In this view, we address the problem of consciousness, and although we focus on its human presentation, we note that the phenomenon is present in numerous nonhuman species and use findings from a variety of animal studies to explain our hypothesis for how consciousness is made. Consciousness occurs when mind contents, such as perceptions and thoughts, are spontaneously identified as belonging to a specific organism/owner. Conscious minds are said to have a self that experiences mental events. We hypothesize that the automatic identification that associates minds and organisms is provided by a continuous flow of homeostatic feelings. Those feelings arise from the uninterrupted process of life regulation and correspond to both salient physiological fluctuations such as hunger, pain, well-being, or malaise, as well as to states closer to metabolic equilibrium and best described as feelings of life/existence, such as breathing or body temperature. We also hypothesize that homeostatic feelings were the inaugural phenomena of consciousness in biological evolution and venture that they were selected because the information they provided regarding the current state of life regulation conferred extraordinary advantages to the organisms so endowed. The "knowledge" carried by conscious homeostatic feelings provided "overt" guidance for life regulation, an advance over the covert regulation present in nonconscious organisms. Finally, we outline a mechanism for the generation of feelings based on a two-way interaction between interoceptive components of the nervous system and a particular set of nonneural components of the organism's interior, namely, viscera and circulating chemical molecules involved in their operations. Feelings emerge from this interaction as continuous and hybrid phenomena, related simultaneously to two series of events. The first is best described by the terms neural/representational/and mental and the second by the terms nonneural/visceral/and chemical. We note that this account offers a solution for the mind-body problem: homeostatic feelings constitute the "mental" version of bodily processes.

Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture industries. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquers this problem by changing the Riemannian metric on the target surface to a hyperbolic metric so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on Ricci flow and nonlinear heat diffusion methods. The approach is general and robust. We employ our algorithm to study the constrained surface registration problem which applies to both computer vision and medical imaging applications. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic and achieve relatively high performance when evaluated with some popular surface registration evaluation standards.

Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquer this problem by changing the Riemannian metric on the target surface to a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on the Ricci flow method and the method is general and robust. We apply our algorithm to study constrained human brain surface registration problem. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, and achieve relative high performance when evaluated with some popular cortical surface registration evaluation standards.

Manually labeled landmark sets are often required as inputs for landmark-based image registration. Identifying an optimal subset of landmarks from a training dataset may be useful in reducing the labor intensive task of manual labeling. In this paper, we present a new problem and a method to solve it: given a set of N landmarks, find the k(< N) best landmarks such that aligning these k landmarks that produce the best overall alignment of all N landmarks. The resulting procedure allows us to select a reduced number of landmarks to be labeled as a part of the registration procedure. We apply this methodology to the problem of registering cerebral cortical surfaces extracted from MRI data. We use manually traced sulcal curves as landmarks in performing inter-subject registration of these surfaces. To minimize the error metric, we analyze the correlation structure of the sulcal errors in the landmark points by modeling them as a multivariate Gaussian process. Selection of the optimal subset of sulcal curves is performed by computing the error variance for the subset of unconstrained landmarks conditioned on the constrained set. We show that the registration error predicted by our method closely matches the actual registration error. The method determines optimal curve subsets of any given size with minimal registration error.