5,802 research outputs found
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
Multi-body Non-rigid Structure-from-Motion
Conventional structure-from-motion (SFM) research is primarily concerned with
the 3D reconstruction of a single, rigidly moving object seen by a static
camera, or a static and rigid scene observed by a moving camera --in both cases
there are only one relative rigid motion involved. Recent progress have
extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid}
relative motions in the scene), as well as {non-rigid SFM} (where there is a
single non-rigid, deformable object or scene). Along this line of thinking,
there is apparently a missing gap of "multi-body non-rigid SFM", in which the
task would be to jointly reconstruct and segment multiple 3D structures of the
multiple, non-rigid objects or deformable scenes from images. Such a multi-body
non-rigid scenario is common in reality (e.g. two persons shaking hands,
multi-person social event), and how to solve it represents a natural
{next-step} in SFM research. By leveraging recent results of subspace
clustering, this paper proposes, for the first time, an effective framework for
multi-body NRSFM, which simultaneously reconstructs and segments each 3D
trajectory into their respective low-dimensional subspace. Under our
formulation, 3D trajectories for each non-rigid structure can be well
approximated with a sparse affine combination of other 3D trajectories from the
same structure (self-expressiveness). We solve the resultant optimization with
the alternating direction method of multipliers (ADMM). We demonstrate the
efficacy of the proposed framework through extensive experiments on both
synthetic and real data sequences. Our method clearly outperforms other
alternative methods, such as first clustering the 2D feature tracks to groups
and then doing non-rigid reconstruction in each group or first conducting 3D
reconstruction by using single subspace assumption and then clustering the 3D
trajectories into groups.Comment: 21 pages, 16 figure
3D curves reconstruction from multiple images
In this paper, we propose a new approach for reconstructing 3D curves from a sequence of 2D images taken by uncalibrated cameras. A curve in 3D space is represented by a sequence of 3D points sampled along the curve, and the 3D points are reconstructed by minimizing the distances from their projections to the measured 2D curves on different images (i.e., 2D curve reprojection error). The minimization problem is solved by an iterative algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Without requiring calibrated cameras or additional point features, our method can reconstruct multiple 3D curves simultaneously from multiple images and it readily handles images with missing and/or partially occluded curves. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia, 1-3 December 2010. In Proceedings of DICTA, 2010, p. 462-46
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
We present an approach to matching images of objects in fine-grained datasets
without using part annotations, with an application to the challenging problem
of weakly supervised single-view reconstruction. This is in contrast to prior
works that require part annotations, since matching objects across class and
pose variations is challenging with appearance features alone. We overcome this
challenge through a novel deep learning architecture, WarpNet, that aligns an
object in one image with a different object in another. We exploit the
structure of the fine-grained dataset to create artificial data for training
this network in an unsupervised-discriminative learning approach. The output of
the network acts as a spatial prior that allows generalization at test time to
match real images across variations in appearance, viewpoint and articulation.
On the CUB-200-2011 dataset of bird categories, we improve the AP over an
appearance-only network by 13.6%. We further demonstrate that our WarpNet
matches, together with the structure of fine-grained datasets, allow
single-view reconstructions with quality comparable to using annotated point
correspondences.Comment: to appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Affine Subspace Representation for Feature Description
This paper proposes a novel Affine Subspace Representation (ASR) descriptor
to deal with affine distortions induced by viewpoint changes. Unlike the
traditional local descriptors such as SIFT, ASR inherently encodes local
information of multi-view patches, making it robust to affine distortions while
maintaining a high discriminative ability. To this end, PCA is used to
represent affine-warped patches as PCA-patch vectors for its compactness and
efficiency. Then according to the subspace assumption, which implies that the
PCA-patch vectors of various affine-warped patches of the same keypoint can be
represented by a low-dimensional linear subspace, the ASR descriptor is
obtained by using a simple subspace-to-point mapping. Such a linear subspace
representation could accurately capture the underlying information of a
keypoint (local structure) under multiple views without sacrificing its
distinctiveness. To accelerate the computation of ASR descriptor, a fast
approximate algorithm is proposed by moving the most computational part (ie,
warp patch under various affine transformations) to an offline training stage.
Experimental results show that ASR is not only better than the state-of-the-art
descriptors under various image transformations, but also performs well without
a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio
Component-wise modeling of articulated objects
We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework for 2D in-the-wild images that is applicable for robotics.
The fitting of the 3D Morphable Model is based exclusively on automatically
detected landmarks. After fitting, the face can be corrected in pose and
transformed back to a frontal 2D representation that is more suitable for face
recognition. We conduct face recognition experiments with non-frontal images
from the MUCT database and uncontrolled, in the wild images from the PaSC
database, the most challenging face recognition database to date, showing an
improved performance. Finally, we present our SCITOS G5 robot system, which
incorporates our framework as a means of image pre-processing for face
analysis
Stereo image processing system for robot vision
More and more applications (path planning, collision avoidance
methods) require 3D description of the surround world. This paper
describes a stereo vision system that uses 2D (grayscale or color) images
to extract simple 2D geometric entities (points, lines) applying a
low-level feature detector. The features are matched across views with a
graph matching algorithm. During the projective reconstruction the 3D
description of the scene is recovered. The developed system uses uncalibrated
cameras, therefore only projective 3D structure can be detected
defined up to a collineation. Using the Euclidean information about a
known set of predefined objects stored in database and the results of the
recognition algorithm, the description can be updated to a metric one
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