986 research outputs found
Convexity in source separation: Models, geometry, and algorithms
Source separation or demixing is the process of extracting multiple
components entangled within a signal. Contemporary signal processing presents a
host of difficult source separation problems, from interference cancellation to
background subtraction, blind deconvolution, and even dictionary learning.
Despite the recent progress in each of these applications, advances in
high-throughput sensor technology place demixing algorithms under pressure to
accommodate extremely high-dimensional signals, separate an ever larger number
of sources, and cope with more sophisticated signal and mixing models. These
difficulties are exacerbated by the need for real-time action in automated
decision-making systems.
Recent advances in convex optimization provide a simple framework for
efficiently solving numerous difficult demixing problems. This article provides
an overview of the emerging field, explains the theory that governs the
underlying procedures, and surveys algorithms that solve them efficiently. We
aim to equip practitioners with a toolkit for constructing their own demixing
algorithms that work, as well as concrete intuition for why they work
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
In remote sensing images, the presence of thick cloud accompanying cloud
shadow is a high probability event, which can affect the quality of subsequent
processing and limit the scenarios of application. Hence, removing the thick
cloud and cloud shadow as well as recovering the cloud-contaminated pixels is
indispensable to make good use of remote sensing images. In this paper, a novel
thick cloud removal method for remote sensing images based on temporal
smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
The basic idea of TSSTO is that the thick cloud and cloud shadow are not only
sparse but also smooth along the horizontal and vertical direction in images
while the clean images are smooth along the temporal direction between images.
Therefore, the sparsity norm is used to boost the sparsity of the cloud and
cloud shadow, and unidirectional total variation (UTV) regularizers are applied
to ensure the unidirectional smoothness. This paper utilizes alternation
direction method of multipliers to solve the presented model and generate the
cloud and cloud shadow element as well as the clean element. The cloud and
cloud shadow element is purified to get the cloud area and cloud shadow area.
Then, the clean area of the original cloud-contaminated images is replaced to
the corresponding area of the clean element. Finally, the reference image is
selected to reconstruct details of the cloud area and cloud shadow area using
the information cloning method. A series of experiments are conducted both on
simulated and real cloud-contaminated images from different sensors and with
different resolutions, and the results demonstrate the potential of the
proposed TSSTO method for removing cloud and cloud shadow from both qualitative
and quantitative viewpoints
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
ν° κ·Έλν μμμμ κ°μΈνλ νμ΄μ§ λν¬μ λν λΉ λ₯Έ κ³μ° κΈ°λ²
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2020. 8. μ΄μꡬ.Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. Because the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thus, severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We improve the convergence rate of traditional Power Iteration method by adopting successive over-relaxation, and initial guess revision, a vector reuse strategy. The proposed method vastly improves on the traditional Power Iteration in terms of convergence rate and computation time, while retaining its simplicity and strictness. Since it can reuse the previously computed vectors for refreshing PPR vectors, its update performance is also greatly enhanced. Also, since the algorithm halts as soon as it reaches a given error threshold, we can flexibly control the trade-off between accuracy and time, a feature lacking in both sampling-based approximation methods and fully exact methods. Experiments show that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms.κ·Έλν
λ΄μμ κ°μΈνλ νμ΄μ§λν¬ (P ersonalized P age R ank, PPR λ₯Ό κ³μ°νλ κ²μ κ²μ , μΆμ² , μ§μλ°κ²¬ λ± μ¬λ¬ λΆμΌμμ κ΄λ²μνκ² νμ©λλ μ€μν μμ
μ΄λ€ . κ°μΈνλ νμ΄μ§λν¬λ₯Ό κ³μ°νλ κ²μ κ³ λΉμ©μ κ³Όμ μ΄ νμνλ―λ‘ , κ°μΈνλ νμ΄μ§λν¬λ₯Ό κ³μ°νλ ν¨μ¨μ μ΄κ³ νμ μ μΈ λ°©λ²λ€μ΄ λ€μ κ°λ°λμ΄μλ€ . κ·Έλ¬λ μλ°±λ§ μ΄μμ λ
Έλλ₯Ό κ°μ§ λμ©λ κ·Έλνμ λν ν¨μ¨μ μΈ κ³μ°μ μ¬μ ν ν΄κ²°λμ§ μμ λ¬Έμ μ΄λ€ . κ·Έμ λνμ¬ , κΈ°μ‘΄ μ μλ μκ³ λ¦¬λ¬λ€μ κ·Έλν κ°±μ μ ν¨μ¨μ μΌλ‘ λ€λ£¨μ§ λͺ»νμ¬ λμ μΌλ‘ λ³ννλ κ·Έλνλ₯Ό λ€λ£¨λ λ°μ νκ³μ μ΄ ν¬λ€ . λ³Έ μ°κ΅¬μμλ λμ μ λ°λλ₯Ό 보μ₯νκ³ μ λ°λλ₯Ό ν΅μ κ°λ₯ν , λΉ λ₯΄κ² μλ ΄νλ κ°μΈνλ νμ΄μ§λν¬ κ³μ° μκ³ λ¦¬λ¬μ μ μνλ€ . μ ν΅μ μΈ κ±°λμ κ³±λ² (Power μ μΆμ°¨κ°μμνλ² (Successive Over Relaxation) κ³Ό μ΄κΈ° μΆμΈ‘ κ° λ³΄μ λ² (Initial Guess μ νμ©ν λ²‘ν° μ¬μ¬μ© μ λ΅μ μ μ©νμ¬ μλ ΄ μλλ₯Ό κ°μ νμλ€ . μ μλ λ°©λ²μ κΈ°μ‘΄ κ±°λμ κ³±λ²μ μ₯μ μΈ λ¨μμ±κ³Ό μλ°μ±μ μ μ§ νλ©΄μ λ μλ ΄μ¨κ³Ό κ³μ°μλλ₯Ό ν¬κ² κ°μ νλ€ . λν κ°μΈνλ νμ΄μ§λν¬ λ²‘ν°μ κ°±μ μ μνμ¬ μ΄μ μ κ³μ° λμ΄ μ μ₯λ 벑ν°λ₯Ό μ¬μ¬μ©ν μ¬ , κ°±μ μ λλ μκ°μ΄ ν¬κ² λ¨μΆλλ€ . λ³Έ λ°©λ²μ μ£Όμ΄μ§ μ€μ°¨ νκ³μ λλ¬νλ μ¦μ κ²°κ³Όκ°μ μ°μΆνλ―λ‘ μ νλμ κ³μ°μκ°μ μ μ°νκ² μ‘°μ ν μ μμΌλ©° μ΄λ νλ³Έ κΈ°λ° μΆμ λ°©λ²μ΄λ μ νν κ°μ μ°μΆνλ μνλ ¬ κΈ°λ° λ°©λ² μ΄ κ°μ§μ§ λͺ»ν νΉμ±μ΄λ€ . μ€ν κ²°κ³Ό , λ³Έ λ°©λ²μ κ±°λμ κ³±λ²μ λΉνμ¬ 20 λ°° μ΄μ λΉ λ₯΄κ² μλ ΄νλ€λ κ²μ΄ νμΈλμμΌλ©° , κΈ° μ μλ μ΅κ³ μ±λ₯ μ μκ³ λ¦¬ λ¬ λ³΄λ€ μ°μν μ±λ₯μ 보μ΄λ κ² λν νμΈλμλ€1 Introduction 1
2 Preliminaries: Personalized PageRank 4
2.1 Random Walk, PageRank, and Personalized PageRank. 5
2.1.1 Basics on Random Walk 5
2.1.2 PageRank. 6
2.1.3 Personalized PageRank 8
2.2 Characteristics of Personalized PageRank. 9
2.3 Applications of Personalized PageRank. 12
2.4 Previous Work on Personalized PageRank Computation. 17
2.4.1 Basic Algorithms 17
2.4.2 Enhanced Power Iteration 18
2.4.3 Bookmark Coloring Algorithm. 20
2.4.4 Dynamic Programming 21
2.4.5 Monte-Carlo Sampling. 22
2.4.6 Enhanced Direct Solving 24
2.5 Summary 26
3 Personalized PageRank Computation with Initial Guess Revision 30
3.1 Initial Guess Revision and Relaxation 30
3.2 Finding Optimal Weight of Successive Over Relaxation for PPR. 34
3.3 Initial Guess Construction Algorithm for Personalized PageRank. 36
4 Fully Personalized PageRank Algorithm with Initial Guess Revision 42
4.1 FPPR with IGR. 42
4.2 Optimization. 49
4.3 Experiments. 52
5 Personalized PageRank Query Processing with Initial Guess Revision 56
5.1 PPR Query Processing with IGR 56
5.2 Optimization. 64
5.3 Experiments. 67
6 Conclusion 74
Bibliography 77
Appendix 88
Abstract (In Korean) 90Docto
Data-driven shape analysis and processing
Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing
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