268 research outputs found
Weak Visibility Queries of Line Segments in Simple Polygons
Given a simple polygon P in the plane, we present new algorithms and data
structures for computing the weak visibility polygon from any query line
segment in P. We build a data structure in O(n) time and O(n) space that can
compute the visibility polygon for any query line segment s in O(k log n) time,
where k is the size of the visibility polygon of s and n is the number of
vertices of P. Alternatively, we build a data structure in O(n^3) time and
O(n^3) space that can compute the visibility polygon for any query line segment
in O(k + log n) time.Comment: 16 pages, 9 figures. A preliminary version of this paper appeared in
ISAAC 2012 and we have improved results in this full versio
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
Convolution is a fundamental operation in many applications, such as computer
vision, natural language processing, image processing, etc. Recent successes of
convolutional neural networks in various deep learning applications put even
higher demand on fast convolution. The high computation throughput and memory
bandwidth of graphics processing units (GPUs) make GPUs a natural choice for
accelerating convolution operations. However, maximally exploiting the
available memory bandwidth of GPUs for convolution is a challenging task. This
paper introduces a general model to address the mismatch between the memory
bank width of GPUs and computation data width of threads. Based on this model,
we develop two convolution kernels, one for the general case and the other for
a special case with one input channel. By carefully optimizing memory access
patterns and computation patterns, we design a communication-optimized kernel
for the special case and a communication-reduced kernel for the general case.
Experimental data based on implementations on Kepler GPUs show that our kernels
achieve 5.16X and 35.5% average performance improvement over the latest cuDNN
library, for the special case and the general case, respectively
Algorithms on Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain
In this paper, we study the problem of moving sensors on a line to form a
barrier coverage of a specified segment of the line such that the maximum
moving distance of the sensors is minimized. Previously, it was an open
question whether this problem on sensors with arbitrary sensing ranges is
solvable in polynomial time. We settle this open question positively by giving
an time algorithm. For the special case when all sensors have
the same-size sensing range, the previously best solution takes time.
We present an time algorithm for this case; further, if all
sensors are initially located on the coverage segment, our algorithm takes
time. Also, we extend our techniques to the cycle version of the problem
where the barrier coverage is for a simple cycle and the sensors are allowed to
move only along the cycle. For sensors with the same-size sensing range, we
solve the cycle version in time, improving the previously best
time solution.Comment: This version corrected an error in the proof of Lemma 2 in the
previous version and the version published in DCG 2013. Lemma 2 is for
proving the correctness of an algorithm (see the footnote of Page 9 for why
the previous proof is incorrect). Everything else of the paper does not
change. All algorithms in the paper are exactly the same as before and their
time complexities do not change eithe
Neuron Segmentation Using Deep Complete Bipartite Networks
In this paper, we consider the problem of automatically segmenting neuronal
cells in dual-color confocal microscopy images. This problem is a key task in
various quantitative analysis applications in neuroscience, such as tracing
cell genesis in Danio rerio (zebrafish) brains. Deep learning, especially using
fully convolutional networks (FCN), has profoundly changed segmentation
research in biomedical imaging. We face two major challenges in this problem.
First, neuronal cells may form dense clusters, making it difficult to correctly
identify all individual cells (even to human experts). Consequently,
segmentation results of the known FCN-type models are not accurate enough.
Second, pixel-wise ground truth is difficult to obtain. Only a limited amount
of approximate instance-wise annotation can be collected, which makes the
training of FCN models quite cumbersome. We propose a new FCN-type deep
learning model, called deep complete bipartite networks (CB-Net), and a new
scheme for leveraging approximate instance-wise annotation to train our
pixel-wise prediction model. Evaluated using seven real datasets, our proposed
new CB-Net model outperforms the state-of-the-art FCN models and produces
neuron segmentation results of remarkable qualityComment: miccai 201
- …