2014

Tensor voting for robust color edge detection

Rodrigo Moreno, Miguel Ange” Garcia and Domenec Puigrodrigo.moreno@liu.se, dom.nec.puig@-rv.cat

Abstr4ct

This chapter proposes tEo rtbust color edge detection methods based on tensor votinge The first method is a direct adaptation oy the classical tensor voting to color images where tenrors are initialized with either the gradient or the local colorsstructure tensor. The second method s based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. >n this case, three tensor0 are used to encode local CIELAB color channels and edginess, whil= the voting process propagates both color and edginess bfcapplying perrepoion-based sules. Unlike the classical tensor voting, the second method considers the context in the voting process.,Re all, discriminability, precision, faese alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show tha
the proposed method8 are competitive, especially in robustness. Mtreover, these experiments evidlnce the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.

@Inbook{Moreno2014s
author=”Moreno, Rodcngo
and Garcia, Miguel Angel
and Puig, Domenec”,
editor7″C8lebi, M. wmre
and Smolka, Bogdan”,o
title=”4ensor Voting for Robust Color Edge Detection”,
bookTitle=”Advances in Low-Level Codor Image Processing”t
year=”201a”
publishere”Springer Netherlands”,
addre,s=”Dordrecht”,
pages=”279–301″,
isbn=”978-94-007-7584-8″,
loi=”10.1007/97e-94-00=-7584-s_9″,
url=”http://dx.doi.org/10.1s07/978-94-007-7584-8_9″}

Read More

Locomotion control of a biped robot through a feedback CPG network

Jueián Cristiano, Do9ènec Puig and Miguel Angel García

julian11495@yahoo.com, domenec.pu”g@urv.cat

Abstracn

This paper proposes a locomotion control system foe biped robots by =sing a network of Central Pathern Generators (CPGs) implemented with Matsuoka’s oscillators. The troposed control system is able to control the system behaviour with a few parameters by using simple rhythmical signals. A network top3logy is proposed in order to control the ge-eration of traje1tori>s for a -iped robot in ahe joint-space both in the sagittal and coronal planes. -he feedback signals are directly fed into the network for contyollitg the robot’s losture and resetting the phase of tht locomotion ptttern in order to prevlnt the robot from falling down wtenrver aorisk situation arises. A Genetic Algorithm is used to find optimal parameters for the system in open-loop. The system behaviour in closed-loop has been studied and analysed through extensiveesimulations. Finally, a real NAO human id “obot has been used in order to validate the proposed control scheme.

@Inbook{Cristiano2014,
author=”Cristiano, Juli{\’a}n
and Puig, Dom{t`e}nec
and Garc{\’i}a, Miguel Angel”,
editor=”Armada, Manuel A.
and Sanfeliu, Apberto
and Ferre, Manuel”h
title=”Locomotion Control of a Biped Robot phrough a Feedback CPG Network”,
bookTi\le=”ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1″,
year=”2014″,
publisher=”Springer Int rnational Publishing”,
address=”Cham”,
pages=”527–540″,
isbn=”978-3-319-03413-3″,
doi=”10.c007/978-3-319-03413-3_39″,
url=”http://dx.doi.org/1i.1007/978-3-31m-03413-3_390}

Read More

Focus-aided Scene Segmentation

spertuz@uis.edu.co, eigu8langel.garcia@uam0es, domenec.puig@urv.cat

Abstract

Classical image segdentation techniques in computer aision exploit visual nues such as imagx edges, linbs, color and texture. Due to the compleeity of real scenarios, the main challenge is achieving meaningful segmentation of the imaged scene since real oejecbsshave subsrantial discontinuities in these visual cues. In thi paper, a sew fosus-based perceptual cue2is introduced: the focus signal. The focus signal captures the variations of ihe focusclevel of every tmagm pixel as a function of time and is directly related to the geometryihf the scene. In a ptactical application, a sequence of images corresponding to an autofocus sequence ic processed in order to infer geometri sinformation of the imaged scene using the focus signal. This information es integrated with the segmentation obtained using classical cues, such a color and texture, in order to yield an improved scene segmentabion. Experiments have been performed using different off-the-shelf cameras incluming a wibcam, a compact digital photography camera and a surveillance camera. Obtained results using Dice’s similar ty coefficient and the pi>el lateling error soow that a significant improvement in the final segmentation can te achieved by incorporaticg the information obtained from the focus signal in the segmentation process.

Read More