1,134 research outputs found
Image-based rendering and synthesis
Multiview imaging (MVI) is currently the focus of some research as it has a wide range of applications and opens up research in other topics and applications, including virtual view synthesis for three-dimensional (3D) television (3DTV) and entertainment. However, a large amount of storage is needed by multiview systems and are difficult to construct. The concept behind allowing 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction is image-based rendering (IBR). Using images as the primary substrate, IBR has many potential applications including for video games, virtual travel and others. The technique creates new views of scenes which are reconstructed from a collection of densely sampled images or videos. The IBR concept has different classification such as knowing 3D models and the lighting conditions and be rendered using conventional graphic techniques. Another is lightfield or lumigraph rendering which depends on dense sampling with no or very little geometry for rendering without recovering the exact 3D-models.published_or_final_versio
Data driven approaches for investigating molecular heterogeneity of the brain
It has been proposed that one of the clearest organizing principles for most sensory systems is the existence of parallel subcircuits and processing streams that form orderly and systematic mappings from stimulus space to neurons. Although the spatial heterogeneity of the early olfactory circuitry has long been recognized, we know comparatively little about the circuits that propagate sensory signals downstream. Investigating the potential modularity of the bulb’s intrinsic circuits proves to be a difficult task as termination patterns of converging projections, as with the bulb’s inputs, are not feasibly realized. Thus, if such circuit motifs exist, their detection essentially relies on identifying differential gene expression, or “molecular signatures,” that may demarcate functional subregions. With the arrival of comprehensive (whole genome, cellular resolution) datasets in biology and neuroscience, it is now possible for us to carry out large-scale investigations and make particular use of the densely catalogued, whole genome expression maps of the Allen Brain Atlas to carry out systematic investigations of the molecular topography of the olfactory bulb’s intrinsic circuits. To address the challenges associated with high-throughput and high-dimensional datasets, a deep learning approach will form the backbone of our informatic pipeline. In the proposed work, we test the hypothesis that the bulb’s intrinsic circuits are parceled into distinct, parallel modules that can be defined by genome-wide patterns of expression. In pursuit of this aim, our deep learning framework will facilitate the group-registration of the mitral cell layers of ~ 50,000 in-situ olfactory bulb circuits to test this hypothesis
Video Propagation Networks
We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17
A multi-camera approach to image-based rendering and 3-D/Multiview display of ancient chinese artifacts
published_or_final_versio
DC-Net: Divide-and-Conquer for Salient Object Detection
In this paper, we introduce Divide-and-Conquer into the salient object
detection (SOD) task to enable the model to learn prior knowledge that is for
predicting the saliency map. We design a novel network, Divide-and-Conquer
Network (DC-Net) which uses two encoders to solve different subtasks that are
conducive to predicting the final saliency map, here is to predict the edge
maps with width 4 and location maps of salient objects and then aggregate the
feature maps with different semantic information into the decoder to predict
the final saliency map. The decoder of DC-Net consists of our newly designed
two-level Residual nested-ASPP (ResASPP) modules, which have the ability
to capture a large number of different scale features with a small number of
convolution operations and have the advantages of maintaining high resolution
all the time and being able to obtain a large and compact effective receptive
field (ERF). Based on the advantage of Divide-and-Conquer's parallel computing,
we use Parallel Acceleration to speed up DC-Net, allowing it to achieve
competitive performance on six LR-SOD and five HR-SOD datasets under high
efficiency (60 FPS and 55 FPS). Codes and results are available:
https://github.com/PiggyJerry/DC-Net
Expanding the market of biomaterials
Biomaterials such as wood and bamboo are in high demand as a building material with the push for building with green technology. The wood product industry accounts for approximately 4% of the total U.S. manufacturing GDP (Gross Domestic Product), which is more than 60 billion annually. This research will explore the use of wood and bamboo in different end use products. The objectives of this research will 1) evaluate the behavior of two single bolt connections in the post-to-rail joint in a hardwood stairway system; 2) the potential of post-treating pre-fabricated cross-laminated timber (CLT) panels with two different copper based preservative treatments; and 3) estimated design values for a commercially sourced bolt laminated bamboo industrial mat. To accomplish these objectives, this dissertation is divided into five sections: 1) Introduction, 2) Structural performance of the post-to-rail connectors in a hardwood stairway handrail, 3) Development of preservative-treated cross-laminated timber: effects of panel layup and thickness on bonding performance and durability when treated with copper-azole (CA-C) and micronized copper-azole (MCA), 4) Strength and stiffness of 3-ply industrial bamboo matting, 5) Conclusion
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