62,369 research outputs found
Kernel Spectral Curvature Clustering (KSCC)
Multi-manifold modeling is increasingly used in segmentation and data
representation tasks in computer vision and related fields. While the general
problem, modeling data by mixtures of manifolds, is very challenging, several
approaches exist for modeling data by mixtures of affine subspaces (which is
often referred to as hybrid linear modeling). We translate some important
instances of multi-manifold modeling to hybrid linear modeling in embedded
spaces, without explicitly performing the embedding but applying the kernel
trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses
kernels at two levels - both as an implicit embedding method to linearize
nonflat manifolds and as a principled method to convert a multiway affinity
problem into a spectral clustering one. We demonstrate the effectiveness of the
method by comparing it with other state-of-the-art methods on both synthetic
data and a real-world problem of segmenting multiple motions from two
perspective camera views.Comment: accepted to 2009 ICCV Workshop on Dynamical Visio
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse
correspondences between keypoints in testing and training images. Keypoints
represent automatically identified distinctive image locations, where each
keypoint correspondence suggests a transformation between images. We use these
correspondences to transfer label maps of entire organs from the training
images to the test image. The keypoint transfer algorithm includes three steps:
(i) keypoint matching, (ii) voting-based keypoint labeling, and (iii)
keypoint-based probabilistic transfer of organ segmentations. We report
segmentation results for abdominal organs in whole-body CT and MRI, as well as
in contrast-enhanced CT and MRI. Our method offers a speed-up of about three
orders of magnitude in comparison to common multi-atlas segmentation, while
achieving an accuracy that compares favorably. Moreover, keypoint transfer does
not require the registration to an atlas or a training phase. Finally, the
method allows for the segmentation of scans with highly variable field-of-view.Comment: Accepted for publication at IEEE Transactions on Medical Imagin
The Whole World in Your Hand: Active and Interactive Segmentation
Object segmentation is a fundamental problem
in computer vision and a powerful resource for
development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided
by the presence of a hand or arm in the proximity of the object to be segmented. The first
approach is suitable for a robotic system, where
the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's
perspective, with instrumentation to help detect
and segment objects that are held in the wearer's
hand. The third method operates when observing
a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it
as a seed for segmentation. We show that object segmentation can serve as a key resource for
development by demonstrating methods that exploit high-quality object segmentations to develop
both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities
(object recognition and localization)
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