- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE Asian Solid-State Circuits Conference (A-SSCC) - Tainan, Taiwan (2018.11.5-2018.11.7)] 2018 IEEE Asian Solid-State Circuits Conference (A-SSCC) - A 137-μW Area-Efficient Real-Time Gesture Recognition System for Smart Wearable Devices
摘要： Gesture recognition has increasingly become one of the most popular human-machine interaction techniques for smart devices. Existing gesture recognition systems suffer from either excessive power consumption or large size, limiting their applications for ultra-low power IoT and wearable devices. This paper presents an accurate, area-efficient, and ultra-low power real-time gesture recognition system for smart wearable devices. The proposed work utilizes a peak-based gesture classification engine with less memory and a low-resolution and low-power on-chip image sensor for achieving high area efficiency and low power. The feature extraction architecture removes fixed-pattern noises from the low-power on-chip image sensor for accuracy improvement and employs parallelism for recognition speed enhancement. The proposed system requires only 3.2 KB on-chip memory for processing 32x32 pixel data. Measurement results of a test chip fabricated in 65nm CMOS demonstrate that the proposed system consumes 137.0 pW at 0.8 V and 30fps while occupying only 1.78mm2, which achieves the lowest power and smallest area among existing gesture recognition systems.
关键词： system on chip,low power processor,image sensor,wearable devices,gesture recognition,feature extraction
更新于2025-09-23 15:22:29
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[IEEE 2017 International Conference on Computational Science and Computational Intelligence (CSCI) - Las Vegas, NV, USA (2017.12.14-2017.12.16)] 2017 International Conference on Computational Science and Computational Intelligence (CSCI) - Estimation of Illumination Map from Dermoscopy Images for Extracting Differential Structures Using Gabor Local Mesh Patterns
摘要： Melanoma is the most deadly form of skin cancer and its incidence rate is significantly increasing. The design of an assisted diagnosis system for the detection of melanoma is a challenging task involving various steps related to computer vision. Researchers have concluded that the accurate identification of melanoma requires robust preprocessing steps on dermoscopy images including hair removal, illumination correction etc., that can help in a better detection of melanoma. In this paper, we propose a novel illumination correction algorithm followed by robust feature extraction from dermoscopy images, leading to a better identification of cancer. Illumination correction is based on statistical estimation of illumination content in the images, followed by the extraction of differential structures using a combination of Gabor filtering followed by extracting local mesh patterns, which exhibit physiological significance based on various clinical rules for detecting melanoma. Our experiments show that the proposed technique outperforms all the other methods that have been considered in this paper.
关键词： Texture analysis,Optimization,Pattern recognition,Gabor filters,Melanoma
更新于2025-09-23 15:22:29
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Spontaneous Facial Micro-expression Recognition via Deep Convolutional Network
摘要： The automatic recognition of spontaneous facial micro-expressions becomes prevalent as it reveals the actual emotion of humans. However, handcrafted features employed for recognizing micro-expressions are designed for general applications and thus cannot well capture the subtle facial deformations of micro-expressions. To address this problem, we propose an end-to-end deep learning framework to suit the particular needs of micro-expression recognition (MER). In the deep model, recurrent convolutional networks are utilized to learn the representation of subtle changes from image sequences. To guarantee the learning of deep model, we present a temporal jittering procedure to greatly enrich the training samples. Through performing the experiments on three spontaneous micro-expression datasets, i.e., SMIC, CASME, and CASME2, we verify the effectiveness of our proposed MER approach.
关键词： Recurrent Convolutional Networks,Micro-Expression Recognition,Motion Magnification,Temporal Jittering
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - 2D Multi-Band PCA and its Application for Ear Recognition
摘要： Principal Component Analysis (PCA) has been successfully used for many application including ear recognition. However, its performance is limited due to its significant data dependency. This paper presents a two dimensional multi-band PCA (2D-MBPCA) method, which has shown a significantly higher performance to that of the PCA. The proposed method divided the input gray image into a number of images, based on the intensity of its pixels using either a dynamic or predefined equal range of threshold values. PCA is then applied on the resulting set of images to extract their features. The resulting features are used to find the best match. The application of the proposed 2D-MBPCA for ear recognition using two benchmark ear image datasets, shows the merit of the proposed technique to that of the standard PCA.
关键词： ear recognition,histogram equalization,PCA
更新于2025-09-23 15:22:29
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An artificial retina processor for track reconstruction at the LHC crossing rate
摘要： The goal of the INFN-RETINA R&D project is to develop and implement a computational methodology that allows to reconstruct events with a large number (> 100) of charged-particle tracks in pixel and silicon strip detectors at 40 MHz, thus matching the requirements for processing LHC events at the full bunch-crossing frequency. Our approach relies on a parallel pattern-recognition algorithm, dubbed artificial retina, inspired by the early stages of image processing by the brain. In order to demonstrate that a track-processing system based on this algorithm is feasible, we built a sizable prototype of a tracking processor tuned to 3 000 patterns, based on already existing readout boards equipped with Altera Stratix III FPGAs. The detailed geometry and charged-particle activity of a large tracking detector currently in operation are used to assess its performances. We report on the test results with such a prototype.
关键词： track reconstruction,artificial retina,pattern recognition,LHC,FPGA
更新于2025-09-23 15:21:21
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The synthesis of water-soluble phosphate pillar[5]arenes functionalized graphene as a fluorescent probe for sensitive detection of paraquat
摘要： We describe a selective and sensitive fluorescence platform for the detection of paraquat (PQ) based on competitive host–guest recognition between phosphate pillar[5]arenes (PWP5) and probe (Safranine T, ST) with using PWP5 functionalized reduced graphene (PWP5-rGO) as the receptor. PQ is a positive charge molecule that is captured by PWP5 via electrostatic interactions. The host-guest interaction between PWP5 and PQ is studied by 1H NMR. Therefore, a selective and sensitive fluorescence sensing of detection PQ is developed. It has a linear response ranges of 0.01?2.0 and 2.0?50.0 μM and a low detection limit of 0.0035 μM (S/N=3) for PQ. The sensing platform is also used to test PQ in two water samples with satisfying results. It suggests that this approach has potential applications for the determination of PQ.
关键词： phosphate pillar[5]arenes,paraquat,reduced graphene,host–guest recognition
更新于2025-09-23 15:21:21
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Deep Residual Network with Subclass Discriminant Analysis for Crowd Behavior Recognition
摘要： In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior attributes (classes). Features from these subclasses are then regularized using an eigenmodeling scheme. This enables to model the variance appearing from the intra-subclass information. Low dimensional discriminative features are extracted after using the total subclass scatter information. Dynamic time warping is used on the cosine distance measure to find the similarity measure between videos. A 1-nearest neighbor (NN) classifier is used to find the respective crowd behavior attribute classes from the normal videos. Experimental results on large crowd behavior video database show the superior performance of our proposed framework as compared to the baseline and current state-of-the-art methodologies for the crowd behavior recognition task.
关键词： Crowd behavior recognition,discriminant analysis,residual network,feature extraction
更新于2025-09-23 15:21:21
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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 - Achieving Sar Target Configuration Recognition By Combining Sparse Graph And Locality Preserving Projections
摘要： Synthetic aperture radar (SAR) target configuration recognition is a challenging task, and the key point is to realize effective feature extraction. An algorithm combing the advantages of sparse graph and locality preserving projections (LPP) is proposed to achieve SAR target configuration recognition. Taking the merits of sparse representation (SR) into consideration, an affinity matrix is established to realize effective structure preserving of the dataset. Besides, the problem of matrix singularity in LPP is effectively resolved by diagonal loading. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database validate the effectiveness and superiority of the proposed algorithm.
关键词： Synthetic aperture radar (SAR) images,sparse representation (SR),locality preserving projections (LPP),target configuration recognition
更新于2025-09-23 15:21:21
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Hand Dorsal Vein Recognition Based on Deep Hash Network
摘要： As a unique biometric technology that has emerged in recent decades, hand dorsal vein recognition has received increasing attention due to its higher safety and convenience. In order to further improve the recognition accuracy, in this paper we propose an end-to-end method for recognizing Hand dorsal vein Based on Deep hash network (DHN), called HBD. The hand dorsal vein image is input into the simpli?ed Convolutional Neural Networks-Fast (SCNN-F) to obtain convolution features. At the last fully connected layer, for the outputs of 128 neurons, sgn function is used to encode each image as 128-bit code. By comparing the distances between images after coding, it can be judged whether they are from the same person. Using a special loss function and training strategy, we verify the effectiveness of HBD on the NCUT, GPDS, and NCUT+GPDS database, respectively. The experimental results show that the HBD method can achieve comparable accuracy to the state-of-the-arts. In NCUT database, when the ratio of training and test set is 7:3, the Equal Error Rate (EER) of the test set is 0.08%, which is an order of magnitude lower than other algorithms. More importantly, due to the adoption of a simpler network structure and hash coding, HBD operates more ef?ciently and has superior performance gains over other deep learning methods while ensuring the accuracy.
关键词： Hand dorsal vein recognition,Deep hash network,Biometrics
更新于2025-09-23 15:21:21
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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 - Multi-View Bistatic Synthetic Aperture Radar Target Recognition Based on Multi-Input Deep Convolutional Neural Network
摘要： Bistatic synthetic aperture radar (SAR) can provide additional observables and scattering information of the target from multiple views. In this paper, a new bistatic SAR automatic target recognition (ATR) method based on multi-input deep convolutional neural network is proposed. The geometry of the multi-view bistatic SAR ATR is modeled, and an electromagnetic simulation approach is utilized as an alternative to generate enough bistatic SAR images for network training. Then a deep convolutional neural network with multiple inputs is designed, and the features of the multi-view bistatic SAR images will be effectively learned by the proposed network. Therefore, the proposed method can achieve a superior recognition performance. Experimental results have shown the superiority of the proposed method based on the electromagnetic simulation bistatic SAR data.
关键词： multi-view,deep convolutional neural network,automatic target recognition,Bistatic synthetic aperture radar
更新于2025-09-23 15:21:21