111,344 research outputs found
An Approach for the Empirical Validation of Software Complexity Measures
Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper
empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics
Evaluation Criteria for Object-oriented Metrics
In this paper an evaluation model for object-oriented (OO) metrics is proposed. We have evaluated the existing evaluation criteria for OO metrics, and based on the observations, a model is proposed which tries to cover most of the features for the evaluation of OO metrics. The model is validated by applying it to existing OO metrics. In contrast to the other existing criteria, the proposed model is simple in implementation and includes the practical and important aspects of evaluation; hence it suitable to evaluate and validate any OO complexity metric
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
This article presents a continuous model for hierarchical networks based on a
combination of mathematically derived models of receptive fields and
biologically inspired computations. Based on a functional model of complex
cells in terms of an oriented quasi quadrature combination of first- and
second-order directional Gaussian derivatives, we couple such primitive
computations in cascade over combinatorial expansions over image orientations.
Scale-space properties of the computational primitives are analysed and it is
shown that the resulting representation allows for provable scale and rotation
covariance. A prototype application to texture analysis is developed and it is
demonstrated that a simplified mean-reduced representation of the resulting
QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Historical collaborative geocoding
The latest developments in digital have provided large data sets that can
increasingly easily be accessed and used. These data sets often contain
indirect localisation information, such as historical addresses. Historical
geocoding is the process of transforming the indirect localisation information
to direct localisation that can be placed on a map, which enables spatial
analysis and cross-referencing. Many efficient geocoders exist for current
addresses, but they do not deal with the temporal aspect and are based on a
strict hierarchy (..., city, street, house number) that is hard or impossible
to use with historical data. Indeed historical data are full of uncertainties
(temporal aspect, semantic aspect, spatial precision, confidence in historical
source, ...) that can not be resolved, as there is no way to go back in time to
check. We propose an open source, open data, extensible solution for geocoding
that is based on the building of gazetteers composed of geohistorical objects
extracted from historical topographical maps. Once the gazetteers are
available, geocoding an historical address is a matter of finding the
geohistorical object in the gazetteers that is the best match to the historical
address. The matching criteriae are customisable and include several dimensions
(fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is
to facilitate historical work, we also propose web-based user interfaces that
help geocode (one address or batch mode) and display over current or historical
topographical maps, so that they can be checked and collaboratively edited. The
system is tested on Paris city for the 19-20th centuries, shows high returns
rate and is fast enough to be used interactively.Comment: WORKING PAPE
- …