57,695 research outputs found
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
Towards recovery of complex shapes in meshes using digital images for reverse engineering applications
When an object owns complex shapes, or when its outer surfaces are simply inaccessible, some of its parts may not be captured during its reverse engineering. These deficiencies in the point cloud result in a set of holes in the reconstructed mesh. This paper deals with the use of information extracted from digital images to recover missing areas of a physical object. The proposed algorithm fills in these holes by solving an optimization problem that combines two kinds of information: (1) the geometric information available on the surrounding of the holes, (2) the information contained in an image of the real object. The constraints come from the image irradiance equation, a first-order non-linear partial differential equation that links the position of the mesh vertices to the light intensity of the image pixels. The blending conditions are satisfied by using an objective function based on a mechanical model of bar network that simulates the curvature evolution over the mesh. The inherent shortcomings both to the current holefilling algorithms and the resolution of the image irradiance equations are overcom
Establishing the behavioural limits for countershaded camouflage
Countershading is a ubiquitous patterning of animals whereby the side that typically faces the highest illumination is darker. When tuned to specific lighting conditions and body orientation with respect to the light field, countershading minimizes the gradient of light the body reflects by counterbalancing shadowing due to illumination, and has therefore classically been thought of as an adaptation for visual camouflage. However, whether and how crypsis degrades when body orientation with respect to the light field is non-optimal has never been studied. We tested the behavioural limits on body orientation for countershading to deliver effective visual camouflage. We asked human participants to detect a countershaded target in a simulated three-dimensional environment. The target was optimally coloured for crypsis in a reference orientation and was displayed at different orientations. Search performance dramatically improved for deviations beyond 15 degrees. Detection time was significantly shorter and accuracy significantly higher than when the target orientation matched the countershading pattern. This work demonstrates the importance of maintaining body orientation appropriate for the displayed camouflage pattern, suggesting a possible selective pressure for animals to orient themselves appropriately to enhance crypsis
Coherent control using adaptive learning algorithms
We have constructed an automated learning apparatus to control quantum
systems. By directing intense shaped ultrafast laser pulses into a variety of
samples and using a measurement of the system as a feedback signal, we are able
to reshape the laser pulses to direct the system into a desired state. The
feedback signal is the input to an adaptive learning algorithm. This algorithm
programs a computer-controlled, acousto-optic modulator pulse shaper. The
learning algorithm generates new shaped laser pulses based on the success of
previous pulses in achieving a predetermined goal.Comment: 19 pages (including 14 figures), REVTeX 3.1, updated conten
Extraction of tidal channel networks from airborne scanning laser altimetry and aerial photography
The study of the morphodynamics of tidal channel networks is important because of their role in tidal propagation and the evolution of salt-marshes and tidal flats. Channel dimensions range from tens of metres wide and metres deep near the low water mark to only 20-30cm wide and 20cm deep for the smallest channels on the marshes. The conventional method of measuring the networks is cumbersome, involving manual digitising of aerial photographs. This paper describes a semi-automatic knowledge-based network extraction method that is being implemented to work using airborne scanning laser altimetry (and later aerial photography). The channels exhibit a width variation of several orders of magnitude, making an approach based on multi-scale line detection difficult. The processing therefore uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels using a distance-with-destination transform. Breaks in the networks are repaired by extending channel ends in the direction of their ends to join with nearby channels, using domain knowledge that flow paths should proceed downhill and that any network fragment should be joined to a nearby fragment so as to connect eventually to the open sea
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page:
http://www.ttic.edu/chakrabarti/consensus
Frontiers of Adaptive Design, Synthetic Biology and Growing Skins for Ephemeral Hybrid Structures
The history of membranes is one of adaptation, from the development in living organisms to man-made versions, with a great variety of uses in temporary design: clothing, building, packaging, etc. Being versatile and simple to integrate, membranes have a strong sustainability potential, through an essential use of material resources and multifunctional design, representing one of the purest cases where “design follows function.” The introduction of new engineered materials and techniques, combined with a growing interest for Nature-inspired technologies are progressively merging man-made artifacts and biological processes with a high potential for innovation. This chapter introduces, through a number of examples, the broad variety of hybrid membranes in the contest of experimental Design, Art and Architecture, categorized following two different stages of biology-inspired approach with the aim of identifying potential developments. Biomimicry, is founded on the adoption of practices from nature in architecture though imitation:
solutions are observed on a morphological, structural or procedural level and copied to design everything from nanoscale materials to building technologies.
Synthetic biology relies on hybrid procedures mixing natural and synthetic materials and processes
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