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过滤筛选
- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
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[IEEE 2018 International Conference on 3D Vision (3DV) - Verona (2018.9.5-2018.9.8)] 2018 International Conference on 3D Vision (3DV) - Learning Material-Aware Local Descriptors for 3D Shapes
摘要： Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material-aware descriptors from view-based representations of 3D points for point-wise material classification or material-aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical materials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variability. In addition, we also contribute a high-quality expert-labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware conditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predictions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation.
关键词： material-aware retrieval,material-aware descriptors,3D shapes,convolutional neural network,material classification
更新于2025-09-04 15:30:14
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Image Exploration Procedure Classification with Spike-timing Neural Network for the Blind
摘要： Individuals who are blind use exploration procedures (EPs) to navigate and understand digital images. The ability to model and detect these EPs can help the assistive technologies’ community build efficient and accessible interfaces for the blind and overall enhance human-machine interaction. In this paper, we propose a framework to classify various EPs using spike-timing neural networks (SNNs). While users interact with a digital image using a haptic device, rotation and translation-invariant features are computed directly from exploration trajectories acquired from the haptic control. These features are further encoded as model strings through trained SNNs. A classification scheme is then proposed to distinguish these model strings to identify the EPs. The framework adapted a modified Dynamic Time Wrapping (DTW) for spatial-temporal matching with Dempster-Shafer Theory (DST) for multimodal fusion. Experimental results (87.05% as EPs’ detection accuracy) indicate the effectiveness of the proposed framework and its potential application in human-machine interfaces.
关键词： Dempster-Shafer Theory,Spike-timing Neural Network,Spatio-temporal Pattern,Exploration Procedures,Haptic-based Interface,Blind Community
更新于2025-09-04 15:30:14
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Efficient Variable Rate Image Compression with Multi-scale Decomposition Network
摘要： While deep learning image compression methods have shown impressive coding performance, most of them output a single optimized compression rate using a trained specific network. However, in practical it is essential to support variable rate compression or meet a target rate with high coding performance. This paper proposes a novel image compression method, making it possible for a single CNN model to generate variable rate efficiently with optimized rate-distortion (RD) performance. The method consists of CNN based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality-factor. For a target rate, the allocation is adaptive based on content complexity. And for a target quality-factor which indicates a trade-off between rate and quality, the optimal scale is determined by minimizing the RD cost. Experimental results have shown that our method has outperformed JPEG2000 and BPG standards with high efficiency and state-of-the-art RD performance as measured by MS-SSIM. Moreover, our method can strictly control the rate to generate the target compression result.
关键词： Convolutional neural network,Multi-scale decomposition transform,Content adaptive rate allocation,Lossy image compression,Variable rate image compression
更新于2025-09-04 15:30:14
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Algorithm for Processing and Analysis of Raman Spectra using Neural Networks
摘要： The solution of the problem of processing of a large data set when analyzing Raman spectra of a gas mixture is considered. The algorithm is based on the artificial neural network. Conditions for the use of neural networks in solving practical problems of real-time analyzing spectra, including that for remote search for heavy hydrocarbons are determined. The algorithm speed is estimated using computer aids with sequential and parallel data processing.
关键词： data processing,Raman spectra,parallel computing,neural network,software,gas mixture
更新于2025-09-04 15:30:14