Jacobus J Barnard

Jacobus J Barnard

Professor, Computer Science
Associate Director, Faculty Affairs-SISTA
Associate Professor, Electrical and Computer Engineering
Member of the Graduate Faculty
Professor, Cognitive Science - GIDP
Professor, Genetics - GIDP
Professor, Statistics-GIDP
Associate Professor, BIO5 Institute
Primary Department
(520) 621-4632

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.


Barnard, K., & Finlayson, G. (2000). Shadow identification using colour ratios. Final Program and Proceedings - IS and T/SID Color Imaging Conference, 97-101.


In this paper we present a comprehensive method for identifying probable shadow regions in an image. Doing so is relevant to computer vision, colour constancy, and image reproduction, specifically dynamic range compression. Our method begins with a segmentation of the image into regions of the same colour. Then the edges between the regions are analyzed with respect to the possibility that each is due to an illumination change as opposed to a material boundary. We then integrate the edge information to produce an estimate of the illumination field.

Barnard, K., & Fan, Q. (2007). Reducing correspondence ambiguity in loosely labeled training data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.


We develop an approach to reduce correspondence ambiguity in training data where data items are associated with sets of plausible labels. Our domain is images annotated with keywords where it is not known which part of the image a keyword refers to. In contrast to earlier approaches that build predictive models or classifiers despite the ambiguity, we argue that that it is better to first address the correspondence ambiguity, and then build more complex models from the improved training data. This addresses difficulties of fitting complex models in the face of ambiguity while exploiting all the constraints available from the training data. We contribute a simple and flexible formulation of the problem, and show results validated by a recently developed comprehensive evaluation data set and corresponding evaluation methodology. © 2007 IEEE.

Barnard, K., & Gabbur, P. (2003). Color and Color Constancy in a Translation Model for Object Recognition. Final Program and Proceedings - IS and T/SID Color Imaging Conference, 364-369.


Color is of interest to those working in computer vision largely because it is assumed to be helpful for recognition. This assumption has driven much work in color based image indexing, and computational color constancy. However, in many ways, indexing is a poor model for recognition. In this paper we use a recently developed statistical model of recognition which learns to link image region features with words, based on a large unstructured data set. The system is general in that it learns what is recognizable given the data. It also supports a principled testing paradigm which we exploit here to evaluate the use of color. In particular, we look at color space choice, degradation due to illumination change, and dealing with this degradation. We evaluate two general approaches to dealing with this color constancy problem. Specifically we address whether it is better to build color variation due to illumination into a recognition system, or, instead, apply color constancy preprocessing to images before they are processed by the recognition system.

Barnard, K., Fan, Q., Swaminathan, R., Hoogs, A., Collins, R., Rondot, P., & Kaufhold, J. (2008). Evaluation of localized semantics: Data, methodology, and experiments. International Journal of Computer Vision, 77(1-3), 199-217.


We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416-421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235-244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior. © 2007 Springer Science+Business Media, LLC.

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.