8,283 research outputs found
Interactive volumetric segmentation for textile micro-tomography data using wavelets and nonlocal means
This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro-tomography data. We propose a semi-supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three-dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high-resolution micro-tomography images
Optimization-based interactive segmentation interface for multiregion problems.
Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality
κ°μΈν λνν μμ λΆν μκ³ λ¦¬μ¦μ μν μλ μ 보 νμ₯ κΈ°λ²μ λν μ°κ΅¬
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2021. 2. μ΄κ²½λ¬΄.Segmentation of an area corresponding to a desired object in an image is essential
to computer vision problems. This is because most algorithms are performed in
semantic units when interpreting or analyzing images. However, segmenting the
desired object from a given image is an ambiguous issue. The target object varies
depending on user and purpose. To solve this problem, an interactive segmentation
technique has been proposed. In this approach, segmentation was performed in the
desired direction according to interaction with the user. In this case, seed information
provided by the user plays an important role. If the seed provided by a user contain
abundant information, the accuracy of segmentation increases. However, providing
rich seed information places much burden on the users. Therefore, the main goal of
the present study was to obtain satisfactory segmentation results using simple seed
information.
We primarily focused on converting the provided sparse seed information to a rich
state so that accurate segmentation results can be derived. To this end, a minimum
user input was taken and enriched it through various seed enrichment techniques.
A total of three interactive segmentation techniques was proposed based on: (1)
Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type
comprised expansion of area around a seed, generation of new seed in a new position,
and attention to semantic information.
First, in seed expansion, we expanded the scope of the seed. We integrated reliable
pixels around the initial seed into the seed set through an expansion step
composed of two stages. Through the extended seed covering a wider area than the
initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed
generation, we created a seed at a new point, but not around the seed. We trained
the system by imitating the user behavior through providing a new seed point in the
erroneous region. By learning the user's intention, our model could e ciently create
a new seed point. The generated seed helped segmentation and could be used as additional
information for weakly supervised learning. Finally, through seed attention,
we put semantic information in the seed. Unlike the previous models, we integrated
both the segmentation process and seed enrichment process. We reinforced the seed
information by adding semantic information to the seed instead of spatial expansion.
The seed information was enriched through mutual attention with feature maps
generated during the segmentation process.
The proposed models show superiority compared to the existing techniques
through various experiments. To note, even with sparse seed information, our proposed
seed enrichment technique gave by far more accurate segmentation results
than the other existing methods.μμμμ μνλ 물체 μμμ μλΌλ΄λ κ²μ μ»΄ν¨ν° λΉμ λ¬Έμ μμ νμμ μΈ μμμ΄λ€. μμμ ν΄μνκ±°λ λΆμν λ, λλΆλΆμ μκ³ λ¦¬μ¦λ€μ΄ μλ―Έλ‘ μ μΈ λ¨μ κΈ°λ°μΌλ‘ λμνκΈ° λλ¬Έμ΄λ€. κ·Έλ¬λ μμμμ 물체 μμμ λΆν νλ κ²μ λͺ¨νΈν λ¬Έμ μ΄λ€. μ¬μ©μμ λͺ©μ μ λ°λΌ μνλ 물체 μμμ΄ λ¬λΌμ§κΈ° λλ¬Έμ΄λ€. μ΄λ₯Ό ν΄κ²°νκΈ° μν΄ μ¬μ©μμμ κ΅λ₯λ₯Ό ν΅ν΄ μνλ λ°©ν₯μΌλ‘ μμ λΆν μ μ§ννλ λνν μμ λΆν κΈ°λ²μ΄ μ¬μ©λλ€. μ¬κΈ°μ μ¬μ©μκ° μ 곡νλ μλ μ λ³΄κ° μ€μν μν μ νλ€. μ¬μ©μμ μλλ₯Ό λ΄κ³ μλ μλ μ λ³΄κ° μ νν μλ‘ μμ λΆν μ μ νλλ μ¦κ°νκ² λλ€. κ·Έλ¬λ νλΆν μλ μ 보λ₯Ό μ 곡νλ κ²μ μ¬μ©μμκ² λ§μ λΆλ΄μ μ£Όκ² λλ€. κ·Έλ¬λ―λ‘ κ°λ¨ν μλ μ 보λ₯Ό μ¬μ©νμ¬ λ§μ‘±ν λ§ν λΆν κ²°κ³Όλ₯Ό μ»λ κ²μ΄ μ£Όμ λͺ©μ μ΄ λλ€.
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νλ€. μ΄λ κ² νμ₯λ μλλ₯Ό μ¬μ©ν¨μΌλ‘μ¨ μλμ ν¬μν¨κ³Ό λΆκ· νμΌλ‘ μΈν λ¬Έμ λ₯Ό ν΄κ²°ν μ μλ€. λ€μμΌλ‘ μλ μμ±μ κΈ°λ°ν κΈ°λ²μμ μ°λ¦¬λ μλ μ£Όλ³μ΄ μλ μλ‘μ΄ μ§μ μ μλλ₯Ό μμ±νλ€. μ°λ¦¬λ μ€μ°¨κ° λ°μν μμμ μ¬μ©μκ° μλ‘μ΄ μλλ₯Ό μ 곡νλ λμμ λͺ¨λ°©νμ¬ μμ€ν
μ νμ΅νμλ€. μ¬μ©μμ μλλ₯Ό νμ΅ν¨μΌλ‘μ¨ ν¨κ³Όμ μΌλ‘ μλλ₯Ό μμ±ν μ μλ€. μμ±λ μλλ μμ λΆν μ μ νλλ₯Ό λμΌ λΏλ§ μλλΌ μ½μ§λνμ΅μ μν λ°μ΄ν°λ‘μ¨ νμ©λ μ μλ€. λ§μ§λ§μΌλ‘ μλ μ£Όμ μ§μ€μ νμ©ν κΈ°λ²μμ μ°λ¦¬λ μλ―Έλ‘ μ μ 보λ₯Ό μλμ λ΄λλ€. κΈ°μ‘΄μ μ μν κΈ°λ²λ€κ³Ό λ¬λ¦¬ μμ λΆν λμκ³Ό μλ νμ₯ λμμ΄ ν΅ν©λ λͺ¨λΈμ μ μνλ€. μλ μ 보λ μμ λΆν λ€νΈμν¬μ νΉμ§λ§΅κ³Ό μνΈ κ΅λ₯νλ©° κ·Έ μ λ³΄κ° νλΆν΄μ§λ€.
μ μν λͺ¨λΈλ€μ λ€μν μ€νμ ν΅ν΄ κΈ°μ‘΄ κΈ°λ² λλΉ μ°μν μ±λ₯μ κΈ°λ‘νμλ€. νΉν μλκ° λΆμ‘±ν μν©μμ μλ νμ₯ κΈ°λ²λ€μ νλ₯ν λνν μμ λΆν μ±λ₯μ 보μλ€.1 Introduction 1
1.1 Previous Works 2
1.2 Proposed Methods 4
2 Interactive Segmentation with Seed Expansion 9
2.1 Introduction 9
2.2 Proposed Method 12
2.2.1 Background 13
2.2.2 Pyramidal RWR 16
2.2.3 Seed Expansion 19
2.2.4 Re nement with Global Information 24
2.3 Experiments 27
2.3.1 Dataset 27
2.3.2 Implement Details 28
2.3.3 Performance 29
2.3.4 Contribution of Each Part 30
2.3.5 Seed Consistency 31
2.3.6 Running Time 33
2.4 Summary 34
3 Interactive Segmentation with Seed Generation 37
3.1 Introduction 37
3.2 Related Works 40
3.3 Proposed Method 41
3.3.1 System Overview 41
3.3.2 Markov Decision Process 42
3.3.3 Deep Q-Network 46
3.3.4 Model Architecture 47
3.4 Experiments 48
3.4.1 Implement Details 48
3.4.2 Performance 49
3.4.3 Ablation Study 53
3.4.4 Other Datasets 55
3.5 Summary 58
4 Interactive Segmentation with Seed Attention 61
4.1 Introduction 61
4.2 Related Works 64
4.3 Proposed Method 65
4.3.1 Interactive Segmentation Network 65
4.3.2 Bi-directional Seed Attention Module 67
4.4 Experiments 70
4.4.1 Datasets 70
4.4.2 Metrics 70
4.4.3 Implement Details 71
4.4.4 Performance 71
4.4.5 Ablation Study 76
4.4.6 Seed enrichment methods 79
4.5 Summary 82
5 Conclusions 87
5.1 Summary 89
Bibliography 90
κ΅λ¬Έμ΄λ‘ 103Docto
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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