190 research outputs found
Real time hand gesture recognition including hand segmentation and tracking
In this paper we present a system that performs automatic gesture recognition. The system consists of two main components: (i) A unified technique for segmentation and tracking of face and hands using a skin detection algorithm along with handling occlusion between skin objects to keep track of the status of the occluded parts. This is realized by combining 3 useful features, namely, color, motion and position. (ii) A static and dynamic gesture recognition system. Static gesture recognition is achieved using a robust hand shape classification, based on PCA subspaces, that is invariant to scale along with small translation and rotation transformations. Combining hand shape classification with position information and using DHMMs allows us to accomplish dynamic gesture recognition
Power spectra of the natural input to the visual system
AbstractThe efficient coding hypothesis posits that sensory systems are adapted to the regularities of their signal input so as to reduce redundancy in the resulting representations. It is therefore important to characterize the regularities of natural signals to gain insight into the processing of natural stimuli. While measurements of statistical regularity in vision have focused on photographic images of natural environments it has been much less investigated, how the specific imaging process embodied by the organismâs eye induces statistical dependencies on the natural input to the visual system. This has allowed using the convenient assumption that natural image data are homogeneous across the visual field. Here we give up on this assumption and show how the imaging process in a human model eye influences the local statistics of the natural input to the visual system across the entire visual field. Artificial scenes with three-dimensional edge elements were generated and the influence of the imaging projection onto the back of a spherical model eye were quantified. These distributions show a strong radial influence of the imaging process on the resulting edge statistics with increasing eccentricity from the model fovea. This influence is further quantified through computation of the second order intensity statistics as a function of eccentricity from the center of projection using samples from the dead leaves image model. Using data from a naturalistic virtual environment, which allows generation of correctly projected images onto the model eye across the entire field of view, we quantified the second order dependencies as function of the position in the visual field using a new generalized parameterization of the power spectra. Finally, we compared this analysis with a commonly used natural image database, the van Hateren database, and show good agreement within the small field of view available in these photographic images. We conclude by providing a detailed quantitative analysis of the second order statistical dependencies of the natural input to the visual system across the visual field and demonstrating the importance of considering the influence of the sensory system on the statistical regularities of the input to the visual system
Global and local statistical regularities control visual attention to object sequences
Many previous studies have shown that both infants and adults are skilled statistical learners. Because statistical learning is affected by attention, learners' ability to manage their attention can play a large role in what they learn. However, it is still unclear how learners allocate their attention in order to gain information in a visual environment containing multiple objects, especially how prior visual experience (i.e., familiarly of objects) influences where people look. To answer these questions, we collected eye movement data from adults exploring multiple novel objects while manipulating object familiarity with global (frequencies) and local (repetitions) regularities. We found that participants are sensitive to both global and local statistics embedded in their visual environment and they dynamically shift their attention to prioritize some objects over others as they gain knowledge of the objects and their distributions within the task
Neuropsychological constraints to human data production on a global scale
Which are the factors underlying human information production on a global
level? In order to gain an insight into this question we study a corpus of
252-633 Million publicly available data files on the Internet corresponding to
an overall storage volume of 284-675 Terabytes. Analyzing the file size
distribution for several distinct data types we find indications that the
neuropsychological capacity of the human brain to process and record
information may constitute the dominant limiting factor for the overall growth
of globally stored information, with real-world economic constraints having
only a negligible influence. This supposition draws support from the
observation that the files size distributions follow a power law for data
without a time component, like images, and a log-normal distribution for
multimedia files, for which time is a defining qualia.Comment: to be published in: European Physical Journal
Attractor Metadynamics in Adapting Neural Networks
Slow adaption processes, like synaptic and intrinsic plasticity, abound in
the brain and shape the landscape for the neural dynamics occurring on
substantially faster timescales. At any given time the network is characterized
by a set of internal parameters, which are adapting continuously, albeit
slowly. This set of parameters defines the number and the location of the
respective adiabatic attractors. The slow evolution of network parameters hence
induces an evolving attractor landscape, a process which we term attractor
metadynamics. We study the nature of the metadynamics of the attractor
landscape for several continuous-time autonomous model networks. We find both
first- and second-order changes in the location of adiabatic attractors and
argue that the study of the continuously evolving attractor landscape
constitutes a powerful tool for understanding the overall development of the
neural dynamics
Partial core power transformer
This thesis describes the design, construction, and testing of a 15kVA, 11kV/230V partial
core power transformer (PCPT) for continuous operation. While applications for the partial
core transformer have been developed for many years, the concept of constructing a partial
core transformer, from conventional copper windings, as a power transformer has not been
investigated, specifically to have a continuous operation. In this thesis, this concept has been
investigated and tested.
The first part of the research involved creating a computer program to model the physical
dimensions and the electrical performance of a partial core transformer, based on the existing
partial core transformer models.
Also, since the hot-spot temperature is the key factor for limiting the power rating of the
PCPT, the second part of the research investigates a thermal model to simulate the change of
the hot-spot temperature for the designed PCPT. The cooling fluid of the PCPT applied in
this project was BIOTEMPÂź. The original thermal model used was from the IEEE Guide for
Loading Mineral-Oil-Immersed transformer. However, some changes to the original thermal
model had to be made since the original model does not include BIOTEMPÂź as a type of
cooling fluid. The constructed partial core transformer was tested to determine its hot-spot
temperature when it is immersed by BIOTEMPÂź, and the results compared with the thermal
model.
The third part of the research involved using both the electrical model and the thermal model
to design a PCPT. The PCPT was tested to obtain the actual electrical and the thermal
performance for the PCPT.
The overall performance of the PCPT was very close to the model estimation. However,
cooling of the PCPT was not sufficient to allow the PCPT to operate at the design rated load
for continuous operation. Therefore, the PCPT was down rated from 15kVA to maintain the
hot-spot temperature at 100°C for continuous operation. The actual rating of the PCPT is 80%
of the original power rating, which is 12kVA
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What can mathematical, computational and robotic models tell us about the origins of syntax?
Spike avalanches in vivo suggest a driven, slightly subcritical brain state
In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mĂ©lange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1005B, Bernstein Zentrum fĂŒr Computational Neuroscience, Göttingen - Kooperative Dynamiken und AdaptivitĂ€t in neuronalen SystemenBMBF, 01GQ0742, Verbundprojekt Bernstein Partner: GedĂ€chtnis-Netzwerk, Teilprojekt
Sparseness, Antisparseness and Anything in Between: The Operating Point of a Neuron Determines Its Computational Repertoire
Task-specific modulation of memory for object features in natural scenes
The influence of visual tasks on short and long-term memory for visual features
was investigated using a change-detection paradigm. Subjects completed 2 tasks:
(a) describing objects in natural images, reporting a specific property of each
object when a crosshair appeared above it, and (b) viewing a modified version of
each scene, and detecting which of the previously described objects had changed.
When tested over short delays (seconds), no task effects were found. Over longer
delays (minutes) we found the describing task influenced what types of changes
were detected in a variety of explicit and incidental memory experiments.
Furthermore, we found surprisingly high performance in the incidental memory
experiment, suggesting that simple tasks are sufficient to instill long-lasting
visual memories
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