57,902 research outputs found
Evaluation and Selection of Models for Motion Segmentation
We present a theoretically optimal linear algorithm for 3-D reconstruction from point correspondences over two views. We also present a similarly constructed optimal linear algorithm for 3-D reconstruction from optical flow. We then compare the performance of the two algorithms by simulation and real-image experiments using the same data. This is the first impartial comparison ever done in the sense that the two algorithms are both optimal, extracting the information contained in the data to a maximum possible degree. We observe that the finite motion solution is always superior to the optical flow solution and conclude
that the finite motion algorithm should be used for 3-D reconstruction
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.Comment: A preliminary version of the material in this document was filed as
University of Texas technical report no. UT AI16-0
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