148 research outputs found
Minimizing Quotient Regularization Model
Quotient regularization models (QRMs) are a class of powerful regularization
techniques that have gained considerable attention in recent years, due to
their ability to handle complex and highly nonlinear data sets. However, the
nonconvex nature of QRM poses a significant challenge in finding its optimal
solution. We are interested in scenarios where both the numerator and the
denominator of QRM are absolutely one-homogeneous functions, which is widely
applicable in the fields of signal processing and image processing. In this
paper, we utilize a gradient flow to minimize such QRM in combination with a
quadratic data fidelity term. Our scheme involves solving a convex problem
iteratively.The convergence analysis is conducted on a modified scheme in a
continuous formulation, showing the convergence to a stationary point.
Numerical experiments demonstrate the effectiveness of the proposed algorithm
in terms of accuracy, outperforming the state-of-the-art QRM solvers.Comment: 20 page
Rank-based Decomposable Losses in Machine Learning: A Survey
Recent works have revealed an essential paradigm in designing loss functions
that differentiate individual losses vs. aggregate losses. The individual loss
measures the quality of the model on a sample, while the aggregate loss
combines individual losses/scores over each training sample. Both have a common
procedure that aggregates a set of individual values to a single numerical
value. The ranking order reflects the most fundamental relation among
individual values in designing losses. In addition, decomposability, in which a
loss can be decomposed into an ensemble of individual terms, becomes a
significant property of organizing losses/scores. This survey provides a
systematic and comprehensive review of rank-based decomposable losses in
machine learning. Specifically, we provide a new taxonomy of loss functions
that follows the perspectives of aggregate loss and individual loss. We
identify the aggregator to form such losses, which are examples of set
functions. We organize the rank-based decomposable losses into eight
categories. Following these categories, we review the literature on rank-based
aggregate losses and rank-based individual losses. We describe general formulas
for these losses and connect them with existing research topics. We also
suggest future research directions spanning unexplored, remaining, and emerging
issues in rank-based decomposable losses.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling
In this thesis, we focus on the image labeling problem which is the task of performing unique
pixel-wise label decisions to simplify the image while reducing its redundant information. We
build upon a recently introduced geometric approach for data labeling by assignment flows
[
APSS17
] that comprises a smooth dynamical system for data processing on weighted graphs.
Hereby we pursue two lines of research that give new application and theoretically-oriented
insights on the underlying segmentation task.
We demonstrate using the example of Optical Coherence Tomography (OCT), which is the
mostly used non-invasive acquisition method of large volumetric scans of human retinal tis-
sues, how incorporation of constraints on the geometry of statistical manifold results in a novel
purely data driven
geometric
approach for order-constrained segmentation of volumetric data
in any metric space. In particular, making diagnostic analysis for human eye diseases requires
decisive information in form of exact measurement of retinal layer thicknesses that has be done
for each patient separately resulting in an demanding and time consuming task. To ease the
clinical diagnosis we will introduce a fully automated segmentation algorithm that comes up
with a high segmentation accuracy and a high level of built-in-parallelism. As opposed to many
established retinal layer segmentation methods, we use only local information as input without
incorporation of additional global shape priors. Instead, we achieve physiological order of reti-
nal cell layers and membranes including a new formulation of ordered pair of distributions in an
smoothed energy term. This systematically avoids bias pertaining to global shape and is hence
suited for the detection of anatomical changes of retinal tissue structure. To access the perfor-
mance of our approach we compare two different choices of features on a data set of manually
annotated
3
D OCT volumes of healthy human retina and evaluate our method against state of
the art in automatic retinal layer segmentation as well as to manually annotated ground truth
data using different metrics.
We generalize the recent work [
SS21
] on a variational perspective on assignment flows and
introduce a novel nonlocal partial difference equation (G-PDE) for labeling metric data on graphs.
The G-PDE is derived as nonlocal reparametrization of the assignment flow approach that was
introduced in
J. Math. Imaging & Vision
58(2), 2017. Due to this parameterization, solving the
G-PDE numerically is shown to be equivalent to computing the Riemannian gradient flow with re-
spect to a nonconvex potential. We devise an entropy-regularized difference-of-convex-functions
(DC) decomposition of this potential and show that the basic geometric Euler scheme for inte-
grating the assignment flow is equivalent to solving the G-PDE by an established DC program-
ming scheme. Moreover, the viewpoint of geometric integration reveals a basic way to exploit
higher-order information of the vector field that drives the assignment flow, in order to devise a
novel accelerated DC programming scheme. A detailed convergence analysis of both numerical
schemes is provided and illustrated by numerical experiments
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, several strategies have been exploited, including
model reduction, warm start and removal of inactive constraints.
We analyze the performance of the proposed method under different settings,
and demonstrate that our method either outperforms or performs on par with
state-of-the-art approaches. Especially when the connectivities are dense or
when the relative magnitudes of the unary costs are low, we achieve the best
reported results. Experiments show that the proposed algorithm achieves better
approximation than the state-of-the-art methods within a variety of time
budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page
Discrete Tomography by Convex-Concave Regularization using Linear and Quadratic Optimization
Discrete tomography concerns the reconstruction of objects that are made up from a few different materials, each of which comprising a homogeneous density distribution. Under the assumption that these densities are a priori known new algorithms can be developed which typically need less projection data to reveal appealing reconstruction results
Courbure discrète : théorie et applications
International audienceThe present volume contains the proceedings of the 2013 Meeting on discrete curvature, held at CIRM, Luminy, France. The aim of this meeting was to bring together researchers from various backgrounds, ranging from mathematics to computer science, with a focus on both theory and applications. With 27 invited talks and 8 posters, the conference attracted 70 researchers from all over the world. The challenge of finding a common ground on the topic of discrete curvature was met with success, and these proceedings are a testimony of this wor
Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently
gained a prominent role in many industries, being widely used not only among enthusiastic
consumers but also in high demanding professional situations, and will have a
massive societal impact over the coming years. However, the operation of UAVs is full
of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or
randomly thrown objects). These collision scenarios are complex to analyze in real-time,
sometimes being computationally impossible to solve with existing State of the Art (SoA)
algorithms, making the use of UAVs an operational hazard and therefore significantly reducing
their commercial applicability in urban environments. In this work, a conceptual
framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on
the architectural requirements of the collision avoidance subsystem to achieve acceptable
levels of safety and reliability. First, the SoA principles for collision avoidance against
stationary objects are reviewed. Afterward, a novel image processing approach that uses
deep learning and optical flow is presented. This approach is capable of detecting and
generating escape trajectories against potential collisions with dynamic objects. Finally,
novel models and algorithms combinations were tested, providing a new approach for
the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed
approach was demonstrated through experimental tests using a UAV, created from
scratch using the framework developed.Os veÃculos aéreos não tripulados (VANTs), embora dificilmente considerados uma
nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias,
sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais
de alta exigência, sendo expectável um impacto social massivo nos próximos
anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como
colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados).
Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente
impossÃvel de resolver com os algoritmos existentes, tornando o uso de
VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade
comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual
para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos
do subsistema de prevenção de colisão para atingir nÃveis aceitáveis de segurança e confiabilidade.
Os estudos presentes na literatura para prevenção de colisão contra objectos
estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas
de aprendizagem profunda e processamento de imagem, para realizar a prevenção de
colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos
são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs
usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através
de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura
apresentada
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