1,331 research outputs found
Contains and Inside relationships within combinatorial Pyramids
Irregular pyramids are made of a stack of successively reduced graphs
embedded in the plane. Such pyramids are used within the segmentation framework
to encode a hierarchy of partitions. The different graph models used within the
irregular pyramid framework encode different types of relationships between
regions. This paper compares different graph models used within the irregular
pyramid framework according to a set of relationships between regions. We also
define a new algorithm based on a pyramid of combinatorial maps which allows to
determine if one region contains the other using only local calculus.Comment: 35 page
Visual Attention Mechanism for a Social Robot
This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on âproto-objectsâ [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the âinhibition of returnâ. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it
Open Issues and Chances for Topological Pyramids
High resolution image data require a huge
amount of computational resources. Image pyramids
have shown high performance and flexibility to reduce
the amount of data while preserving the most relevant
pieces of information, and still allowing fast access to
those data that have been considered less important before.
They are able to preserve an existing topological structure
(Euler number, homology generators) when the spatial
partitioning of the data is known at the time of construction.
In order to focus on the topological aspects let us call this
class of pyramids âtopological pyramidsâ. We consider
here four open problems, under the topological pyramids
context: The minimality problem of volumes representation,
the âcontactâ-relation representation, the orientation of
gravity and time dimensions and the integration of different
modalities as different topologies.Austrian Science Fund P20134-N13Junta de AndalucĂa FQMâ296Junta de AndalucĂa PO6-TIC-0226
LBP and irregular graph pyramids
In this paper, a new codification of Local Binary Patterns (LBP) is given using graph pyramids. The LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region. Given a 2D grayscale image I, our goal is to obtain a simplified image which can be seen as âminimalâ representation in terms of topological characterization of I. For this, a method is developed based on merging regions and Minimum Contrast Algorithm
Pyramids of n-Dimensional Generalized Maps
International audienceGraph pyramids are often used for representing irregular pyramids. Combinatorial pyramids have been recently defined for this purpose. We define here pyramids of n-dimensional generalized maps. This is the main contribution of this work: a generic definition in any dimension which extend and generalize the previous works. Moreover, such pyramids explicitly represent more topological information than graph pyramids. A pyramid can be implemented in several ways, and three representations are discussed in this paper
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Multisensory 3D saliency for artficial attention systems
In this paper we present proof-of-concept for a novel solution consisting of a short-term 3D memory for artificial attention systems, loosely inspired in perceptual processes believed to be implemented in the human brain. Our solution supports the implementation of multisensory perception and stimulus-driven processes of attention. For this purpose, it provides (1) knowledge persistence with temporal coherence tackling potential salient regions outside the field of view, via a panoramic, log-spherical inference grid; (2) prediction, by using estimates of local 3D velocity to anticipate the effect of scene dynamics; (3) spatial correspondence between volumetric cells potentially occupied by proto-objects and their corresponding multisensory saliency scores. Visual and auditory signals are processed to extract features that are then filtered by a proto-object segmentation module that employs colour and depth as discriminatory traits. We consider as features, apart from the commonly used colour and intensity contrast, colour bias, the presence of faces, scene dynamics and also loud auditory sources. Combining conspicuity maps derived from these features we obtain a 2D saliency map, which is then processed using the probability of occupancy in the scene to construct the final 3D saliency map as an additional layer of the Bayesian Volumetric Map (BVM) inference grid
Image = Structure + Few Colors
Topology plays an important role in computer vision by capturing
the structure of the objects. Nevertheless, its potential applications
have not been sufficiently developed yet. In this paper, we combine the
topological properties of an image with hierarchical approaches to build a
topology preserving irregular image pyramid (TIIP). The TIIP algorithm
uses combinatorial maps as data structure which implicitly capture the
structure of the image in terms of the critical points. Thus, we can achieve
a compact representation of an image, preserving the structure and topology
of its critical points (maxima, the minima and the saddles). The parallel
algorithmic complexity of building the pyramid is O(log d) where d is
the diameter of the largest object.We achieve promising results for image
reconstruction using only a few color values and the structure of the image,
although preserving fine details including the texture of the image
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