Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Physically Interacting With Four Dimensions

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2009People have long been fascinated with understanding the fourth dimension. While making pictures of 4D objects by projecting them to 3D can help reveal basic geometric features, 3D graphics images by themselves are of limited value. For example, just as 2D shadows of 3D curves may have lines crossing one another in the shadow, 3D graphics projections of smooth 4D topological surfaces can be interrupted where one surface intersects another. The research presented here creates physically realistic models for simple interactions with objects and materials in a virtual 4D world. We provide methods for the construction, multimodal exploration, and interactive manipulation of a wide variety of 4D objects. One basic achievement of this research is to exploit the free motion of a computer-based haptic probe to support a continuous motion that follows the \emph{local continuity\/} of a 4D surface, allowing collision-free exploration in the 3D projection. In 3D, this interactive probe follows the full local continuity of the surface as though we were in fact \emph{physically touching\/} the actual static 4D object. Our next contribution is to support dynamic 4D objects that can move, deform, and collide with other objects as well as with themselves. By combining graphics, haptics, and collision-sensing physical modeling, we can thus enhance our 4D visualization experience. Since we cannot actually place interaction devices in 4D, we develop fluid methods for interacting with a 4D object in its 3D shadow image using adapted reduced-dimension 3D tools for manipulating objects embedded in 4D. By physically modeling the correct properties of 4D surfaces, their bending forces, and their collisions in the 3D interactive or haptic controller interface, we can support full-featured physical exploration of 4D mathematical objects in a manner that is otherwise far beyond the real-world experience accessible to human beings

Human pose estimation from video and inertial sensors

[no abstract

Modelling the Emergence and Dynamics of Perceptual Organisation in Auditory Streaming

Many sound sources can only be recognised from the pattern of sounds they emit, and not from the individual sound events that make up their emission sequences. Auditory scene analysis addresses the difficult task of interpreting the sound world in terms of an unknown number of discrete sound sources (causes) with possibly overlapping signals, and therefore of associating each event with the appropriate source. There are potentially many different ways in which incoming events can be assigned to different causes, which means that the auditory system has to choose between them. This problem has been studied for many years using the auditory streaming paradigm, and recently it has become apparent that instead of making one fixed perceptual decision, given sufficient time, auditory perception switches back and forth between the alternatives—a phenomenon known as perceptual bi- or multi-stability. We propose a new model of auditory scene analysis at the core of which is a process that seeks to discover predictable patterns in the ongoing sound sequence. Representations of predictable fragments are created on the fly, and are maintained, strengthened or weakened on the basis of their predictive success, and conflict with other representations. Auditory perceptual organisation emerges spontaneously from the nature of the competition between these representations. We present detailed comparisons between the model simulations and data from an auditory streaming experiment, and show that the model accounts for many important findings, including: the emergence of, and switching between, alternative organisations; the influence of stimulus parameters on perceptual dominance, switching rate and perceptual phase durations; and the build-up of auditory streaming. The principal contribution of the model is to show that a two-stage process of pattern discovery and competition between incompatible patterns can account for both the contents (perceptual organisations) and the dynamics of human perception in auditory streaming

New Directions for Cu2+ Labeling of Biomolecules to Determine Structure, Conformation, and Flexibility

In this thesis, we showcase several key applications of Cu2+ labeling in proteins and nucleic acids using electron paramagnetic resonance (EPR) spectroscopy. In proteins, the double histidine (dHis) motif is employed to coordinate a Cu2+ complex. With EPR, it is possible to measure nanoscale distances between two such sites. The dHis method produces distance measurements with a very narrow probability distribution, enabling precise structural assessment. First, we provide optimal conditions to label a dHis motif with a Cu2+ complex in proteins, and a comprehensive overview of the factors that impact labeling efficiency. We show that dHis labeling is sensitive to the buffer used, and under optimal conditions, up to 80% of proteins can be doubly labeled – a substantial improvement over previous implementations. We demonstrate the power of the dHis method in the precise location of a native metal binding site within a protein. We show that the narrow distance distributions enable a high precision localization, even with very few measured constraints. Such principles can be extrapolated to protein-protein docking, quaternary structural assembly, substrate binding, and protein-DNA interactions. Additionally, we show that the dHis motif is uniquely suited to determine protein subunit orientations by performing distance measurements at high frequencies. Such orientational information is extracted from the Cu2+ center, and we show that the Cu2+ orientation is correlated to the protein subunit on which the dHis motif is applied. Finally, we take the characteristics behind the success of the dHis motif and apply them to peptide nucleic acids (PNA) to determine precise distance constraints between two site-specific Cu2+ labels. Combined with molecular dynamics simulations, we gain an atomistic insight into the structure and dynamics of the PNA duplex. Such work presents an efficient, precise method that provides detailed structural information regarding the nucleic acid, and may be applied to other systems such as DNA or RNA in the future. Overall, this body of work marks a significant advancement of Cu2+ labeling to determine relevant structural information in proteins and nucleic acids, while presenting clear methodologies to promote the widespread adoption of this technique within the scientific community

Spatiotemporal dynamics of continuum neural fields

We survey recent analytical approaches to studying the spatiotemporal dynamics of continuum neural fields. Neural fields model the large-scale dynamics of spatially structured biological neural networks in terms of nonlinear integrodifferential equations whose associated integral kernels represent the spatial distribution of neuronal synaptic connections. They provide an important example of spatially extended excitable systems with nonlocal interactions and exhibit a wide range of spatially coherent dynamics including traveling waves oscillations and Turing-like patterns