, E. A. (2015). Distinguishing emotional coregulation from codysregulation: an investigation of emotional dynamics and body weight in romantic couples. Emotion (Washington, D.C.), 15(1), 45-60.

Well-regulated emotions, both within people and between relationship partners, play a key role in facilitating health and well-being. The present study examined 39 heterosexual couples' joint weight status (both partners are healthy-weight, both overweight, 1 healthy-weight, and 1 overweight) as a predictor of 2 interpersonal emotional patterns during a discussion of their shared lifestyle choices. The first pattern, coregulation, is one in which partners' coupled emotions show a dampening pattern over time and ultimately return to homeostatic levels. The second, codysregulation, is one in which partners' coupled emotions are amplified away from homeostatic balance. We demonstrate how a coupled linear oscillator (CLO) model (Butner, Amazeen, & Mulvey, 2005) can be used to distinguish coregulation from codysregulation. As predicted, healthy-weight couples and mixed-weight couples in which the man was heavier than the woman displayed coregulation, but overweight couples and mixed-weight couples in which the woman was heavier showed codysregulation. These results suggest that heterosexual couples in which the woman is overweight may face formidable coregulatory challenges that could undermine both partners' well-being. The results also demonstrate the importance of distinguishing between various interpersonal emotional dynamics for understanding connections between interpersonal emotions and health.

Barnard, K., Finlayson, G., & Funt, B. (1997). Color Constancy for Scenes with Varying Illumination. Computer Vision and Image Understanding, 65(2), 311-321.


We present an algorithm which uses information from both surface reflectance and illumination variation to solve for color constancy. Most color constancy algorithms assume that the illumination across a scene is constant, but this is very often not valid for real images. The method presented in this work identifies and removes the illumination variation, and in addition uses the variation to constrain the solution. The constraint is applied conjunctively to constraints found from surface reflectances. Thus the algorithm can provide good color constancy when there is sufficient variation in surface reflectances, or sufficient illumination variation, or a combination of both. We present the results of running the algorithm on several real scenes, and the results are very encouraging. © 1997 Academic Press.

Finlayson, G. D., Funt, B. V., & Barnard, K. (1995). Color constancy under varying illumination. IEEE International Conference on Computer Vision, 720-725.


Illumination is rarely constant in intensity or color throughout a scene. Multiple light sources with different spectra - sun and sky, direct and interreflected light - are the norm. Nonetheless, almost all color constancy algorithms assume that the spectrum of the incident illumination remains constant across the scene. We assume the converse, that illumination does vary, in developing a new algorithm for color constancy. Rather than creating difficulties, varying illumination is in fact a very powerful constraint. Indeed tests of our algorithm using real images of an office scene show excellent results.

Schlecht, J., Kaplan, M. E., Barnard, K., Karafet, T., Hammer, M. F., & Merchant, N. C. (2008). Machine-learning approaches for classifying haplogroup from Y chromosome STR data. PLoS Computational Biology, 4(6).

PMID: 18551166;PMCID: PMC2396484;Abstract:

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner. © 2008 Schlecht et al.

Brau, E., Barnard, J. J., Palanivelu, R. -., Dunatunga, D., Tsukamoto, T., & Lee, P. (2011). A generative statistical model for tracking multiple smooth trajectories of pollen tubes. Proceedings of the IEEE Computer Vision and Pattern Recognition, 1137-1144.

doi: 10.1109/CVPR.2011.5995736