Probabilistic models of early vision

How do our brains transform patterns of light striking the retina into useful knowledge about objects and events of the external world? Thanks to intense research into the mechanisms of vision, much is now known about this process. However, we do not yet have anything close to a complete picture, and many questions remain unanswered. In addition to its clinical relevance and purely academic significance, research on vision is important because a thorough understanding of biological vision would probably help solve many major problems in computer vision. A major framework for investigating the computational basis of vision is what might be called the probabilistic view of vision. This approach emphasizes the general importance of uncertainty and probabilities in perception and, in particular, suggests that perception is tightly linked to the statistical structure of the natural environment. This thesis investigates this link by building statistical models of natural images, and relating these to what is known of the information processing performed by the early stages of the primate visual system. Recently, it was suggested that the response properties of simple cells in the primary visual cortex could be interpreted as the result of the cells performing an independent component analysis of the natural visual sensory input. This thesis provides some further support for that proposal, and, more importantly, extends the theory to also account for complex cell properties and the columnar organization of the primary visual cortex. Finally, the application of these methods to predicting neural response properties further along the visual pathway is considered. Although the models considered account for only a relatively small part of known facts concerning early visual information processing, it is nonetheless a rather impressive amount considering the simplicity of the models. This is encouraging, and suggests that many of the intricacies of visual information processing might be understood using fairly simple probabilistic models of natural sensory input.reviewe

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable, we would like our models to quantify their uncertainty in order to achieve robustness against images of varying quality. Probabilistic deep learning models combine the expressive power of deep learning with uncertainty quantification. In this paper, we propose a novel probabilistic deep learning model for the task of angular regression. Our model uses von Mises distributions to predict a distribution over object pose angle. Whereas a single von Mises distribution is making strong assumptions about the shape of the distribution, we extend the basic model to predict a mixture of von Mises distributions. We show how to learn a mixture model using a finite and infinite number of mixture components. Our model allows for likelihood-based training and efficient inference at test time. We demonstrate on a number of challenging pose estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the current state-of-the-art

Straight or curved? From deterministic to probabilistic models of 3D motion perception

A commentary on Detection of 3D curved trajectories: the role of binocular disparity by Pierce, R. S., Bian, Z., Braunstein, M. L., and Andersen, G. J. (2013). Front. Behav. Neurosci. 7:12. doi: 10.3389/fnbeh.2013.0001

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Shape-Based Models for Interactive Segmentation of Medical Images

Accurate image segmentation is one of the key problems in computer vision. In domains such as radiation treatment planning, dosimetrists must manually trace the outlines of a few critical structures on large numbers of images. Considerable similarity can be seen in the shape of these regions, both between adjacent slices in a particular patient and across the spectrum of patients. Consequently we should be able to model this similarity and use it to assist in the process of segmentation. Previous work has demonstrated that a constraint-based 2D radial model can capture generic shape information for certain shape classes, and can reduce user interaction by a factor of three over purely manual segmentation. Additional simulation studies have shown that a probabilistic version of the model has the potential to further reduce user interaction. This paper describes an implementation of both models in a general-purpose imaging and graphics framework and compares the usefulness of the models on several shape classes