8,165 research outputs found
3D Model Assisted Image Segmentation
The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for proces
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
Object segmentation from low depth of field images and video sequences
This thesis addresses the problem of autonomous object segmentation. To do so
the proposed segementation method uses some prior information, namely that the
image to be segmented will have a low depth of field and that the object of interest
will be more in focus than the background. To differentiate the object from the
background scene, a multiscale wavelet based assessment is proposed. The focus
assessment is used to generate a focus intensity map, and a sparse fields level set
implementation of active contours is used to segment the object of interest. The
initial contour is generated using a grid based technique.
The method is extended to segment low depth of field video sequences with
each successive initialisation for the active contours generated from the binary dilation
of the previous frame's segmentation. Experimental results show good segmentations
can be achieved with a variety of different images, video sequences, and
objects, with no user interaction or input.
The method is applied to two different areas. In the first the segmentations
are used to automatically generate trimaps for use with matting algorithms. In the
second, the method is used as part of a shape from silhouettes 3D object reconstruction
system, replacing the need for a constrained background when generating
silhouettes. In addition, not using a thresholding to perform the silhouette segmentation
allows for objects with dark components or areas to be segmented accurately.
Some examples of 3D models generated using silhouettes are shown
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