9 research outputs found
YACCLAB - Yet Another Connected Components Labeling Benchmark
The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark
Connected Components Labeling on DRAGs: Implementation and Reproducibility Notes
In this paper we describe the algorithmic implementation details of "Connected Components Labeling on DRAGs'' (Directed Rooted Acyclic Graphs), studying the influence of parameters on the results. Moreover, a detailed description of how to install, setup and use YACCLAB (Yet Another Connected Components LAbeling Benchmark) to test DRAG is provided
Towards Reliable Experiments on the Performance of Connected Components Labeling Algorithms
The problem of labeling the connected components of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for many kinds of tests, which analyze the methods from different perspectives. An extensive set of experiments among state-of-the-art techniques is reported and discussed
Parallel Image Processing Using a Pure Topological Framework
Image processing is a fundamental operation
in many real time applications, where lots of parallelism
can be extracted. Segmenting the image into different
connected components is the most known operations, but
there are many others like extracting the region adjacency
graph (RAG) of these regions, or searching for features
points, being invariant to rotations, scales, brilliant
changes, etc. Most of these algorithms part from the basis
of Tracing-type approaches or scan/raster methods. This
fact necessarily implies a data dependence between the
processing of one pixel and the previous one, which
prevents using a pure parallel approach. In terms of time
complexity, this means that linear order O(N) (N being the
number of pixels) cannot be cut down. In this paper, we
describe a novel approach based on the building of a pure
Topological framework, which allows to implement fully
parallel algorithms. Concerning topological analysis, a first
stage is computed in parallel for every pixel, thus
conveying the local neighboring conditions. Then, they are
extended in a second parallel stage to the necessary global
relations (e.g. to join all the pixels of a connected
component). This combinatorial optimization process can
be seen as the compression of the whole image to just one
pixel. Using this final representation, every region can be
related with the rest, which yields to pure topological
construction of other image operations. Besides, complex
data structures can be avoided: all the processing can be
done using matrixes (with the same indexation as the
original image) and element-wise operations. The time
complexity order of our topological approach for a mĂ—n
pixel image is near O(log(m+n)), under the assumption that
a processing element exists for each pixel. Results for a
multicore processor show very good scalability until the
memory bandwidth bottleneck is reached, both for bigger
images and for much optimized implementations. The
inherent parallelism of our approach points to the
direction that even better results will be obtained in other
less classical computing architectures.1Ministerio de EconomĂa y Competitividad (España) TEC2012-37868-C04-02AEI/FEDER (UE) MTM2016-81030-PVPPI of the University of Sevill
Computing the Component-Labeling and the Adjacency Tree of a Binary Digital Image in Near Logarithmic-Time
Connected component labeling (CCL) of binary images is
one of the fundamental operations in real time applications. The adjacency
tree (AdjT) of the connected components offers a region-based
representation where each node represents a region which is surrounded
by another region of the opposite color. In this paper, a fully parallel
algorithm for computing the CCL and AdjT of a binary digital image
is described and implemented, without the need of using any geometric
information. The time complexity order for an image of m Ă— n pixels
under the assumption that a processing element exists for each pixel is
near O(log(m+ n)). Results for a multicore processor show a very good
scalability until the so-called memory bandwidth bottleneck is reached.
The inherent parallelism of our approach points to the direction that
even better results will be obtained in other less classical computing
architectures.Ministerio de EconomĂa y Competitividad MTM2016-81030-PMinisterio de EconomĂa y Competitividad TEC2012-37868-C04-0
Efficient Source Finding for Radio Interferometric Images
Object detection in astronomical images, generically referred to as source
finding, is often performed before the object characterisation stage in
astrophysical processing work flows. In radio astronomy, source finding has
historically been performed by bespoke off-line systems; however, modern data
acquisition systems as well as those proposed for upcoming observatories such
as the Square Kilometre Array (SKA), will make this approach unfeasible. One
area where a change of approach is particularly necessary is in the design of
fast imaging systems for transient studies. This paper presents a number of
advances in accelerating and automating the source finding in such systems.Comment: submitted to Astronomy & Computin
Kidney and tumor segmentation using an ensemble of deep neural networks
For the segmentation of kidney and tumor task, we propose a two stages model that consists of several classification networks and segmentation models. The first stage is the foreground and background classification subnetwork, this stage is to recognize whether there are kidneys or tumors on images, so we propose a classification model called RD-Net which can effectively reduce the errors caused by a large of background images and improve the efficiency of the whole segmentation results. The second stage is the segmentation model used to predict the contour of the target (kidney or tumor). Therefore, we propose Att-ResUnet model and multi-scale ensemble of postprocessing methods used to integrate the predicted results of multiple models, so as to improve the accuracy of prediction results
Optical Music Recognition
Diplomová práce specifikuje digitálnĂ metody optickĂ©ho rozpoznávánĂ notovĂ©ho záznamu s podrobnou analĂ˝zou metod zaloĹľenĂ˝ch na odstranÄ›nĂ notovĂ˝ch linek a vytvoĹ™enĂ testovacĂho programu, kterĂ˝ automaticky pĹ™evede obrázky zapsanĂ© v notovĂ©m zápisu na digitálnĂ formát. Tato práce shrnuje poznatky jak z rešeršnĂ, tak z praktickĂ© části. V rešeršnà části jsou popsány stěžejnĂ kapitoly jako architektura OMR zahrnujĂcĂ processing, klasifikace symbolĹŻ, postprocessing a dalšĂ. Praktická část diplomovĂ© práce prezentuje vĂ˝sledky vĂ˝voje a testovánĂ navrĹľenĂ© aplikace.The diploma thesis specifies digital methods of optical recognition of a notation, by detailed analysis of methods based on removal of notation lines and creation of a test program which automatically converts the images written in the notation into digital format. This work summarizes the knowledge from the research and practical part. In the research section, key chapters are described as OMR architecture, including processing, symbol classification, postprocessing, and more. The practical part of the thesis presents the results of the development and testing of the proposed application.