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  1. Article ; Online: Sym3DNet: Symmetric 3D Prior Network for Single-View 3D Reconstruction.

    Siddique, Ashraf / Lee, Seungkyu

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 2

    Abstract: The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of ...

    Abstract The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of an object. In this work, we propose Sym3DNet for single-view 3D reconstruction, which employs a three-dimensional reflection symmetry structure prior of an object. More specifically, Sym3DNet includes 2D-to-3D encoder-decoder networks followed by a symmetry fusion step and multi-level perceptual loss. The symmetry fusion step builds flipped and overlapped 3D shapes that are fed to a 3D shape encoder to calculate the multi-level perceptual loss. Perceptual loss calculated in different feature spaces counts on not only voxel-wise shape symmetry but also on the overall global symmetry shape of an object. Experimental evaluations are conducted on both large-scale synthetic 3D data (ShapeNet) and real-world 3D data (Pix3D). The proposed method outperforms state-of-the-art approaches in terms of efficiency and accuracy on both synthetic and real-world datasets. To demonstrate the generalization ability of our approach, we conduct an experiment with unseen category samples of ShapeNet, exhibiting promising reconstruction results as well.
    MeSH term(s) Imaging, Three-Dimensional
    Language English
    Publishing date 2022-01-11
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22020518
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Internal-External Boundary Attention Fusion for Glass Surface Segmentation

    Han, Dongshen / Lee, Seungkyu

    2023  

    Abstract: Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions ... ...

    Abstract Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions from a single-color image is a challenging task. Recent deep-learning approaches have paid attention to the description of glass surface boundary where the transition of visual appearances between glass and non-glass surfaces are observed. In this work, we analytically investigate how glass surface boundary helps to characterize glass objects. Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image. Our proposed method is evaluated on six public benchmarks comparing with state-of-the-art methods showing promising results.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-06-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Neural Radiance Fields for Transparent Object Using Visual Hull

    Yoon, Heechan / Lee, Seungkyu

    2023  

    Abstract: Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change. Recently introduced ... ...

    Abstract Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change. Recently introduced Neural Radiance Fields (NeRF) is a view synthesis method. Thanks to its remarkable performance improvement, lots of following applications based on NeRF in various topics have been developed. However, if an object with a different refractive index is included in a scene such as transparent object, NeRF shows limited performance because refracted light ray at the surface of the transparent object is not appropriately considered. To resolve the problem, we propose a NeRF-based method consisting of the following three steps: First, we reconstruct a three-dimensional shape of a transparent object using visual hull. Second, we simulate the refraction of the rays inside of the transparent object according to Snell's law. Last, we sample points through refracted rays and put them into NeRF. Experimental evaluation results demonstrate that our method addresses the limitation of conventional NeRF with transparent objects.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-12-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: 'Eye' of the molecule-a viewpoint.

    Lee, Seungkyu / Yaghi, Omar M

    Faraday discussions

    2021  Volume 231, Page(s) 145–149

    Abstract: If the twentieth century was the age of precisely designed molecules, the twenty-first century is beginning to look like the age of reticulated molecules. In the spirit of ... ...

    Abstract If the twentieth century was the age of precisely designed molecules, the twenty-first century is beginning to look like the age of reticulated molecules. In the spirit of the
    Language English
    Publishing date 2021-10-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1364-5498
    ISSN (online) 1364-5498
    DOI 10.1039/d1fd00032b
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Pharmacological Inhibition of Voltage-gated Ca(2+) Channels for Chronic Pain Relief.

    Lee, Seungkyu

    Current neuropharmacology

    2013  Volume 11, Issue 6, Page(s) 606–620

    Abstract: Chronic pain is a major therapeutic problem as the current treatment options are unsatisfactory with low efficacy and deleterious side effects. Voltage-gated Ca2+ channels (VGCCs), which are multi-complex proteins consisting of α1, β, γ, and α2δ subunits, ...

    Abstract Chronic pain is a major therapeutic problem as the current treatment options are unsatisfactory with low efficacy and deleterious side effects. Voltage-gated Ca2+ channels (VGCCs), which are multi-complex proteins consisting of α1, β, γ, and α2δ subunits, play an important role in pain signaling. These channels are involved in neurogenic inflammation, excitability, and neurotransmitter release in nociceptors. It has been previously shown that N-type VGCCs (Cav2.2) are a major pain target. U.S. FDA approval of three Cav2.2 antagonists, gabapentin, pregabalin, and ziconotide, for chronic pain underlies the importance of this channel subtype. Also, there has been increasing evidence that L-type (Cav1.2) or T-type (Cav3.2) VGCCs may be involved in pain signaling and chronic pain. In order to develop novel pain therapeutics and to understand the role of VGCC subtypes, discovering subtype selective VGCC inhibitors or methods that selectively target the inhibitor into nociceptors would be essential. This review describes the various VGCC subtype inhibitors and the potential of utilizing VGCC subtypes as targets of chronic pain. Development of VGCC subtype inhibitors and targeting them into nociceptors will contribute to a better understanding of the roles of VGCC subtypes in pain at a spinal level as well as development of a novel class of analgesics for chronic pain.
    Language English
    Publishing date 2013-12-12
    Publishing country United Arab Emirates
    Document type Journal Article
    ZDB-ID 2192352-8
    ISSN 1875-6190 ; 1570-159X
    ISSN (online) 1875-6190
    ISSN 1570-159X
    DOI 10.2174/1570159X11311060005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6150: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  6. Book ; Online: Unrealistic Feature Suppression for Generative Adversarial Networks

    Kim, Sanghun / Lee, SeungKyu

    2021  

    Abstract: Due to the unstable nature of minimax game between generator and discriminator, improving the performance of GANs is a challenging task. Recent studies have shown that selected high-quality samples in training improve the performance of GANs. However, ... ...

    Abstract Due to the unstable nature of minimax game between generator and discriminator, improving the performance of GANs is a challenging task. Recent studies have shown that selected high-quality samples in training improve the performance of GANs. However, sampling approaches which discard samples show limitations in some aspects such as the speed of training and optimality of the networks. In this paper we propose unrealistic feature suppression (UFS) module that keeps high-quality features and suppresses unrealistic features. UFS module keeps the training stability of networks and improves the quality of generated images. We demonstrate the effectiveness of UFS module on various models such as WGAN-GP, SNGAN, and BigGAN. By using UFS module, we achieved better Frechet inception distance and inception score compared to various baseline models. We also visualize how effectively our UFS module suppresses unrealistic features through class activation maps.

    Comment: 8 pages, 10 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2021-07-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Continual Learning with Neuron Activation Importance

    Kim, Sohee / Lee, Seungkyu

    2021  

    Abstract: Continual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. ...

    Abstract Continual learning is a concept of online learning with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. In this paper, we propose a neuron activation importance-based regularization method for stable continual learning regardless of the order of tasks. We conduct comprehensive experiments on existing benchmark data sets to evaluate not just the stability and plasticity of our method with improved classification accuracy also the robustness of the performance along the changes of task order.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2021-07-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Mode Penalty Generative Adversarial Network with adapted Auto-encoder

    Lee, Gahye / Lee, Seungkyu

    2020  

    Abstract: Generative Adversarial Networks (GAN) are trained to generate sample images of interest distribution. To this end, generator network of GAN learns implicit distribution of real data set from the classification with candidate generated samples. Recently, ... ...

    Abstract Generative Adversarial Networks (GAN) are trained to generate sample images of interest distribution. To this end, generator network of GAN learns implicit distribution of real data set from the classification with candidate generated samples. Recently, various GANs have suggested novel ideas for stable optimizing of its networks. However, in real implementation, sometimes they still represent a only narrow part of true distribution or fail to converge. We assume this ill posed problem comes from poor gradient from objective function of discriminator, which easily trap the generator in a bad situation. To address this problem, we propose a mode penalty GAN combined with pre-trained auto encoder for explicit representation of generated and real data samples in the encoded space. In this space, we make a generator manifold to follow a real manifold by finding entire modes of target distribution. In addition, penalty for uncovered modes of target distribution is given to the generator which encourages it to find overall target distribution. We demonstrate that applying the proposed method to GANs helps generator's optimization becoming more stable and having faster convergence through experimental evaluations.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 006
    Publishing date 2020-11-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Small Noisy and Perspective Face Detection using Deformable Symmetric Gabor Wavelet Network

    Salokhiddinov, Sherzod / Lee, Seungkyu

    2020  

    Abstract: Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization. Moreover, facial expression gives additional distortion on this small and noisy face. In this paper, we ... ...

    Abstract Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization. Moreover, facial expression gives additional distortion on this small and noisy face. In this paper, we propose deformable symmetric Gabor wavelet network face model for face detection in low resolution image. Our model optimizes the rotation, translation, dilation, perspective and partial deformation amount of the face model with symmetry constraints. Symmetry constraints help our model to be more robust to noise and distortion. Experimental results on our low resolution face image dataset and videos show promising face detection and tracking results under various challenging conditions.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Publishing date 2020-10-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Sub-clusters of Normal Data for Anomaly Detection

    Lee, Gahye / Lee, Seungkyu

    2020  

    Abstract: Anomaly detection in data analysis is an interesting but still challenging research topic in real world applications. As the complexity of data dimension increases, it requires to understand the semantic contexts in its description for effective anomaly ... ...

    Abstract Anomaly detection in data analysis is an interesting but still challenging research topic in real world applications. As the complexity of data dimension increases, it requires to understand the semantic contexts in its description for effective anomaly characterization. However, existing anomaly detection methods show limited performances with high dimensional data such as ImageNet. Existing studies have evaluated their performance on low dimensional, clean and well separated data set such as MNIST and CIFAR-10. In this paper, we study anomaly detection with high dimensional and complex normal data. Our observation is that, in general, anomaly data is defined by semantically explainable features which are able to be used in defining semantic sub-clusters of normal data as well. We hypothesize that if there exists reasonably good feature space semantically separating sub-clusters of given normal data, unseen anomaly also can be well distinguished in the space from the normal data. We propose to perform semantic clustering on given normal data and train a classifier to learn the discriminative feature space where anomaly detection is finally performed. Based on our careful and extensive experimental evaluations with MNIST, CIFAR-10, and ImageNet with various combinations of normal and anomaly data, we show that our anomaly detection scheme outperforms state of the art methods especially with high dimensional real world images.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2020-11-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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