- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Change Detection in Semantic Level for SAR Images
摘要： Considering that the traditional change detection algorithms only focus on extracting the change area but ignore the change content identification, a novel change detection framework for synthetic aperture radar (SAR) images is proposed. The framework integrates the merits of unsupervised and supervised learning to detect changes in semantic level. First, the residual convolutional auto-encoder (RCAE) is designed to convert SAR image slices to the histogram representation. Then, we calculate the difference vectors and extract the change area by their norms. Finally, we classify the difference vectors of change region and identify the content of change. Experimental results indicate that the proposed method significantly achieves performance improvement over existing algorithms.
关键词： semantic,bag of visual words,synthetic aperture radar,auto-encoder,change detection
更新于2025-09-23 15:23:52
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Bidirectional Recurrent Auto-Encoder for Photoplethysmogram Denoising
摘要： Photoplethysmography (PPG) has become ubiquitous with the development of smartwatches and the mobile healthcare market. However, PPG is vulnerable to various types of noises which are ever-present in uncontrolled environments, and the key to obtaining meaningful signals depends on successful denoising of PPG. In this context, algorithms have been developed to denoise PPG, but many were validated in controlled settings or are reliant on multiple steps that must all work correctly. This paper proposes a novel PPG denoising algorithm based on bidirectional recurrent denoising auto-encoder (BRDAE) which requires minimal pre-processing steps and have the benefit of waveform feature accentuation beyond simple denoising. The BRDAE was trained and validated on a dataset with artificially augmented noise, and was tested on a large open-database of PPG signals collected from patients enrolled in intensive care units (ICUs) as well as from PPG data collected intermittently during the daily routine of 9 subjects over 24-hours. Denoising with the trained BRDAE improved signal-to-noise ratio of the noise-augmented data by 7.9dB during validation. In the test datasets, the denoised PPG showed statistically significant improvement in heart rate detection as compared to the original PPG in terms of correlation to reference and root-mean-squared error. These results indicate that the proposed method is an effective solution for denoising the PPG signal, and promises values beyond traditional denoising by providing PPG feature accentuation for pulse waveform analysis.
关键词： auto-encoder (AE),denoising,recurrent neural networks (RNN),photoplethysmography (PPG)
更新于2025-09-23 15:23:52
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An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images
摘要： Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.
关键词： auto-encoder,regularized extreme learning machine,security,HOG-SGF feature extraction,visible and multispectral palmprint images
更新于2025-09-23 15:22:29
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Enhancing the Reliability of Protection Scheme for PV Integrated Microgrid by Discriminating between Array Faults and Symmetrical Line Faults using Sparse Auto Encoder
摘要： The ever increasing power demand and the stress on reducing carbon footprint have paved the way for widespread use of PV integrated microgrid. However, the development of a reliable protection scheme for PV integrated microgrid is challenging because of the similar voltage-current profile of PV array faults and symmetrical line faults. Conventional protection schemes based on pre-defined threshold setting are not able to distinguish between PV array and symmetrical faults, and hence fail to provide separate controlling actions for the two cases. In this regard, a protection scheme based on sparse auto-encoder and deep neural network (SAE-DNN) approach has been proposed to discriminate between array faults and symmetrical line faults in addition to performing the tasks of mode detection, fault detection, classification and section identification. The voltage and current signals retrieved from relaying buses are converted into grayscale image dataset, which is fed as input to the SAE to perform the unsupervised feature learning. The performance of proposed scheme has been evaluated through reliability analysis and compared with ANN, SVM and DT based techniques under both islanding and grid-connected mode of the microgrid. The scheme has been also validated for field applications by performing real-time simulations on OPAL-RT digital simulator.
关键词： sparse auto-encoder,classification,deep neural network,PV integrated microgrid,section identification,protection scheme,fault detection,OPAL-RT digital simulator
更新于2025-09-23 15:21:01
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Micro-cracks detection of solar cells surface via combining short-term and long-term deep features
摘要： The machine vision based methods for micro-cracks detection of solar cells surface have become one of the main research directions with its efficiency and convenience. The existed methods are roughly classified into two categories: current viewing information based methods, prior knowledge based methods, however, the former usually adopt hand-designed features with poor generality and lacks the guidance of prior knowledge, the latter are usually implemented through the machine learning, and the generalization ability is also limited since the large-scale annotation dataset is scarce. To resolve above problems, a novel micro-cracks detection method via combining short-term and long-term deep features is proposed in this paper. The short-term deep features which represent the current viewing information are learned from the input image itself through stacked denoising auto encoder (SDAE), the long-term deep features which represent the prior knowledge are learned from a large number of natural scene images that people often see through convolutional neural networks (CNNs). The subjective and objective evaluations demonstrate that: 1) the performance of combing the short-term and long-term deep features is better than any of them alone, 2) the performance of proposed method is superior to the shallow learning based methods, 3) the proposed method can effectively detect various kinds of micro-cracks.
关键词： solar cell,stacked denoising auto encoder,long-term,convolutional neural networks,short-term,micro-cracks detection
更新于2025-09-23 15:19:57
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Polarization Feature Extraction Using Quaternion Neural Networks for Flexible Unsupervised Polsar Land Classification
摘要： We propose an unsupervised PolSAR land classification system consisting of quaternion auto-encoder and quaternion self-organizing map (SOM). Most of the conventional methods extract features necessary for the land classification based on a few of scattering models predefined by human beings. However, we cannot expect classification into a large number of land categories recognizable to humans by using such restricted features. In this paper, we propose a method employing quaternion auto-encoder and quaternion SOM for feature extraction and classification, respectively. As a result, we succeed in discovering new and more detailed land categories. For example, town areas are divided into residential areas and factory sites.
关键词： Poincare parameter,quaternion neural network,auto-encoder,unsupervised classification,Polarimetric synthetic aperture radar (PolSAR),self-organizing map (SOM),land classification
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Unfeatured Weld Positioning Technology Based on Neural Network and Machine Vision
摘要： In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.
关键词： weld positioning,machine vision,neural networks,featureless image,convolutional auto-encoder
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Auto-Encoder Network for Hyperspectral Image Unmixing
摘要： In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder network composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a variational auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic dataset. In our comparison with other state-of-the-art unmixing methods, the proposed approach demonstrates highly competitive performance.
关键词： Variational auto-encoder,Hyperspectral unmixing,Non-negative sparse auto-encoder,Deep learning
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11257 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II) || Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image
摘要： Human body detection is a key technology in the ?elds of biometric recognition, and the detection in a depth image is rather challenging due to serious noise e?ects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Speci?cally, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and signi?cantly alleviates the in?uences of noise and corruptions on body detection.
关键词： Feature visualization,Sparse auto-encoder,Convolutional neural network,Depth image,Human detection
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11257 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II) || Deep Supervised Auto-encoder Hashing for Image Retrieval
摘要： Image hashing approaches map high dimensional images to compact binary codes that preserve similarities among images. Although the image label is important information for supervised image hashing methods to generate hashing codes, the retrieval performance will be limited according to the performance of the classi?er. Therefore, an e?ective supervised auto-encoder hashing method (SAEH) is proposed to generate low dimensional binary codes in a point-wise manner through deep convolutional neural network. The auto-encoder structure in SAEH is designed to simultaneously learn image features and generate hashing codes. Moreover, some extra relaxations for generating binary hash codes are added to the objective function. The extensive experiments on several large scale image datasets validate that the auto-encoder structure can indeed increase the performance for supervised hashing and SAEH can achieve the best image retrieval results among other prominent supervised hashing methods.
关键词： Image hashing,Image retrieval,Supervised learning,Deep neural network,Convolutional auto-encoder
更新于2025-09-04 15:30:14