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10th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2017

作者： Aichinger, P. Roesner, I. Schoentgen, J. Pernkopf, F. Department of Otorhinolaryngology Division of Phoniatrics-Logopedics Medical University of Vienna Austria F.N.R.S. Université Libre de Bruxelles Laboratories of Image Signal Processing and Acoustics Faculty of Applied Sciences Brussels Belgium Signal Processing and Speech Communication Lab Graz University of Technology Austria

ISBN: (纸本)9788864536064

The presence of random extra pulses during quasi-closed glottal cycle phases may constitute a distinct voice quality type relevant to the clinical care of disordered voices. In this paper, we propose for this voice type a glottal area waveform model that includes automatic parameter estimation. The model involves (1) extraction of the fundamental frequency, (2) estimation of the cyclic pulse times, heights and shapes, (3) Fourier synthesis of a cyclic pulse train model, (4) closed phases estimation via fitting an inverted parabola to the averaged pulse shape, (5) estimation of the random extra pulses’ positions and shapes, and (6) pulse shape filtering based synthesis of the random extra pulses. For a typical voice sample, the root mean square error energy level of the purely cyclic model = -13.2 dB, which improves by 1.5 dB when extra pulses are added to the model. © Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA *** right reserved.

关键词： Mean square error

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Error concealment techniques for video transmission over error-prone channels: a survey

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Journal of Computational Information Systems 2012年第21期8卷 8807-8818页

作者： Cui, Ziguan Gan, Zongliang Zhan, Xuefeng Zhu, Xiuchang Image Processing and Image Communication Lab Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of 'Broadband Wireless Communication and Sensor Network Technology' Nanjing University of Posts and Telecommunications Ministry of Education China Institute of Physics and Communication Electronics Jiangxi Normal University Nanchang 330027 China

Efficient video transmission over unreliable channels may encounter huge challenge due to unavoidable bit error or packets loss. Error concealment (EC) techniques at the decoder side have been developed to recover the damaged regions utilizing spatial or temporal redundant information without changing the encoder structure or adding extra bandwidth. In this work the classic EC techniques and their developments are first reviewed, high-level semantics based EC schemes are also surveyed, and then the emphasis is focused on new EC features introduced by H.264/AVC. Finally, the challenges and future development directions in EC for advanced video coding schemes such as scalable video coding (SVC), multiple description coding (MDC), multi-view video coding (MVC) and stereo video coding are prospected, and future research directions are also indicated according to the current research status and existent problems. © 2012 Binary Information Press.

关键词： Scalable video coding

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Signal detection in severely heavy-tailed radar clutter 29

Signal detection in severely heavy-tailed radar clutter

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29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995

作者： Tsihrintzis, George A. Tsakalides, Panagiotis Nikias, Chrysostomos L. Communication Systems Lab Department of Electrical Engineering University of Virginia CharlottesvilleVA22903-2442 United States Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los AngelesCA90089-2564 United States

ISBN: (纸本)0818673702

Alpha-stable distributions have recently been recognized in the signal processing community as simple, yet very accurate, two-parameter statistical models for signals and noises that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order statistics. In this paper, we attempt to popularize these new signal processing tools within the radar community. In particular, we evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection and classification on real radar, sea-clutter data. © 1996 IEEE

关键词： Signal detection

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Visual perception preserving decolorization method

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, image processing and Pattern Recognition 2016年第7期9卷 65-78页

作者： Chen, Jie Li, Xin Zhu, Xiuchang Wang, Jin Key Lab of Image Processing and Image Communication of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China College of Information Engineering Yangzhou University Yangzhou225009 China

This paper presents a decolorization method using gradient and saliency as the maintained features in the conversion to preserve the local and global visual perception. First, we construct a linear parametric mapping function of RGB color channels. Then, we calculate the feature value of each pixel in the color image and the parameterized grayscale image, the feature value integrates the pixel gradient and region saliency. Finally, we search for the parameters which can get the minimum of the total differences between the feature values of color and grayscale images, and substitute into the linear parametric function to get the decolorization result. To enhance the efficiency of getting the best parameters, we properly relax the strict computation formulas of the gradient and saliency to construct a linear least square problem, and obtain the optimal parameters by solving optimization. Experimental results show that our method using the discrete searching strategy can maintain the contrasts meanwhile avoid the excessive enlargement of the contrasts during the color-to-gray conversion, this property guarantees the preserving of the visual perception. Our method using the linear least square strategy can reduce the computation time and frequently get the similar results with our discrete searching method. © 2016 SERSC.

关键词： Pixels

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The statistic modeling of eye movement viewing S3D images 14th

The statistic modeling of eye movement viewing S3D images

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14th International Forum of Digital TV and Wireless Multimedia communication, IFTC 2017

作者： Zhang, Chi Zhou, Jun Zhu, Shoucheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Shanghai Yanan High School Shanghai200336 China

ISBN: (纸本)9789811081071

Nowadays, more and more families are willing to buy 3D TV to improve their watching experience. Stereo perception produced by watching 3D images or videos brings strong immersive watching experience to users. However, accumulated vision fatigue confuses users a lot after watching 3D TV for a long time. When watching 3D images, controlled by past recognition experience and visual attention mechanism, gaze point of two eyes is changing among different objects which have different depth of field. The eye movement in this changing process is called vergence. Vergence can be defined as movement of our eyes in opposite directions to locate the area of interest on the fovea and accommodation as alteration of the lens to obtain and maintain the area of interest focused on the fovea. So the more frequently the vergence process occurs, the more uncomfortable we feel. We expect to obtain several eye movement patterns, which can be considered as some typical visual attention patterns, by building a top-down recognition and visual attention model and then applying some clustering methods to find them. So we use an eye tracker to record eye movement data and then model it as a bayesian network model. The generative model is based on beta process and we build an Autoregression-HMM model to describe the relationship between latent eye movement patterns and eye movement data. To uncover parameters which represent different eye movement patterns in this model, we use MCMC method to calculate them with iterative computations. In this work, some different latent patterns existed in the sequential eye movement data can be revealed. After analyzing these patterns, we are able to find out some similarities and differences of visual attention models between different people watching the same image or between different images viewed by the same one. These conclusions can help to improve quality of 3D image thus lessening the users’ vision fatigue when watching 3D TV. This work also will con

关键词： Eye movements

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Automatic sign language recognition: Vision based feature extraction and probabilistic recognition scheme from multiple cues 08

Automatic sign language recognition: Vision based feature ex...

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1st International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2008

作者： Caridakis, George Diamanti, Olga Karpouzis, Kostas Maragos, Petros Image Video and Multimedia Systems Lab. National Technical University of Athens Iroon Polytexneiou 9 15780 Athens Greece Computer Vision Speech Communication and Signal Processing Group National Technical University of Athens Iroon Polytexneiou 9 15780 Athens Greece

ISBN: (纸本)9781605580678

This work focuses on two of the research problems comprising automatic sign language recognition, namely robust computer vision techniques for consistent hand detection and tracking, while preserving the hand shape contour which is useful for extraction of features related to the handshape and a novel classification scheme incorporating Self-organizing maps, Markov chains and Hidden Markov Models. Geodesic Active Contours enhanced with skin color and motion information are employed for the hand detection and the extraction of the hand silhouette, while features extracted describe hand trajectory, region and shape. Extracted features are used as input to separate classifiers, forming a robust and adaptive architecture whose main contribution is the optimal utilization of the neighboring characteristic of the SOM during the decoding stage of the Markov chain, representing the sign class. Copyright 2008 ACM.

关键词： image segmentation

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Adaptive Person-Specific Appearance-Based Gaze Estimation 16th

Adaptive Person-Specific Appearance-Based Gaze Estimation

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16th International Forum on Digital TV and Wireless Multimedia communication, IFTC 2019

作者： Zheng, Chuanyang Zhou, Jun Sun, Jun Zhao, Lihua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Children’s Hospital of Shanghai Shanghai200062 China

ISBN: (纸本)9789811533402

Non-invasive gaze estimation from only eye images captured by camera is a challenging problem due to various eye shapes, eye structures and image qualities. Recently, CNN network has been applied to directly regress eye image to gaze direction and obtains good performance. However, generic approaches are susceptible to bias and variance highly relating to different individuals. In this paper, we study the person-specific bias when applying generic methods on new person. And we introduce a novel appearance-based deep neural network integrating meta-learning to reduce the person-specific bias. Given only a few person-specific calibration images collected in normal calibration process, our model adapts quickly to test person and predicts more accurate gaze directions. Experiments on public MPIIGaze dataset and Eyediap dataset show our approach has achieved competitive accuracy to current state-of-the-art methods and are able to alleviate person-specific bias problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences

FC-GNN: Recovering Reliable and Accurate Correspondences fro...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haobo Xu Jun Zhou Hua Yang Renjie Pan Cunyan Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However, matching noise within and between images, along with inconsistent key-point detection, frequently degrades the matching performance. We review these problems and thus propose: 1) a novel and unified Filtering and Calibrating (FC) approach that jointly rejects outliers and optimizes inliers, and 2) leveraging both the matching context and the underlying image texture to remove matching uncertainties. Under the guidance of the above innovations, we construct Filtering and Calibrating Graph Neural Network (FC-GNN), which follows the FC approach to recover reliable and accurate correspondences from various interferences. FC-GNN conducts an effectively combined inference of contextual and local information through careful embedding and multiple information aggregations, predicting confidence scores and calibration offsets for the input correspondences to jointly filter out outliers and improve pixel-level matching accuracy. Moreover, we exploit the local coherence of matches to perform inference on local graphs, thereby reducing computational complexity. Overall, FC-GNN operates at lightning speed and can greatly boost the performance of diverse matching pipelines across various tasks, showcasing the immense potential of such approaches to become standard and pivotal components of image matching. Code is avaiable at https://***/xuy123456/fcgnn.

关键词： Matched filters Computer vision Technological innovation Accuracy Uncertainty Computer network reliability Pipelines

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Automated Anemia Classification and Hemoglobin Level Prediction using Deep CNN and GLCM Features of Palpebral Conjunctiva images

Automated Anemia Classification and Hemoglobin Level Predict...

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Conference on Information and communication Technology (CICT)

作者： Chandrasekhar Bhusham Ajay Kumar Reddy Poreddy Thunakala Bala Krishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Anemia is a common medical condition affecting millions worldwide, particularly in developing countries. Early detection of anemia is crucial for prompt treatment and prevention of its potential complications. In recent years, deep learning (DL) has shown great potential in various medical applications, including medical image classification, anomaly detection, and segmentation. This study proposes a transfer learning-based approach using a pre-trained DL model to detect anemia from palpebral conjunctiva images. The proposed method utilizes a pre-trained DenseNet-201 model and fine-tuned it on a target dataset of palpebral conjunctiva images to detect anemia. Deep features of palpebral conjunctiva images computed from the fine-tuned DenseNet-201 are fed to MLP to identify anemia. The performance of the proposed method is evaluated on a publicly available anemia dataset, and the results show that the proposed method achieves an accuracy of 93.7 % in detecting anemia from palpebral conjunctiva images. In addition to anemia classification, we computed the hemoglobin level of palpebral conjunctiva images based on the gray-level co-occurrence matrix (GLCM) statistical properties. The statistical properties of GLCM are given to support vector and polynomial regressors, and the mean value of the predicted scores of both regressors is used to estimate the hemoglobin level. Experimental results show that the proposed model achieves an average root mean square error of 0.72 for conjunctiva images.

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Noise and performance analysis on fundus images with CNN and transformer models

Noise and performance analysis on fundus images with CNN and...

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Conference on Information and communication Technology (CICT)

作者： Niranjana Vannadil Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

Fundus imaging is a valuable diagnostic tool in ophthalmology, providing clinicians with detailed visualizations of the retina and aiding in the detection and monitoring of various eye diseases, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and cataract. However, the quality of fundus images can be significantly affected by noise, mainly additive white Gaussian noise (AWGN), which is inherent in many imaging systems. The presence of noise in real-world data poses significant challenges for computer vision tasks. In the field of medical image classification, a wrong diagnoisis has heavy consequences. Understanding the impact of AWGN on fundus images is crucial for developing practical denoising algorithms and improving diagnostic accuracy. This work presents an analysis of AWGN noise in fundus images aims to characterize its effects on image quality and assess its impact on diagnostic tasks. The work also analyzes the performance of six models (3 each) of two popular deep learning architectures, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in the presence of AWGN. AWGN is first introduced to the clean image datasets to conduct the analysis. The CNN and ViT models are trained on the noisy datasets to evaluate the performance of the image classification task. The work also involves six denoising algorithms and a popular image enhancement algorithm- Contrast Limited Adaptive Histogram Equalization (CLAHE).

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