Authors:
David Geisler
;
Wolfgang Fuhl
;
Thiago Santini
and
Enkelejda Kasneci
Affiliation:
University of Tübingen, Germany
Keyword(s):
Saliency Sandbox, Feature Maps, Attention Maps, Saliency Maps, Bottom Up.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Early and Biologically-Inspired Vision
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Visual Attention and Image Saliency
Abstract:
Saliency maps are used to predict the visual stimulus raised from a certain region in a scene. Most approaches
to calculate the saliency in a scene can be divided into three consecutive steps: extraction of feature maps,
calculation of activation maps, and the combination of activation maps. In the past two decades, several new
saliency estimation approaches have emerged. However, most of these approaches are not freely available as
source code, thus requiring researchers and application developers to reimplement them. Moreover, others are
freely available but use different platforms for their implementation. As a result, employing, evaluating, and
combining existing approaches is time consuming, costly, and even error-prone (e.g., when reimplementation
is required). In this paper, we introduce the Saliency Sandbox, a framework for the fast implementation and
prototyping of saliency maps, which employs a flexible architecture that allows designing new saliency maps
by combining exist
ing and new approaches such as Itti & Koch, GBVS, Boolean Maps and many more. The
Saliency Sandbox comes with a large set of implemented feature extractors as well as some of the most popular
activation approaches. The framework core is written in C++; nonetheless, interfaces for Matlab and Simulink
allow for fast prototyping and integration of already existing implementations. Our source code is available
at: www.ti.uni-tuebingen.de/perception.
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