Schlecht, J., Barnard, K., Spriggs, E., & Pryor, B. (2007). Inferring grammar-based structure models from 3D microscopy data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Abstract:
We present a new method to fit grammar-based stochastic models for biological structure to stacks of microscopic images captured at incremental focal lengths. Providing the ability to quantitatively represent structure and automatically fit it to image data enables important biological research. We consider the case where individuals can be represented as an instance of a stochastic grammar, similar to L-systems used in graphics to produce realistic plant models. In particular, we construct a stochastic grammar of Alternaria, a genus of fungus, and fit instances of it to microscopic image stacks. We express the image data as the result of a generative process composed of the underlying probabilistic structure model together with the parameters of the imaging system. Fitting the model then becomes probabilistic inference. For this we create a reversible-jump MCMC sampler to traverse the parameter space. We observe that incorporating spatial structure helps fit the model parts, and that simultaneously fitting the imaging system is also very helpful. © 2007 IEEE.
Morris, S., Morris, A., & Barnard, K. (2004). Digital trail libraries. Proceedings of the ACM IEEE International Conference on Digital Libraries, JCDL 2004, 63-71.
Abstract:
We propose the idea of an online, user submitted digital library of recreation trails. Digital libraries of trails offer advantages over paper guidebooks in that they are more accurate, dynamic and not limited to the experience of the author(s). The basic representation of a trail is a GPS track log, recorded as recreators travel on trails. As users complete trips, the GPS track logs of their trips are submitted to the central library voluntarily. A major problem is that track logs will overlap and intersect each other. We present a method for the combination of overlapping and intersecting GPS track logs to create a network of GPS trails. Each trail segment in the network can then be characterized by automatic and manual means, producing a digital library of trails. We also describe the TopoFusion system which creates, manages and visualizes GPS data, including GPS networks.
Savage, R., Palafox, L. F., Morrison, C. T., Rodriguez, J. J., Barnard, K. J., Byrne, S., & Hamilton, C. W. (2018). A Bayesian Approach to Sub-Kilometer Crater Shape Analysis using Individual HiRISE Images. Transactions on Geoscience and Remote Sensing.
Funt, B., Cardei, V., & Barnard, K. (1997). Learning color constancy. Proceedings of the Color Imaging Conference: Color Science, Systems, and Applications, 58-60.
Abstract:
We decided to test a surprisingly simple hypothesis; namely, that the relationship between an image of a scene and the chromaticity of scene illumination could be learned by a neural network. The thought was that if this relationship could be extracted by a neural network, then the trained network would be able to determine a scene's Illuminant from its image, which would then allow correction of the image colors to those relative to a standard illuminance, thereby providing color constancy. Using a database of surface reflectances and illuminants, along with the spectral sensitivity functions of our camera, we generated thousands of images of randomly selected illuminants lighting `scenes' of 1 to 60 randomly selected reflectances. During the learning phase the network is provided the image data along with the chromaticity of its illuminant. After training, the network outputs (very quickly) the chromaticity of the illumination given only the image data. We obtained surprisingly good estimates of the ambient illumination lighting from the network even when applied to scenes in our lab that were completely unrelated to the training data.
Pero, L. D., Bowdish, J., Kermgard, B., Hartley, E., & Barnard, K. (2013). Understanding bayesian rooms using composite 3D object models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 153-160.
Abstract:
We develop a comprehensive Bayesian generative model for understanding indoor scenes. While it is common in this domain to approximate objects with 3D bounding boxes, we propose using strong representations with finer granularity. For example, we model a chair as a set of four legs, a seat and a backrest. We find that modeling detailed geometry improves recognition and reconstruction, and enables more refined use of appearance for scene understanding. We demonstrate this with a new likelihood function that rewards 3D object hypotheses whose 2D projection is more uniform in color distribution. Such a measure would be confused by background pixels if we used a bounding box to represent a concave object like a chair. Complex objects are modeled using a set or re-usable 3D parts, and we show that this representation captures much of the variation among object instances with relatively few parameters. We also designed specific data-driven inference mechanisms for each part that are shared by all objects containing that part, which helps make inference transparent to the modeler. Further, we show how to exploit contextual relationships to detect more objects, by, for example, proposing chairs around and underneath tables. We present results showing the benefits of each of these innovations. The performance of our approach often exceeds that of state-of-the-art methods on the two tasks of room layout estimation and object recognition, as evaluated on two bench mark data sets used in this domain. © 2013 IEEE.
