1,106 research outputs found
Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces
The tolerancing process links the virtual and the real worlds. From the
former, tolerances define a variational geometrical language (geometric
parameters). From the latter, there are values limiting those parameters. The
beginning of a tolerancing process is in this duality. As high precision
assemblies cannot be analyzed with the assumption that form errors are
negligible, we propose to apply this process to assemblies with form errors
through a new way of allowing to parameterize forms and solve their assemblies.
The assembly process is calculated through a method of allowing to solve the 3D
assemblies of pairs of surfaces having form errors using a static equilibrium.
We have built a geometrical model based on the modal shapes of the ideal
surface. We compute for the completely deterministic contact points between
this pair of shapes according to a given assembly process. The solution gives
an accurate evaluation of the assembly performance. Then we compare the results
with or without taking into account the form errors. When we analyze a batch of
assemblies, the problem is to compute for the nonconformity rate of a pilot
production according to the functional requirements. We input probable errors
of surfaces (position, orientation, and form) in our calculus and we evaluate
the quality of the results compared with the functional requirements. The pilot
production then can or cannot be validated
Laplacian-Steered Neural Style Transfer
Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to
synthesize a new image that retains the high-level structure of a content
image, rendered in the low-level texture of a style image. This is achieved by
constraining the new image to have high-level CNN features similar to the
content image, and lower-level CNN features similar to the style image. However
in the traditional optimization objective, low-level features of the content
image are absent, and the low-level features of the style image dominate the
low-level detail structures of the new image. Hence in the synthesized image,
many details of the content image are lost, and a lot of inconsistent and
unpleasing artifacts appear. As a remedy, we propose to steer image synthesis
with a novel loss function: the Laplacian loss. The Laplacian matrix
("Laplacian" in short), produced by a Laplacian operator, is widely used in
computer vision to detect edges and contours. The Laplacian loss measures the
difference of the Laplacians, and correspondingly the difference of the detail
structures, between the content image and a new image. It is flexible and
compatible with the traditional style transfer constraints. By incorporating
the Laplacian loss, we obtain a new optimization objective for neural style
transfer named Lapstyle. Minimizing this objective will produce a stylized
image that better preserves the detail structures of the content image and
eliminates the artifacts. Experiments show that Lapstyle produces more
appealing stylized images with less artifacts, without compromising their
"stylishness".Comment: Accepted by the ACM Multimedia Conference (MM) 2017. 9 pages, 65
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Exploring Defocus Matting: Nonparametric Acceleration, Super-Resolution, and Off-Center Matting
Defocus matting is a fully automatic and passive method for pulling mattes from video captured with coaxial cameras that have different depths of field and planes of focus. Nonparametric sampling can accelerate the video-matting process from minutes to seconds per frame. In addition, a super-resolution technique efficiently bridges the gap between mattes from high-resolution video cameras and those from low-resolution cameras. Off-center matting pulls mattes for an external high-resolution camera that doesn't share the same center of projection as the low-resolution cameras used to capture the defocus matting data.Engineering and Applied Science
Object Discovery via Cohesion Measurement
Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However, image pixels, which share a similar real-world color, may
be quite different since colors are often distorted by intensity. In this
paper, we reinvestigate the affinity matrices originally used in image
segmentation methods based on spectral clustering. A new affinity matrix, which
is robust to color distortions, is formulated for object discovery. Moreover, a
Cohesion Measurement (CM) for object regions is also derived based on the
formulated affinity matrix. Based on the new Cohesion Measurement, a novel
object discovery method is proposed to discover objects latent in an image by
utilizing the eigenvectors of the affinity matrix. Then we apply the proposed
method to both saliency detection and object proposal generation. Experimental
results on several evaluation benchmarks demonstrate that the proposed CM based
method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure
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