206 research outputs found
Image Segmentation with Eigenfunctions of an Anisotropic Diffusion Operator
We propose the eigenvalue problem of an anisotropic diffusion operator for
image segmentation. The diffusion matrix is defined based on the input image.
The eigenfunctions and the projection of the input image in some eigenspace
capture key features of the input image. An important property of the model is
that for many input images, the first few eigenfunctions are close to being
piecewise constant, which makes them useful as the basis for a variety of
applications such as image segmentation and edge detection. The eigenvalue
problem is shown to be related to the algebraic eigenvalue problems resulting
from several commonly used discrete spectral clustering models. The relation
provides a better understanding and helps developing more efficient numerical
implementation and rigorous numerical analysis for discrete spectral
segmentation methods. The new continuous model is also different from
energy-minimization methods such as geodesic active contour in that no initial
guess is required for in the current model. The multi-scale feature is a
natural consequence of the anisotropic diffusion operator so there is no need
to solve the eigenvalue problem at multiple levels. A numerical implementation
based on a finite element method with an anisotropic mesh adaptation strategy
is presented. It is shown that the numerical scheme gives much more accurate
results on eigenfunctions than uniform meshes. Several interesting features of
the model are examined in numerical examples and possible applications are
discussed
Self adaptive global-local feature enhancement for radiology report generation
Automated radiology report generation aims at automatically generating a
detailed description of medical images, which can greatly alleviate the
workload of radiologists and provide better medical services to remote areas.
Most existing works pay attention to the holistic impression of medical images,
failing to utilize important anatomy information. However, in actual clinical
practice, radiologists usually locate important anatomical structures, and then
look for signs of abnormalities in certain structures and reason the underlying
disease. In this paper, we propose a novel framework AGFNet to dynamically fuse
the global and anatomy region feature to generate multi-grained radiology
report. Firstly, we extract important anatomy region features and global
features of input Chest X-ray (CXR). Then, with the region features and the
global features as input, our proposed self-adaptive fusion gate module could
dynamically fuse multi-granularity information. Finally, the captioning
generator generates the radiology reports through multi-granularity features.
Experiment results illustrate that our model achieved the state-of-the-art
performance on two benchmark datasets including the IU X-Ray and MIMIC-CXR.
Further analyses also prove that our model is able to leverage the
multi-grained information from radiology images and texts so as to help
generate more accurate reports
External Periodic Force Control of a Single-Degree-of-Freedom Vibroimpact System
A single-degree-of-freedom mechanical model of vibro-impact system is established. Bifurcation and chaos in the system are revealed with the time history diagram, phase trajectory map, and Poincaré map. According to the bifurcation and chaos of the actual vibro-impact system, the paper puts forward external periodic force control strategy. The method of controlling chaos by external periodic force feedback controller is developed to guide chaotic motions towards regular motions. The stability of the control system is also analyzed especially by theory. By selecting appropriate feedback coefficients, the unstable periodic orbits of the original chaotic orbit can be stabilized to the stable periodic orbits. The effectiveness of this control method is verified by numerical simulation
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