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6th international Symposium on signal processing and Intelligent Recognition Systems, SIRS 2020

6th International Symposium on Signal Processing and Intelli...

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6th international Symposium on Advances in signal processing and Intelligent Recognition Systems, SIRS 2020

ISBN: (纸本)9789811604249

The proceedings contain 27 papers. The special focus in this conference is on signal processing and Intelligent Recognition Systems. The topics include: Improved Long-Short Term Memory U-Net for image Segmentation;Extraction of Parcel Boundary from UAV images Using Deep Learning Techniques;Grad-CAM-Based Classification of Chest X-Ray images of Pneumonia Patients;periocular Recognition Under Unconstrained image Capture Distances;acoustic Prediction of Elephants for Localization and Movement Tracking Using Sensors and Distance Metrics;community Detection algorithms in Complex Networks: A Survey;a Systematic Review on the Influence of User Personality in Rumor and Misinformation Propagation Through Social Networks;HRIDAI: A Tale of Two Categories of ECGs;channel-Aware Decision Fusion with Rao Test for Multisensor Fusion;detection of Breast Cancer from Mammogram images Using Deep Transfer Learning;Comprehending the Dynamics of EEG Generated Under Various Odorant Stimulation on the Brain;Robust Beamforming Against DOA Mismatch with Null Widening for Moving Interferences;6G Ultra-Low Latency Communication in Future Mobile XR Applications;applying Neural Style Transfer to Spectrograms of Environmental Audio;identification of Indian English by Speakers of Multiple Native Languages;Robust and Imperceptible Digital image Watermarking Based on DWT-DCT-Schur;comparative Study of Maturation Profiles of Neural Cells in Different Species with the Help of Computer Vision and Deep Learning;deep Learning algorithms to Detect and Localize Acute Intracranial Hemorrhages;Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity;a Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network;supervised Feature Learning for Music Recommendation.

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From Synthetic to Real: image Dehazing Collaborating with Unlabeled Real Data 21

From Synthetic to Real: Image Dehazing Collaborating with Un...

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29th ACM international conference on Multimedia (MM)

作者： Liu, Ye Zhu, Lei Pei, Shunda Fu, Huazhu Qin, Jing Zhang, Qing Wan, Liang Feng, Wei Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Univ Cambridge Cambridge England Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9781450386517

Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, i.e. the latent haze-free image, the transmission map, and the global atmospheric light estimate, respecting the physical model of a haze process. Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing. Specifically, we encourage the coarse predictions and refinements of each disentangled component to be consistent between the student and teacher networks by using a consistency loss on unlabeled real data. We make comparison with 13 state-of-the-art dehazing methods on a new collected dataset (Haze4K) and two widely-used dehazing datasets (i.e., SOTS and HazeRD), as well as on real-world hazy images. Experimental results demonstrate that our method has obvious quantitative and qualitative improvements over the existing methods.

关键词： Single image dehazing feature disentangling unlabeled real data

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Free-obstacle path finding for assistant robot based on image skeletonization 6th

Free-obstacle path finding for assistant robot based on imag...

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6th international conference on advanced Engineering Theory and Applications, AETA 2019

作者： Martínez Santa, Fernando Orjuela Rivera, Santiago Arbulú, Mario Universidad Distrital Francisco José de Caldas Bogotá Colombia Corporación Unificada Nacional de educación superior CUN Bogotá Colombia

ISBN: (纸本)9783030530204

This document explores an autonomous navigation approach based on the generation of free-obstacle navigation diagrams by means of using the skeletonization technique and other image processing algorithms. These diagrams are turned into a path by means of a vector of points and performed by a hybrid robotic platform, composed of a humanoid robot and one with omni-directional locomotion. Four different configurations (origin, obstacles and destination) were studied in order to test the algorithm performance, computing the mean squared error and the average of relative error for each point in the path. For all of the tested configurations, the proposed algorithm chooses the shortest path through the navigation diagram. The final point and mean point-by-point relative errors were obtained for each of the 4 proposed configurations, where the average values over all of them were 1.11% for the final point relative error and 1.23% for the mean relative error, which validates the use of the algorithm for the autonomous generation of navigation paths for the hybrid robotic platform under study. © Springer Nature Switzerland AG 2021.

关键词： Motion planning

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Non-Local Single image DE-Raining Without Decomposition

Non-Local Single Image DE-Raining Without Decomposition

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IEEE international conference on Acoustics, Speech and signal processing

作者： Chaobing Zheng Zhengguo Li Yuwen Li Shiqian Wu Wuhan University of Science and Technology Wuhan China Institute for Infocomm Research Singapore

It is challenging to remove rain steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. On top of a new insight in single image de-raining, a nonlocal de-raining algorithm is proposed in this paper to remove the rain streaks from the rainy image. The rainy image is not decomposed into different layers by the proposed algorithm. Experimental results validate the proposed algorithm.

关键词： Rain conferences signal processing algorithms signal processing Acoustics Speech processing

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Speech Stuttering Detection and Removal Using Deep Neural Networks 11th

Speech Stuttering Detection and Removal Using Deep Neural Ne...

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11th international conference on Computer Engineering and Networks, CENet2021

作者： Rajput, Shaswat Nersisson, Ruban Raj, Alex Noel Joseph Mary Mekala, A. Frolova, Olga Lyakso, Elena School of Electrical Engineering Vellore Institute of Technology Vellore India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou China School of Information Technology and Engineering Vellore Institute of Technology Vellore India St. Petersburg State University Saint Petersburg Russia

ISBN: (纸本)9789811665530

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Cascaded coarse-to-fine network with hybrid loss for eyeball segmentation in CT image 21

Cascaded coarse-to-fine network with hybrid loss for eyeball...

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6th international conference on Multimedia Systems and signal processing, ICMSSP 2021

作者： Zhang, Anqi Li, Wentao Shi, Jieliang Fan, Jingfan Gao, Mingwei Yang, Jian Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology China Eye Hospital of Wenzhou Medical University Wenzhou325027 China School of Optics and Photonics Beijing Institute of Technology China

ISBN: (纸本)9781450390378

Eyeball segmentation in computed tomography (CT) images is the basis of computer-assisted diagnosis and surgery navigation system for eye diseases. Fully automatic eyeball segmentation is challenging due to the blurry boundaries, low contrast, and small proportion the eyeball occupied. We propose a framework based on cascaded coarse-to-fine network, combined with hybrid loss function for eyeball segmentation in CT images. The application of refinement module optimizes the coarse segmentation result. The hybrid loss function composed of CE, IoU, and SSIM simultaneously supervises the region and boundaries of the segmentation. Experiments on 4590 2D head CT images show that our method can effectively maintain the eyeball structure with clear boundaries and reduce the false-positive prediction in noneyeball regions. © 2021 ACM.

关键词： image segmentation

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Edge detection algorithm of plant leaf image based on improved Canny

Edge detection algorithm of plant leaf image based on improv...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Shen Xizhen Zeng Wei Guo Yiling Yin Shengyang Chengdu University of Technology Chengdu

Edge detection technology is a key technology in the field of image processing. Aiming at the problem that traditional Canny algorithm has poor adaptive ability and cannot effectively determine image edge, this paper proposes an improved Canny edge detection algorithm. The algorithm uses an improved filtering method to de-noise the image. Secondly, the four-direction gradient template is used to calculate the gradient amplitude. Finally, the high and low thresholds of images are obtained by image block processing combined with the maximum inter-class variance (Otsu) algorithm. The experimental results show that the improved Canny algorithm has good noise reduction performance and can detect the edge information of plant leaves more accurately.

关键词： image edge detection image processing Noise reduction signal processing algorithms Adaptive filters signal processing Filtering algorithms

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image Deep Steganography Detection based on Knowledge Distillation in Teacher-Student Network 3

Image Deep Steganography Detection based on Knowledge Distil...

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3rd international conference on Video, signal and image processing, VSIP 2021

作者： Hao, Huifang Zhong, Shangping Chen, Kaizhi College Of Mathematics And Computer Science Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781450385886

The current hot deep steganography can hide confidential information of an image into a carrier image of the same size. It also has the characteristics of a high steganography rate and is often used for private communication within specific organizations. However, the existing deep steganography detection network is complex and time-consuming. Thus, rapid steganography detection for massive image information is urgently needed. In this study, a typical distillation algorithm is applied to the existing classical deep steganography detection network (Ye-Net and Yedroudj-Net). We constructed a student network, taking Ye-Net and Yedroudj-Net as teacher networks respectively. Appropriate parameters T and α are also selected in accordance with the effect of knowledge distillation. With these parameters, a fast training model of deep steganography detection is established. The proposed deep steganography detection network is compared with Ye-Net and Yedroudj-Net through experiments. Three spatial steganography algorithms (Wow,S-uniward and Hill) and different steganography rates are used. The experimental results show that the proposed network can shorten the training and average detection time by about 70% and 24% when the detection accuracy is slightly lower. Overall, compared with Ye-Net and Yedroudj-Net, the proposed method can achieve fast detection of deep steganography. And code will be available at: https://***/hhfshiqi/KD-Ye-net © 2021 ACM.

关键词： Distillation

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Lightweight Multi-scale Attentional Network for Single image Dehazing

Lightweight Multi-scale Attentional Network for Single Image...

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image processing, Computer Vision and Machine Learning (ICICML), international conference on

作者： Ping Zong Jinjiang Li Zhen Hua School of Information and Electronic Engineering Shandong Technology and Business University Yantai China

ISBN: (纸本)9781665464697

Neural network-based image dehazing algorithms have obtained extensive research results. However, haze retention is still present in the results for complex outdoor images. Designing complicated networks may be improved, yet they consist of a tremendous number of parameters, which causes slow inference and requires a more demanding computer environment for execution. The network globally adopts a U-Net-like structure and employs two improved modules ARM and SAM based on the attention mechanism to implement multi-scale feature extraction and fusion. Our proposed network combines the high and low level semantics in diverse scale feature images while raising the focus on diverse haze concentration regions. Experimental results reveal that our network can get improved visual results in RESIDE-SOTS (Outdoor) and NTIRE2021 NH-HAZE image Dehazing Challenge datasets in comparison with other SOTA methods and attains higher Peak signal to Noise Ratio and Structural Similarity Index.

关键词： Performance evaluation Visualization Runtime PSNR Computational modeling Semantics Machine learning

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Depth image Super-resolution via Two-Branch Network 6th

Depth Image Super-resolution via Two-Branch Network

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Guo, Jiaxin Xiong, Rong Ou, Yongsheng Wang, Lin Liu, Chao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789811692468

With the continuous development of sensors, a number of inexpensive and effective depth cameras have shown up, which have greatly contributed to the development of autonomous driving and 3D reconstruction technologies. However, the depth images captured by low-cost depth cameras are low-resolution, which is difficult to meet the needs of practical applications. We propose a two-branch network to achieve depth map super-resolution with high-resolution guidance image, which can be viewed as a prior to guide the low-resolution depth map to restore the missing high-frequency details of structures. To emphasize the guidance role of high-resolution images, we use spatially-variant kernels based on the guidance feature map to replace the original convolution kernels. In addition, in order to extract the feature maps of the depth images more effectively, we add the channel attention mechanism between convolution layers. Our network is trained end-to-end, supporting various sizes of input images because the network backbone uses full convolution and no fully connected layers. The proposal model is trained only on a certain dataset under three super-resolution factors and utilized directly on other datasets without fine-tuning. We show the effectiveness of our model by comparing it with state-of-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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