Five Minute Speech: An Overview of Activities Developed in Disciplines and Guided Studies

Universidade Federal do Rio de Janeiro - UFRJ - Campus Cidade Universitária - Rio de Janeiro - Ilha do Fundão, CEP: 21941-972 - COPPE/PESC/LCG
FMS :: Five Minute Speech :: An Overview of Activities Developed in Disciplines and Guided Studies :: Laboratory Seminars and Meetings :: January, 2014

Five Minute Speech
An Overview of Activities Developed in Disciplines and Guided Studies

Michel Alves dos Santos
Pós-Graduação em Engenharia de Sistemas e Computação
Universidade Federal do Rio de Janeiro - UFRJ - COPPE
Cidade Universitária - Rio de Janeiro - CEP: 21941-972
Docentes Responsáveis: Prof. Dsc. Ricardo Marroquim & Prof. PhD. Cláudio Esperança

{michel.mas, michel.santos.al}@gmail.com

January, 2014

Michel Alves dos Santos: Laboratório de Computação Gráﬁca - LCG

Pós-Graduação em Engenharia de Sistemas e Computação - PESC


Introduction
Activities developed since the
last meeting to date:

1.01
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0.40
0.20
0.00

1.005
1
0.995
0.99

0.00
1.00

0.20
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0.60
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0.40
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Adjustment and ﬁnalization of the computer
vision project;
Results obtained by the method ‘CapacityConstrained Point Distributions’;
Increased proﬁciency in the use of Gnuplot,
Maxima and Scilab tools;
Extension of studies on the synthesis of
images (texture and noise);
Update contents of the institutional page;
Survey of bibliography and possible themes
for dissertation preparation.
Presentation Hosted on: http://www.lcg.ufrj.br/Members/malves/index



Capacity-Constrained Point Distributions Results
LCG :: Laboratory of Computer Graphics :: malves@cos.ufrj.br :: http://www.lcg.ufrj.br/Members/malves

Capacity-Constrained Point Distribution
Michel Alves

December, 2013
Rio de Janeiro - Brazil
Graduate Program in Systems Engineering and Computing :: Federal University of Rio de Janeiro :: UFRJ

Applications: Stippling, HDR Sampling Radiance/Luminance, etc.



Possible Dissertation Themes
Eﬀectiveness of Image Quality Assessment Indexes on
Detection of Structural and Nonstructural Distortions:
Use of Image Quality Assessment Indexes.
Detection of Structural and Nonstructural Distortions.
Admissible levels of distortion for: noise, blocking, compression,
fusion/blending, watermarking, etc.

A Framework for Harmonic Color Measures:
Main objective: to introduce a quality comparison scale for color
images that takes into account the "balance" or harmony of the
existing sets of colors in the input model;

Intelligent Transfer of Thematic Harmonic Color Palettes:
Main objective: to introduce a "smart" transfer method of
harmonic color palettes based on a particular theme or color
expression model.

Fast Procedural Texture Synthesis - An Approach Based on
GPU Use:
Fast generation of procedural textures using the parallel
architecture of GPUs.



Effectiveness of Image Quality Assessment
WHY IS IMAGE QUALITY ASSESSMENT SO DIFFICULT?
Zhou Wang and Alan
Noname manuscript No.
(will be inserted by the editor)

C. Bovik

Lab for Image and Video Engi., Dept. of ECE
Univ. of Texas at Austin, Austin, TX 78703-1084
zhouwang@ieee.org, bovik@ece.utexas.edu

Ligang Lu
IBM T. J. Watson Research Center
Yorktown Heights, NY 10598
lul@us.ibm.com

Visual Quality Assessment Algorithms : What Does the
ABSTRACT
However, the MOS method is too inconvenient, slow and expenFuture Hold?

sive for practical usage. The goal of objective image and video
Image quality assessment plays an important role in various image
quality assessment research is to supply quality metrics that can
processing applications. A great · Alan C. has been made in repredict perceived image and video quality automatically. Peak
Anush K. Moorthy deal of effort Bovik
cent years to develop objective image quality metrics that correlate
Signal-to-Nose Ratio (PSNR) and Mean Squared Error (MSE) are
with perceived quality measurement. Unfortunately, only limited
the most widely used objective image quality/distortion metrics,
success has been achieved. In this paper, we provide some insights
Error are widely criticized as well, for not correlating well with
Error
but they
on why image quality assessment is so difficult by pointing out the
perceived
Weighting quality measurement. In the past three to four decades,
Masking
weaknesses of the error sensitivity based framework, which has
a great deal of effort has been made to develop new objective imbeen used by most image quality assessment approaches in the litage and video quality measurement approaches which incorporate
erature.
Original
Error
Error
perceptual quality measures by considering human visual system
Received: date / Accepted: date
Furthermore, we propose a new philosophy in designing im- Weightingcharacteristics [1, 2, 3, 4, 5, 6, 7, 8, 9].
signal
Qualtiy/
Masking
(HVS)
Channel
Error
age quality metrics: Preprocessing
The main function of the human eyes is to
Surprisingly, only limited success has been Distortion It has
achieved.
.
.
Decomposition
Summation
extract structural information from the viewing field, and the hu.
.
.
been reported that none of .the complicated objective image qualMeasure
Distorted
Abstract Creating algorithms capable of predicting.
man visual system is highly adapted for this purpose. Therefore, a the perceived quality of a visual shown any clear advantage over
.
.
.
ity metrics in the literature has
signal
.
.
stimulus defines the field of objective good approximameasurement of structural distortion should be a visual quality assessment (QA). The field of ob- such as PSNR under strict testing
simple mathematical measures
tion ofjective QA has received tremendousthe new philosophy,
perceived image distortion. Based on attention in the recent conditions and different image distortion environments [2, 9, 10].
past, with many successful
we implemented a simple but effective image quality indexing alError is not with the past
algorithms being proposed for this purpose. Our concern For example, in Error test conducted by the Video Quality Exhere
a recent
gorithm, which is very promising as shown by our current results. Weighting quality assessment
Masking
however; in this paper we discuss our vision for the future of visual
perts Group (VQEG) in validating objective video quality assessresearch.
ment methods, there are eight to nine proponent models whose
1. INTRODUCTION
We first introduce the area of quality assessment and performance is statistically indistinguishable [2]. Unfortunately,
state its relevance. We dethis and of models includes
Fig. 1. Error algorithmic performancegroup define terms thatPSNR.
scribe current standards for gaugingsensitivity based image quality measurement.
Michel Alves dosquality measurement is crucial for mostGráfica processing
It Pós-Graduação that Engenharia de objective image quality - PESC
is worth noting em most proposed Sistemas e Computação
Image Santos: Laboratório de Computação image - LCG


A Framework for Harmonic Color Measures
Saliency-Guided Consistent Color
Harmonization
2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Yoann Baveye, Fabrice Urban, Christel Chamaret, Vincent Demoulin,
and Pierre Hellier
No-reference Harmony-guided Quality Assessment
Technicolor Research and Innovation, Rennes, France
{baveyey,urbanf,chamaretc,demoulinv,hellierp}@technicolor.com
Christel Chamaret and Fabrice Urban
https://research.technicolor.com/rennes/

Technicolor
975, avenue des Champs Blancs ZAC des Champs Blancs CS 17616 35576 Cesson Sevigne
Hierarchical
Harmony Map
christel.chamaret@technicolor.com, fabrice.urban@technicolor.com
Harmony Distance
(HSV space)

(RGB
Abstract. The space) of this paper is automatic color harmonization,
focus
Activity
which amounts to re-coloring an image Masking Perceptual
so that the obtained color palette
Abstract
Activity Masking
Inter-level
Map
Spatial
(YUV space)
masking
accumulation
is more harmonious for human observers. The proposed automatic algo- pooling
Color harmony of simple color patterns has been widely
rithm buildsRules the pioneering works described in [3,12] where templates
on defined then by psychologistudied for color design.
of harmonious colors are defined on the hue wheel. We bring three conPerceptual
cal experiments have been applied to derive image aesthetic
Score
Contrast Masking
Harmony Map
Masking
scores, tributions in this But what is first, saliency Contrastis used to predict the most
or to re-colorize pictures. paper: harmonious
[9] Map
DWT (YUV space)
or not in an image? What can the human eye perceive
attractive visual areas and estimate a consistent harmonious template.
disharmonious? Extensive research has been done in the
context Second,assessment to define what is3. Overview of the complete system.
of quality an efficient color segmentation algorithm, adapted from [4], is
Figure visible or
not in images and videos. performbased on - LCG color mapping. Third, a new mapping
Michel Alves dos Santos: Laboratório de Computação Gráfica human viPós-Graduação em Engenharia de Sistemas e Computação - PESC
proposed to Techniques consistent


Intelligent Transfer of Harmonic Palettes
Vis Comput (2010) 26: 933–942
DOI 10.1007/s00371-010-0498-y

O R I G I N A L A RT I C L E

Computational Aesthetics

xample-based painting guided by color features

Example-based painting guided by color features
Hua Huang · Yu Zang · Chen-Feng Li

Automatic Mood-Transferring
between Color Images
Published Chuan-Kai Yang and Li-Kai Peng ■ National Taiwan University of Science and Technology
online: 14 April 2010
© Springer-Verlag 2010

W

Abstract In this paper, by analyzing and learning the color
1 Introduction
features of the reference paintingith the a novel set of meawith digital camera’s invention, cap- rithm then uses the selected image to determine the
turing images has become extremely input image’s color distribution. We thereby achieveexampleIn computer painting and image synthesis, an
sures, an example-based approach is and widespread,transfer
easy developed to while image data more accurate and justifiable color conversion rebased approach creates paintings/images by automatically
some key color features has become more robustthe source imfrom the template to and easy to manipulate. sults, while also preserving spatial coherence. Here,
modifying the source image to imitate some specific feaop- we further describe our solutions
age. First, color featuresAmong the template painting is anaof a given many possible image-processingtures of the reference image. Many and their results
existing
tions, users have become increasingly interested and compare them to existing approaches. methods try to
lyzed in terms of hue distribution and the overall color tone. altering the texture features of a painting [10, 16] and although
in changing an image’s tone or mood by
learn
Color-mood conversion
These features are then its colors—such as converting the algoextracted and learned by a tree’s leaves from present impressive results, the color features of a paintthey
green to yellow to suggest a
Our approach adopts Whelan’s classification
rithm through an optimization scheme. Next, to change of season. are seldom considered. In this paper, we mainly focus
ensure the
ing
Groundbreaking work by Reinhard and colleagues scheme,2 dividing the color spectrum into 24 catspatial coherence of themade such a conversion possible and extremely on how to learnmoods. To create a color features of the temfinal result, a segmentation based
sim- egories or and extract the mood image dataple.1 In their approach, users provide an input plate base, we and to transfer to five source image
post processing is performed. Finally, a new color blending image, painting,collect from the Web the to 10 images for the color
Michel Alves dos Santos: avoids the dependence of edge detection and desired Pós-Graduação em Engenharia de Sistemas e Computação - PESC
along Computação Gráfica - LCG
and category; these
model, which Laboratório de with a reference image to exemplify the theme each color style. serve as our reference pool.

94

EUROGRAPHICS 2010/ H. Hauser and E. Reinhard
STAR – State of The Art Report

Fast Procedural Texture Synthesis
Pacific Graphics 2012

Volume 31 (2012), Number 7

C. Bregler, P. Sander, and M. Wimmer
(Guest Editors)

State of the Art in Procedural Noise Functions

Procedural GPU Shading Ready for Use

A. Lagae1,2 S. Lefebvre2,3 R. Cook4 T. DeRose4 G. Drettakis2 D.S. Ebert5 J.P. Lewis6 K. Perlin7 M. Zwicker8

Stefan Gustavson , Linköping University, Sweden and Ian McEwan , Ashima Research, USA
Multi-scale Assemblage for Procedural Texturing
1

1

2

Katholieke Universiteit Leuven 2 REVES/INRIA Sophia-Antipolis 3 ALICE/INRIA Nancy Grand-Est / Loria
Pixar Animation Studios 5 Purdue University 6 Weta Digital 7 New York University 8 University of Bern

4

G. Gilet1 , J-M. Dischler2 and D. Ghazanfarpour1
Abstract

1 XLIM

- UMR CNRS 7252, University of Limoges, France

2 LSIIT - UMR CNRS 7005, University from off-line rendering
Procedural noise functions are widely used in Computer Graphics,of Strasbourg, France in movie production to
interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one
of its main attractions. This state-of-the-art report is motivated by the inherent importance of noise in graphics,
the widespread use of noise in industry, and the fact that many recent research developments justify the need for an
up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as
well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions
in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and
A selection of procedural patterns, generated entirely on the GPU withoutnoise texture accesses.on stochastic processes and
well established methods. We first formally define procedural any functions based The left two spheres use Perlin simplex
noise by itself then in a fractal sum. The right two spheresnoiseWorley cellular noise in different ways. The functions the bottom shows
and classify and review existing procedural use functions. We discuss how procedural noise plane at are used
Perlin and Neyret's ”flow noise”,how they are applied on surfaces. We then introduce analysis tools and apply them to evaluate easy to
for modeling and with rotating gradients. All these shaders are animated, have analytic derivatives that are
compute, andand compare the major considered fornoise generation. We finally identify several directions for future work.
are fast enough to be approaches to routine use even on previous generation GPU hardware.

Keywords: procedural noise of software shadWhile all these advantages have made procedural
Procedural patterns have been a staple function, noise, stochastic process, procedural, Perlin noise, wavelet noise, shading
anisotropic noise revolutionized the industry
popular for surface noise, solid noise, anti-aliasing,
ing for decades. Perlin noise, sparse convolution noise, Gabor noise, spot noise,offline rendering, real time applications have not
filtering, stochasticfor technical achievement.procedural adopted this practice. One obvious reason is that the GPU
modeling, procedural texture,
yet modeling, solid texture, texture synthesis, spectral
and won an Academy award
analysis, power spectrum estimation
is a limited resource, and quality often has to be sacrificed for
With the comparably recent introduction of programmaCategories and Subject Descriptors (according to
CCS): I.3.3 [Computer Graphics]: Picture/Image
ble shading in GPU architectures, hardware accelerated ACMperformance. However, recent developments have given us
Figure 1: Multi-scale assemblage straightforward and demassive computing power sparse convolution.
Generation—I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, It level
procedural shading is now very is a random pattern generation process generalizingeven on typical consumerallows users




Thanks

Thanks for your attention!
Michel Alves dos Santos - michel.mas@gmail.com
Michel Alves dos Santos - (Alves, M.)
MSc Candidate at Federal University of Rio de Janeiro.

E-mail: michel.mas@gmail.com, malves@cos.ufrj.br
Lattes: http://lattes.cnpq.br/7295977425362370
Home: http://www.michelalves.com
Phone: +55 21 2562 8572 (Institutional Phone Number)

http://www.facebook.com/michel.alves.santos
http://www.linkedin.com/proﬁle/view?id=26542507


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