68,721 research outputs found
A Testability Analysis Framework for Non-Functional Properties
This paper presents background, the basic steps and an example for a
testability analysis framework for non-functional properties
Diagnosing Errors in DbC Programs Using Constraint Programming
Model-Based Diagnosis allows to determine why a correctly
designed system does not work as it was expected. In this paper, we propose
a methodology for software diagnosis which is based on the combination
of Design by Contract, Model-Based Diagnosis and Constraint
Programming. The contracts are specified by assertions embedded in the
source code. These assertions and an abstraction of the source code are
transformed into constraints, in order to obtain the model of the system.
Afterwards, a goal function is created for detecting which assertions or
source code statements are incorrect. The application of this methodology
is automatic and is based on Constraint Programming techniques.
The originality of this work stems from the transformation of contracts
and source code into constraints, in order to determine which assertions
and source code statements are not consistent with the specification.Ministerio de Ciencia y TecnologÃa DPI2003-07146-C02-0
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4.Comment: This paper has been accepted by IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR 2019
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