4,880 research outputs found
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Thermal conductivity of monolayer MoS2, MoSe2, and WS2: Interplay of mass effect, interatomic bonding and anharmonicity
Phonons are essential for understanding the thermal properties in monolayer
transition metal dichalcogenides, which limit their thermal performance for
potential applications. We investigate the lattice dynamics and thermodynamic
properties of MoS2, MoSe2, and WS2 by first principles calculations. The
obtained phonon frequencies and thermal conductivities agree well with the
measurements. Our results show that the thermal conductivity of MoS2 is highest
among the three materials due to its much lower average atomic mass. We also
discuss the competition between mass effect, interatomic bonding and anharmonic
vibrations in determining the thermal conductivity of WS2. Strong covalent W-S
bonding and low anharmonicity in WS2 are found to be crucial in understanding
its much higher thermal conductivity compared to MoSe2.Comment: 19 pages, 7 figure
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of
success in image classification. The main difference among them is their
encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of
Vector Quantization (VQ) into the framework of SPM. Although the methods
achieve a higher recognition rate than the traditional SPM, they consume more
time to encode the local descriptors extracted from the image. In this paper,
we propose using Low Rank Representation (LRR) to encode the descriptors under
the framework of SPM. Different from SC, LRR considers the group effect among
data points instead of sparsity. Benefiting from this property, the proposed
method (i.e., LrrSPM) can offer a better performance. To further improve the
generalizability and robustness, we reformulate the rank-minimization problem
as a truncated projection problem. Extensive experimental studies show that
LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving
competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201
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