314,256 research outputs found
Adapted active appearance models
Active Appearance Models (AAM) are able to align known faces in an efficient manner when face pose and illumination
are controlled. The AAM exploit a set of face examples in order to extract a statistical model. There is no difficulty to
align a face with the same type (same morphology, illumination and pose) which constitute the example data set.
Unfortunately, the AAM are less outstanding from the moment when the illumination, pose and face type changes.
AAM robustness is link to the variability introduced in the learning base. The more the AAM will contain variability, the
more it will be able to adapt itself to variable faces with the following drawback : the data represented in the reduced
parameters space will then form different classes letting appear holes, regions without any data (see Fig. 1). It is
therefore very difficult to make the AAM converge in this scattered space.
We propose in this paper a robust Active Appearance Models allowing a real-time implementation. To increase the AAM
robustness to illumination changes, we propose Oriented Map AAM (OM-AAM). Adapted AAM will be presented after to
increase the AAM robustness to any other types of variability (in identity, pose, expression etc.)...Les ModĂšles Actifs dâApparence (MAA) sont efficaces lorsquâil sâagit dâaligner (dĂ©tecter les contours des yeux,
du nez et de la bouche) des visages connus dans des espaces contraints (illumination et pose contrÎlées).
Nous proposons des ModĂšles Actifs dâApparence AdaptĂ©s afin dâaligner des visages inconnus dans des poses
et illuminations quelconques. Notre proposition repose dâune part sur une transformation des textures du
modĂšle actif en carte dâorientation, ce qui impacte lâopĂ©ration de normalisation des MAA ; et dâautre part sur
une recherche dans une banque de modÚles pré-calculés du MAA le plus adapté au visage inconnu.
Des tests sur des bases publiques et privĂ©es (BioId, CMU-PIE) montrent lâintĂ©rĂȘt de notre approche: il devient
possible dâaligner en temps rĂ©el des visages inconnus dans des situations oĂč la lumiĂšre et la pose sont non
contrÎlées
Active skeleton for bacteria modeling
The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical
Engineering: Imaging and Visualizationto appear i
Who am I talking with? A face memory for social robots
In order to provide personalized services and to
develop human-like interaction capabilities robots need to rec-
ognize their human partner. Face recognition has been studied
in the past decade exhaustively in the context of security systems
and with significant progress on huge datasets. However, these
capabilities are not in focus when it comes to social interaction
situations. Humans are able to remember people seen for a
short moment in time and apply this knowledge directly in
their engagement in conversation. In order to equip a robot with
capabilities to recall human interlocutors and to provide user-
aware services, we adopt human-human interaction schemes to
propose a face memory on the basis of active appearance models
integrated with the active memory architecture. This paper
presents the concept of the interactive face memory, the applied
recognition algorithms, and their embedding into the robotâs
system architecture. Performance measures are discussed for
general face databases as well as scenario-specific datasets
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
The accurate segmentation and tracking of cells in microscopy image sequences
is an important task in biomedical research, e.g., for studying the development
of tissues, organs or entire organisms. However, the segmentation of touching
cells in images with a low signal-to-noise-ratio is still a challenging
problem. In this paper, we present a method for the segmentation of touching
cells in microscopy images. By using a novel representation of cell borders,
inspired by distance maps, our method is capable to utilize not only touching
cells but also close cells in the training process. Furthermore, this
representation is notably robust to annotation errors and shows promising
results for the segmentation of microscopy images containing in the training
data underrepresented or not included cell types. For the prediction of the
proposed neighbor distances, an adapted U-Net convolutional neural network
(CNN) with two decoder paths is used. In addition, we adapt a graph-based cell
tracking algorithm to evaluate our proposed method on the task of cell
tracking. The adapted tracking algorithm includes a movement estimation in the
cost function to re-link tracks with missing segmentation masks over a short
sequence of frames. Our combined tracking by detection method has proven its
potential in the IEEE ISBI 2020 Cell Tracking Challenge
(http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE
multiple top three rankings including two top performances using a single
segmentation model for the diverse data sets.Comment: 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE
ISBI 2020 Cell Tracking Challeng
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
A morphogenetic crop model for sugar-beet (Beta vulgaris L.)
This paper is the instructions for the proceeding of the International
Symposium on Crop. Sugar beet crop models have rarely taken into account the
morphogenetic process generating plant architecture despite the fact that plant
architectural plasticity plays a key role during growth, especially under
stress conditions. The objective of this paper is to develop this approach by
applying the GreenLab model of plant growth to sugar beet and to study the
potential advantages for applicative purposes. Experiments were conducted with
husbandry practices in 2006. The study of sugar beet development, mostly
phytomer appearance, organ expansion and leaf senescence, allowed us to define
a morphogenetic model of sugar beet growth based on GreenLab. It simulates
organogenesis, biomass production and biomass partitioning. The functional
parameters controlling source-sink relationships during plant growth were
estimated from organ and compartment dry masses, measured at seven different
times, for samples of plants. The fitting results are good, which shows that
the introduced framework is adapted to analyse source-sink dynamics and
shoot-root allocation throughout the season. However, this approach still needs
to be fully validated, particularly among seasons
The symbiotic star CH Cygni. II. The broad Ly alpha emission line explained by shocks
Context. In 1985, at the end of the active phase 1977-1986, a broad (4000
km/s) Ly alpha line appeared in the symbiotic system CH Cygni that had never
been observed previously. Aims. In this work we investigate the origin of this
anomalous broad Ly alpha line. Methods. We suggest a new interpretation of the
broad Ly alpha based on the theory of charge transfer reactions between ambient
hydrogen atoms and post-shock protons at a strong shock front. Results. We have
found that the broad Ly alpha line originated from the blast wave created by
the outburst, while the contemporary optical and UV lines arose from the nebula
downstream of the expanding shock in the colliding wind scenario.Comment: 5 pages, 2 figures, accepted for publication in A&A on 7th April 200
Prostate MR image segmentation using 3D active appearance models
This paper presents a method for automatic segmentation of the prostate from transversal T2-weighted images based on 3D Active Appearance Models (AAM). The algorithm consist of two stages. Firstly, Shape Context based non-rigid surface registration of the manual segmented images is used to obtain the point correspondence between the given training cases. Subsequently, an AAM is used to segment the prostate on 50 training cases. The method is evaluated using a 5-fold cross validation over 5 repetitions. The mean Dice similarity coefficient and 95% Hausdorff distance are 0.78 and 7.32 mm respectively
A Novel BiLevel Paradigm for Image-to-Image Translation
Image-to-image (I2I) translation is a pixel-level mapping that requires a
large number of paired training data and often suffers from the problems of
high diversity and strong category bias in image scenes. In order to tackle
these problems, we propose a novel BiLevel (BiL) learning paradigm that
alternates the learning of two models, respectively at an instance-specific
(IS) and a general-purpose (GP) level. In each scene, the IS model learns to
maintain the specific scene attributes. It is initialized by the GP model that
learns from all the scenes to obtain the generalizable translation knowledge.
This GP initialization gives the IS model an efficient starting point, thus
enabling its fast adaptation to the new scene with scarce training data. We
conduct extensive I2I translation experiments on human face and street view
datasets. Quantitative results validate that our approach can significantly
boost the performance of classical I2I translation models, such as PG2 and
Pix2Pix. Our visualization results show both higher image quality and more
appropriate instance-specific details, e.g., the translated image of a person
looks more like that person in terms of identity
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