554,053 research outputs found
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Pooling is a ubiquitous operation in image processing algorithms that allows
for higher-level processes to collect relevant low-level features from a region
of interest. Currently, max-pooling is one of the most commonly used operators
in the computational literature. However, it can lack robustness to outliers
due to the fact that it relies merely on the peak of a function. Pooling
mechanisms are also present in the primate visual cortex where neurons of
higher cortical areas pool signals from lower ones. The receptive fields of
these neurons have been shown to vary according to the contrast by aggregating
signals over a larger region in the presence of low contrast stimuli. We
hypothesise that this contrast-variant-pooling mechanism can address some of
the shortcomings of max-pooling. We modelled this contrast variation through a
histogram clipping in which the percentage of pooled signal is inversely
proportional to the local contrast of an image. We tested our hypothesis by
applying it to the phenomenon of colour constancy where a number of popular
algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and
Double-Opponency). For each of these methods, we investigated the consequences
of replacing their original max-pooling by the proposed
contrast-variant-pooling. Our experiments on three colour constancy benchmark
datasets suggest that previous results can significantly improve by adopting a
contrast-variant-pooling mechanism
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
How do computers and intelligent agents view the world around them? Feature
extraction and representation constitutes one the basic building blocks towards
answering this question. Traditionally, this has been done with carefully
engineered hand-crafted techniques such as HOG, SIFT or ORB. However, there is
no ``one size fits all'' approach that satisfies all requirements. In recent
years, the rising popularity of deep learning has resulted in a myriad of
end-to-end solutions to many computer vision problems. These approaches, while
successful, tend to lack scalability and can't easily exploit information
learned by other systems. Instead, we propose SAND features, a dedicated deep
learning solution to feature extraction capable of providing hierarchical
context information. This is achieved by employing sparse relative labels
indicating relationships of similarity/dissimilarity between image locations.
The nature of these labels results in an almost infinite set of dissimilar
examples to choose from. We demonstrate how the selection of negative examples
during training can be used to modify the feature space and vary it's
properties. To demonstrate the generality of this approach, we apply the
proposed features to a multitude of tasks, each requiring different properties.
This includes disparity estimation, semantic segmentation, self-localisation
and SLAM. In all cases, we show how incorporating SAND features results in
better or comparable results to the baseline, whilst requiring little to no
additional training. Code can be found at:
https://github.com/jspenmar/SAND_featuresComment: CVPR201
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