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conference on Optics and Biophotonics in Low-Resource Settings iii

作者： Neuhaus, Kai Alexandrov, Sergey Dsouza, Roshan O'Gorman, Sean Subhash, Hrebesh M. Hogan, Josh Wilson, Carol McNamara, Paul Leahy, Martin Natl Univ Ireland Tissue Opt & Microcirculat Imaging Facil Galway Ireland Compact Imaging Inc 897 Independence AveSuite 5B Mountain View CA 94043 USA Royal Coll Surg RCSI Dublin Ireland

ISBN: (数字)9781510605527

ISBN: (纸本)9781510605510;9781510605527

Portable and low-cost optical coherence tomography (OCT) is increasingly used to improve the accuracy of point-of-care applications. The increasing research efforts to use photonics integrated circuits (PIC) is without doubts the future for highest packaging densities in miniature optical systems. MR-OCT is another technology that is using the advantages of well known CD,DVD-ROM technology to build miniaturized and low-cost OCT systems and may be more readily available before PICs reach their full potential. For MR-OCT it is essential to separate the the multiple signals originating from the multiple reflections of the partial mirror in the reference arm of Michelson interferometer. An image analysis on MR-OCT scans is performed for filter types such as a Chebychev2 and an elliptic filter, but also an FFT filter with Gaussian window is discussed. The SNR is compared on a variety of filter parameters and the quality of the point spread function along the scan depth.

关键词： Gaussian filters Electronic filtering Signal processing Signal to noise ratio Optical coherence tomography image filtering image processing Point-of-care devices Signal attenuation

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Cartooning for Enhanced Privacy in Lifelogging and Streaming Videos 30

Cartooning for Enhanced Privacy in Lifelogging and Streaming...

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30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Hassan, Eman T. Hasan, Rakibul Shaffer, Patrick Crandall, David Kapadia, Apu Indiana Univ Bloomington Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781538607336

We describe an object replacement approach whereby privacy-sensitive objects in videos are replaced by abstract cartoons taken from clip art. Our approach uses a combination of computer vision, deep learning, and image processing techniques to detect objects, abstract details, and replace them with cartoon clip art. We conducted a user study (N=85) to discern the utility and effectiveness of our cartoon replacement technique. The results suggest that our object replacement approach preserves a video's semantic content while improving its privacy by obscuring details of objects.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Playback detection using machine learning with spectrogram features approach 10

Playback detection using machine learning with spectrogram f...

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10th International conference on Human System Interactions (HSI)

作者： Dembski, Jerzy Ruminski, Jacek Gdansk Univ Technol Fac Elect Telecommun & Informat Ul Narutowicza 11-12 PL-80952 Gdansk Poland

ISBN: (纸本)9781509046881

This paper presents 2D image processing approach to playback detection in automatic speaker verification ( ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients ( HOG) with support vector machines ( SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks ( CNN) were compared on different data partitions in respect of speakers or playback devices: for instance with different speakers in training and test subsets. The playback detection systems were trained and tested on two speech datasets S-1 and S-2 manufactured independently by two different institutions. The test error for both datasets oscillates about the level of 1% for HOG+SVM and even below it for CNN in bigger S-1 base. In cross validation scenario in which one base was used for training and second base for the test the results were very poor what suggests that the information relevant for playback detection appeared in each base in different way.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Leveraging Deep Preference Learning for Indexing and Retrieval of Biomedical images 8

Leveraging Deep Preference Learning for Indexing and Retriev...

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8th International IEEE/EMBS conference on Neural Engineering (NER)

作者： Pang, Shuchao Orgun, Mehmet A. Du, Anan Yu, Zhezhou Jilin Univ Coll Comp Sci & Technol Qianjin St 2699 Changchun Jilin Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia China Mobile HangZhou Informat Technol Co Ltd Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781509046034

This paper presents an original framework based on deep learning and preference learning to retrieve and characterize biomedical images for assisting physicians in diagnosing complex diseases with potentially only small differences between them. In particular, we use deep learning to extract the high-level and compact features for biomedical images. In contrast to the traditional biomedical algorithms or general image retrieval systems that only consider the use of pixel and/or hand-crafted features to represent images, we utilize deep neural networks for feature discovery of biomedical images. Moreover, in order to be able to index the similarly referenced images, we introduce preference learning in a novel way to learn what kinds of images we need so that we can obtain the similarity ranking list of biomedical images. We evaluate the performance of our system in detailed experiments over the well-known available OASIS-MRI database for whole brain neuroimaging as a benchmark and compare it with those of the traditional biomedical and general image retrieval approaches. Our proposed system exhibits an outstanding retrieval ability and efficiency for biomedical image applications.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Spatial and Spectral Features Fusion for EEG Classification during Motor imagery in BCI 4

Spatial and Spectral Features Fusion for EEG Classification ...

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4th IEEE EMBS International conference on Biomedical and Health Informatics (BHI)

作者： Tan, Chuanqi Sun, Fuchun Zhang, Wenchang Liu, Shaobo Liu, Chunfang Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781509041794

Brain computer interface (BCI) is the only way for some special patients to communicate with the outside world and provide a direct control channel between brain and the external devices. As a non-invasive interface, the scalp electroencephalography (EEG) has a significant potential to be a major input signal for future BCI systems. Traditional methods only focus on a particular feature in the EEG signal, which limits the practical applications of EEG-based BCI. In this paper, we propose a algorithm for EEG classification with the ability to fuse multiple features. First, use the common spatial pattern (CSP) as the spatial feature and use wavelet coefficient as the spectral feature. Second, fuse these features with a fusion algorithm in orchestrate way to improve the accuracy of classification. Our algorithms are applied to the dataset IVa from BCI complete iii. By analyzing the experimental results, it is possible to conclude that we can speculate that our algorithm perform better than traditional methods.

关键词： Electroencephalography Feature extraction Wavelet transforms Signal processing algorithms Algorithm design and analysis Brain-computer interfaces Fuses

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A Novel Algorithm for Laser Spot Detection Based on Quaternion Discrete Cosine Transform

A Novel Algorithm for Laser Spot Detection Based on Quaterni...

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International conference on Intelligent and Interactive systems and Applications (IISA)

作者： Miao, Xiaodong Shi, Xiang Xu, Zhixing Wang, Aiqing Wang, Wei Kangni Mech & Elect Co Ltd Nanjing 210009 Jiangsu Peoples R China Nanjing Univ Technol Sch Mech & Power Engn Nanjing 211800 Jiangsu Peoples R China

ISBN: (纸本)9783319495682;9783319495675

Laser spot detection is an important problem in optical measurement. The precision and speed of the detection algorithm influence the optical measurement system directly. The traditional algorithms such as Hough Transform and Gravity model are unsatisfactory in complex conditions. The laser spot detection algorithm referred in this paper is based on the quaternion discrete cosine transform and moment and a method is adopted to approximate the edge of the laser spot. Not only the center and edge can be detected simultaneously but also the robustness of noise is better than others. The algorithm is suitable for the real-time optical measurement.

关键词： Laser spot image processing Quaternion discrete cosine transform

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Development of an Embedded Atmospheric Turbulence Mitigation Engine 2

Development of an Embedded Atmospheric Turbulence Mitigation...

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conference on Long-Range Imaging II

作者： Paolini, Aaron Bonnett, James Kozacik, Stephen Kelmelis, Eric EM Photon Inc 51 East Main StSuite 203 Newark DE 19711 USA Univ Delaware Dept Elect & Comp Engn 140 Evans Hall Newark DE 19716 USA

ISBN: (数字)9781510609105

ISBN: (纸本)9781510609099;9781510609105

Methods to reconstruct pictures from imagery degraded by atmospheric turbulence have been under development for decades. The techniques were initially developed for observing astronomical phenomena from the Earth's surface, but have more recently been modified for ground and air surveillance scenarios. Such applications can impose significant constraints on deployment options because they both increase the computational complexity of the algorithms themselves and often dictate a requirement for low size, weight, and power (SWaP) form factors. Consequently, embedded implementations must be developed that can perform the necessary computations on low-SWaP platforms. Fortunately, there is an emerging class of embedded processors driven by the mobile and ubiquitous computing industries. We have leveraged these processors to develop embedded versions of the core atmospheric correction engine found in our ATCOM software. In this paper, we will present our experience adapting our algorithms for embedded systems on a chip (SoCs), namely the NVIDIA Tegra that couples general-purpose ARM cores with their graphics processing unit (GPU) technology and the Xilinx Zynq which pairs similar ARM cores with their field-programmable gate array (FPGA) fabric.

关键词： Atmospheric Turbulence Turbulence Mitigation Atmospheric Compensation image processing LongRange imagery image Enhancement Real-Time Embedded

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Machine-learning algorithms for helicopter hydraulic faults detection: model based research

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Journal of Physics: conference Series 2019年第5期1368卷

作者： A M Gareev E Yu Minaev D M Stadnik N S Davydov V I Protsenko I A Popelniuk A V Nikonorov A G Gimadiev Samara National Research University Moskovskoe Shosse 34A Samara Russia 443086 Image Processing Systems Institute of RAS - Branch of the FSRC 'Crystallography and Photonics' RAS Molodogvardejskaya street 151 Samara Russia 443001

The problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a hydraulic system. The first three approaches are based on SVM classifiers with linear, polynomial and RBF kernels and the last one is a gradient boosting on oblivious decision trees. We evaluate algorithms on the synthetic dataset generated by our simulation model of the helicopter hydraulic system and show that high accuracy fault detection can be achieved.

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Estimation of the particle concentration in hydraulic liquid by the in-line automatic particle counter based on the CMOS image sensor 10

Estimation of the particle concentration in hydraulic liquid...

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conference on Optical Measurement systems for Industrial Inspection X part of the SPIE Optical Metrology Symposium

作者： Kornilin, Dmitriy V. Kudryavtsev, Ilya A. McMillan, Alison J. Osanlou, Ardeshir Ratcliffe, Ian Samara Natl Res Univ 34 Moskovskoe Shosse Samara 443086 Russia Glyndwr Univ Mold Rd Wrexham LL11 2AW Wales

ISBN: (纸本)9781510611047;9781510611030

Modern hydraulic systems should be monitored on the regular basis. One of the most effective ways to address this task is utilizing in-line automatic particle counters (APC) built inside of the system. The measurement of particle concentration in hydraulic liquid by APC is crucial because increasing numbers of particles should mean functional problems. Existing automatic particle counters have significant limitation for the precise measurement of relatively low concentration of particle in aerospace systems or they are unable to measure higher concentration in industrial ones. Both issues can be addressed by implementation of the CMOS image sensor instead of single photodiode used in the most of APC. CMOS image sensor helps to overcome the problem of the errors in volume measurement caused by inequality of particle speed inside of tube. Correction is based on the determination of the particle position and parabolic velocity distribution profile. Proposed algorithms are also suitable for reducing the errors related to the particles matches in measurement volume. The results of simulation show that the accuracy increased up to 90 per cent and the resolution improved ten times more compared to the single photodiode sensor.

关键词： CMOS matrix sensor image processing automatic particles counters hydraulic systems diagnostic concentration measurement

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Parallel and Efficient Sensitivity Analysis of Microscopy image Segmentation Workflows in Hybrid systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

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IEEE International conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： image segmentation Sensitivity analysis Optimization Algorithm design and analysis Runtime image analysis

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