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International Conference on Wireless communications and Signal processing (WCSP)

作者： Ya'nan Yang Xiaofan Wang Feng Liu Zongliang Gan Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509028610

Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. In view of this, combining with the human visual characteristics, a color image enhancement algorithm based on tone-preserving was proposed. A determination function was added to the bilateral filter to estimate illuminance image more accurately and weaken over-enhancement. According to human visual masking effect, the improved gamma correction was utilized to correct the brightness of illumination image adaptively and the local contrast of reflection image obtained by division was enhanced based on local statistics. Besides, the final enhanced image was obtained by combining illumination image with reflection image, which can make image appear more natural. Compared with other similar algorithms from both subjective and objective aspects, the results show that this method being applied to low-contrast color image enhancement can not only improve image clarity, but reduce color distortion.

关键词： image color analysis Brightness Lighting Color Visualization Nonlinear distortion

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Maneuvering target tracking based on fuzzy reasoning

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2009年第3期31卷 541-544页

作者： Han, Hong Chen, Zhao-Ping Jiao, Li-Cheng Zhi, Jian-Wei Key Lab. of Intelligent Perception and Image Understanding Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 China

The performance of the 'current' statistical model algorithm gets worse when there is a sudden maneuver. To solve this problem, an improved algorithm is presented. This algorithm is implemented through a pair of parallel adaptive filters together with information fusion technique. By introducing the state information of targets, the output of the fuzzy system can adjust the predicted covariance of the filter adaptively. The simulation results show that the proposed algorithm has better performance when there is a sudden maneuver.

关键词： Kalman filters

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Recognizing Human Group Behaviors with Multi-group Causalities

Recognizing Human Group Behaviors with Multi-group Causaliti...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Cong Zhang Xiaokang Yang Weiyao Lin Jun Zhu Institute of Image Communication and Network Engineering Shanghai Key Laboratories of Digital Media Processing and Communication Shanghai Jiaotong University Shanghai China

Human group behaviors are usually composed of several sub-groups. Considering the interaction between groups, this paper presents an algorithm to recognize human group behavior with multi-group causalities. It has two main contributions: (1) we introduce inter-group causality to reflect the interaction between human groups, (2) an improved coding scheme (i.e. Locality-constrained Linear Coding) is used for encoding the causality to go beyond Vector Quantization (VQ). Finally, a simple linear SVM is adopted to learn this model. Our experiment results demonstrate that inter-group causality feature and LLC methods can significantly boost behavior recognition performance.

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Spatial-Temporal Constrained Pseudo-labeling for Unsupervised Person Re-identification via GCN Inference 18th

Spatial-Temporal Constrained Pseudo-labeling for Unsupervis...

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18th International Forum of Digital Multimedia communication, IFTC 2021

作者： Ling, Sen Yang, Hua Liu, Chuang Chen, Lin Zhao, Hongtian The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811922657

Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the available source labeled data to assign pseudo labels for the unannotated data. Whereas, the cluster distance usually fails to adapt to different datasets due to the domain gap. Besides, learning exclusively from the source data can not generate accurate pseudo labels for the lack of the target data information. To address this problem, we propose to exploit the spatial-temporal constraints to facilitate the pseudo label generation process. Specifically, graphs for the labeled source data are constructed and the graph convolution network (GCN) is used to learn graph embeddings. Based on these graph embeddings, the likelihood of linkages between graph nodes is estimated and utilized to assign pseudo labels for the unlabeled data. Then, with the pseudo labels, a smoothed spatial-temporal probability distribution model is generated to amend the likelihood of linkages between graph nodes as well as correct the visual similarity scores for person Re-ID. Finally, we optimize the pseudo label assignment, feature extraction networks, and spatial-temporal model alternatively and iteratively to improve the person Re-ID performance. Comprehensive experiments demonstrate that the proposed method outperforms state-of-the-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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Dual-Mode iterative denoiser: Tackling the weak label for anomaly detection

Dual-Mode iterative denoiser: Tackling the weak label for an...

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International Conference on Pattern Recognition

作者： Shuheng Lin Hua Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai key lab of digital media processing and transmission Shanghai China

Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar video clips, where the spatial-temporal similarity helps the cluster metric to represent the reconstruction error. Then we use the graph convolution neural network (GCN) to explore the temporal correlation and the feature similarity between video clips within different rough labels, where the classifier can be constantly updated in the label denoising process. Without specific image-level labels, our model can predict the clip-level anomaly probabilities for videos. Extensive experiment results on two public datasets show that our approach performs favorably against the state-of-the-art methods.

关键词： Training Convolution Noise reduction Neural networks Training data Predictive models Pattern recognition

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Supervised segmentation of vasculature in retinal images using neural networks

Supervised segmentation of vasculature in retinal images usi...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Chen Ding Yong Xia Ying Li Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) Northwestern Polytechnical University Xian

ISBN: (纸本)9781479962853

This paper proposes a neural network based supervised segmentation algorithm for retinal vessel delineation. The histogram of each training image patch and its optimal threshold acquired through iteratively comparing the binaryzation result to the manual segmentation are applied to a BP neural network to establish the correspondence between the intensity distribution and optimal segmentation parameter. Finally, each test image can be segmented by using a number of local thresholds that are predicted by the trained the neural network according the histograms of image patches. The propose algorithm has been evaluated on the DRIVE database that contains forty retinal images with manually segmented vessel trees. Our results show that the proposed algorithm can effective segment the vasculature in retinal images.

关键词： image segmentation Biomedical imaging Blood vessels Retina Training Histograms Artificial neural networks

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Dynamic integral sliding mode for launch vehicle attitude control system

Dynamic integral sliding mode for launch vehicle attitude co...

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Chinese Control and Decision Conference, CCDC

作者： Zirui Wang Lei Liu Yongji Wang Zhishen Wang Key Lab for Image Processing & Intelligent Control Huazhong University of Science & Technology Wuhan China

As it is known that launch vehicle is facing a very harsh environment during the fight process. The challenge of various perturbations and uncertainties has lead to many traditional control methods' failure to meet the requirements of attitude control system. Due to the main advantage of sliding mode control's robustness to the system uncertainties and disturbances in the so-called sliding mode, it has been widely used in engineering. In this paper, regarding particularly on chattering problem, the authors developed a novel dynamic integral sliding mode control scheme and the comparative simulation results carried out with traditional dynamic integral sliding mode demonstrates the superiority of the newly designed control law.

关键词： Sliding mode control Rockets Attitude control Simulation Vehicle dynamics Vehicles

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A radix-2 FFT algorithm for Modern Single Instruction Multiple Data (SIMD) architectures

A radix-2 FFT algorithm for Modern Single Instruction Multip...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Paul Rodríguez V. Image and Video Processing and Communication Lab (ivPCL) Department of Electrical and Computer Engineering The University of New Mexico Mexico

Modern Single Instruction Multiple Data (SIMD) microprocessor architectures allow parallel floating point operations over four contiguous elements in memory. The radix-2 FFT algorithm is well suited for modern SIMD architectures after the second stage (decimation-in-time case). In this paper, a general radix-2 FFT algorithm is developed for the modern SIMD architectures. This algorithm (SIMD-FFT) is implemented on the Intel Pentium and Motorola PowerPC architecture for 1D and 2D. The results are compared against Intel's implementation of the split-radix FFT for the SIMD architecture [2] and the FFTW [3]. Overall, the SIMDFFT was found to be faster than the other two implementations for complex 1D input data (ranging from 95.9% up to 372%), and for complex 2D input data (ranging from 68.8% up to 343%) as well.

关键词： Linux Modems Microprocessors Clocks

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Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

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16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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Extraction method of microscopic moving particle basing on multimodal background model

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ICIC Express Letters, Part B: Applications 2015年第12期6卷 3153-3158页

作者： Zhang, Yanbin Yang, Zhangzhen Zhang, Hui Qin, Lei Image Processing and Image Communication Lab Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications No. 66 New Mofan Road Nanjing210003 China Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications No. 66 New Mofan Road Nanjing210003 China

Target extraction is a key technology for image measurement of moving particles distributed in fluidic system. In this paper, we propose a novel moving particle extraction method based on multimodal characteristic of the image. For the proposed particle extraction method, we firstly analyze the origin of multimodal characteristic of image background in the microscopic observation system, and then we design multimodal background model for particle extraction. For improving the performance of the model, we exploit the moving principle in the micro channel, and discuss the setting of learning rate. The experimental results demonstrate that the proposed methods achieve good performance compared with several traditional methods. © 2015, ICIC Express Letters Office. All rights reserved.

关键词： Extraction

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