28,867 research outputs found
Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance
Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016
Real-Time Cloth Simulation on Virtual Human Character Using Enhanced Position Based Dynamic Framework Technique
كانت محاكاة القماش والرسوم المتحركة موضوعًا للبحث منذ منتصف الثمانينيات في مجال رسومات الكمبيوتر. إن فرض عدم الضغط مهم جدًا في محاكاة الوقت الفعلي. على الرغم من أن هناك إنجازات كبيرة في هذا الصدد ، إلا أنها لا تزال تعاني من استهلاك الوقت غير الضروري في خطوات معينة شائعة في التطبيقات في الوقت الفعلي. يطور هذا البحث محاكاة قماش في الوقت الفعلي لشخصية بشرية افتراضية مرتدية ملابس. وقد حققت هذه المخطوطة الاهداف في محاكاة القماش على الشخصية الافتراضة من خلال تعزيز إطار الديناميكيات القائمة على الموقع من خلال حساب سلسلة من القيود الموضعية التي تنفذ كثافات ثابتة. أيضا ، يتم تنفيذ التصادم الذاتي والاصطدام مع الكبسولات المتحركة لتحقيق قماش سلوك واقعي على غرار الرسوم المتحركة. وذلك لتمكين عدم قابلية المقارنة والالتقاء مقارنة بمذيبات دالة تشوه جيب التمام عند التنفيذ ، نحقق تصادمًا محسنًا بين الملابس ، ومزامنة الرسوم المتحركة مع محاكاة القماش وتحديد خصائص القماش للحصول على أفضل النتائج الممكنة. لذلك ، تم تحقيق محاكاة القماش في الوقت الحقيقي ، مع إخراج معقول ، على الشخصية الافتراضية المتحركة. ندرك أن طريقتنا المقترحة يمكن أن تكون بمثابة استكمال للبحوث السابقة في حقل ملابس الشخصيات الافتراضية. Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications. This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision with moving capsules is implemented to achieve realistic behavior cloth modelled on animated characters. This is to enable comparable incompressibility and convergence to raised cosine deformation (RCD) function solvers. On implementation, this research achieves optimized collision between clothes, syncing of the animation with the cloth simulation and setting the properties of the cloth to get the best results possible. Therefore, a real-time cloth simulation, with believable output, on animated VHC is achieved. This research perceives our proposed method can serve as a completion to the game assets clothing pipeline
A multi-level preconditioned Krylov method for the efficient solution of algebraic tomographic reconstruction problems
Classical iterative methods for tomographic reconstruction include the class
of Algebraic Reconstruction Techniques (ART). Convergence of these stationary
linear iterative methods is however notably slow. In this paper we propose the
use of Krylov solvers for tomographic linear inversion problems. These advanced
iterative methods feature fast convergence at the expense of a higher
computational cost per iteration, causing them to be generally uncompetitive
without the inclusion of a suitable preconditioner. Combining elements from
standard multigrid (MG) solvers and the theory of wavelets, a novel
wavelet-based multi-level (WMG) preconditioner is introduced, which is shown to
significantly speed-up Krylov convergence. The performance of the
WMG-preconditioned Krylov method is analyzed through a spectral analysis, and
the approach is compared to existing methods like the classical Simultaneous
Iterative Reconstruction Technique (SIRT) and unpreconditioned Krylov methods
on a 2D tomographic benchmark problem. Numerical experiments are promising,
showing the method to be competitive with the classical Algebraic
Reconstruction Techniques in terms of convergence speed and overall performance
(CPU time) as well as precision of the reconstruction.Comment: Journal of Computational and Applied Mathematics (2014), 26 pages, 13
figures, 3 table
Fiber Bundles, Yang-Mills Theory, and General Relativity
I articulate and discuss a geometrical interpretation of Yang-Mills theory.
Analogies and disanalogies between Yang-Mills theory and general relativity are
also considered.Comment: 54 page
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