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Reducing dimension of parametric space based on the approximation of components for interpolation of multidimensional signals

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Journal of Physics: conference Series 2019年第3期1368卷

作者： Gashnikov, M V 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

In this paper, we address the problem of parametric space dimension reduction in the interpolation of multidimensional signals task. We develop adaptive parameterized interpolation algorithms for multidimensional signals. We perform a dimension reduction of the parameter space to reduce the complexity of optimizing such algorithms. The dependences of the samples inside the signal sections and between the signal sections are taken into account in various ways to reduce the dimension. We consider the dependencies between the signal sections through the approximation algorithm for the sections. We take into account the sample dependencies inside sections due to an adaptive parameterized interpolation algorithm. As a result, we solve the optimization problem of an adaptive interpolator in the parameter space of lower dimension for each signal section separately. To study the effectiveness of adaptive interpolators, we perform computational experiments using real-world multidimensional signals. Experimental results showed that the proposed interpolator improves the efficiency of the compression method up to 10% compared with the prototype algorithm.

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The algorithm Study for Using the Back Propagation Neural Network in CT image Segmentation

The algorithm Study for Using the Back Propagation Neural Ne...

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International conference on Innovative Optical Health Science

作者： Zhang, Peng Liu, Jie Chen, Chen Li, YingQi Beijing Jiaotong Univ Sch Comp & Informat Technol Dept Biomed Engn Beijing 100044 Peoples R China

ISBN: (数字)9781510609921

ISBN: (纸本)9781510609914;9781510609921

Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can'tt accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.

关键词： Neural networks Biomedical optics Evolutionary algorithms image processing algorithms and systems image segmentation Optical imaging

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Rapid Prototyping of SoC-based Real-Time Vision System: Application to image Preprocessing and Face Detection 28

Rapid Prototyping of SoC-based Real-Time Vision System: Appl...

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conference on Pattern Recognition and Tracking XXViii

作者： Jridi, Maher Alfalou, Ayman ISEN Brest Lab ISEN Equipe Vis 20 Rue Cuirasse BretagneCS 42807 Brest 2 France

ISBN: (数字)9781510609082

ISBN: (纸本)9781510609075;9781510609082

By this paper, the major goal is to investigate the Multi-CPU/FPGA SoC (System on Chip) design flow and to transfer a know-how and skills to rapidly design embedded real-time vision system. Our aim is to show how the use of these devices can be benefit for system level integration since they make possible simultaneous hardware and software development. We take the facial detection and pretreatments as case study since they have a great potential to be used in several applications such as video surveillance, building access control and criminal identification. The designed system use the Xilinx Zedboard platform. The last is the central element of the developed vision system. The video acquisition is performed using either standard webcam connected to the Zedboard via USB interface or several camera IP devices. The visualization of video content and intermediate results are possible with HDMI interface connected to HD display. The treatments embedded in the system are as follow: (i) pre-processing such as edge detection implemented in the ARM and in the reconfigurable logic, (ii) software implementation of motion detection and face detection using either ViolaJones or LBP (Local Binary Pattern), and (iii) application layer to select processing application and to display results in a web page. One uniquely interesting feature of the proposed system is that two functions have been developed to transmit data from and to the VDMA port. With the proposed optimization, the hardware implementation of the Sobel filter takes 27 ms and 76 ms for 640x480, and 720p resolutions, respectively. Hence, with the FPGA implementation, an acceleration of 5 times is obtained which allow the processing of 37 fps and 13 fps for 640x480, and 720p resolutions, respectively.

关键词： Video Facial recognition systems Video surveillance System on a chip image processing Optical filters Cameras Embedded systems Human-machine interfaces IP cameras Field programmable gate arrays Logic Rapid manufacturing Telecommunications

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Machine Learning for Micro-Tomography 11

Machine Learning for Micro-Tomography

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SPIE conference on Developments in X-Ray Tomography XI

作者： Parkinson, Dilworth Y. Pelt, Daniel M. Perciano, Talita Ushizima, Daniela Krishnan, Harinarayan Barnard, Harold S. MacDowell, Alastair A. Sethian, James Lawrence Berkeley Natl Lab Adv Light Source Berkeley CA 94720 USA Lawrence Berkeley Natl Lab CAMERA Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781510612402;9781510612396

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommendation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab-from workstations to supercomputers-and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

关键词： micro-tomography machine learning image processing

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Cone-Beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

Cone-Beam CT image contrast and attenuation-map linearity im...

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conference on Medical Imaging - Physics of Medical Imaging

作者： Hashemi, Sayed Masoud Lee, Young Eriksson, Markus Nordstrom, Hakan Mainprize, James Grouza, Vladimir Huynh, Christopher Sahgal, Arjun Song, William Y. Ruschin, Mark Sunnybrook Res Inst Odette Canc Ctr Toronto ON Canada Univ Toronto Dept Radiat Oncol Toronto ON Canada Elekta AB Stockholm Sweden Sunnybrook Res Inst Toronto ON Canada Ryerson Univ Dept Med Phys Toronto ON Canada

ISBN: (数字)9781510607101

ISBN: (纸本)9781510607095;9781510607101

A Contrast and Attenuation-map (CT-number) Linearity Improvement (CALI) framework is proposed for cone-beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is used together with our high spatial resolution iterative reconstruction algorithm and is tailored for the Leksell Gamma Knife ICON (Elekta, Stockholm, Sweden). The incorporated CBCT system in ICON facilitates frameless SRS planning and treatment delivery. The ICON employs a half-cone geometry to accommodate the existing treatment couch. This geometry increases the amount of artifacts and together with other physical imperfections causes image inhomogeneity and contrast reduction. Our proposed framework includes a preprocessing step, involving a shading and beam-hardening artifact correction, and a post-processing step to correct the dome/capping artifact caused by the spatial variations in x-ray energy generated by bowtie-filter. Our shading correction algorithm relies solely on the acquired projection images (i.e. no prior information required) and utilizes filtered-back-projection (FBP) reconstructed images to generate a segmented bone and soft-tissue map. Ideal projections are estimated from the segmented images and a smoothed version of the difference between the ideal and measured projections is used in correction. The proposed beam-hardening and dome artifact corrections are segmentation free. The CALI was tested on CatPhan, as well as patient images acquired on the ICON system. The resulting clinical brain images show substantial improvements in soft contrast visibility, revealing structures such as ventricles and lesions which were otherwise un-detectable in FBP-reconstructed images. The linearity of the reconstructed attenuation-map was also improved, resulting in more accurate CT#.

关键词： Signal attenuation Reconstruction algorithms Brain image processing algorithms and systems Neuroimaging X-ray computed tomography Visibility X-rays Brain mapping Cones image filtering Spatial resolution

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A New Method to Detect Moving Targets

A New Method to Detect Moving Targets

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2018 3rd International conference on Education, Management and systems Engineering(EMSE 2018)

作者： Yang-ming HE Ya-fen WU School of Computer Jiangxi University of Traditional Chinese Medicine Office of Academic Research Jiangxi University of Traditional Chinese Medicine

In the moving target detection, there are a lot of methods. But the effects of those algorithms are limited, especially in complex environment. This paper putts forward a new method to detect moving targets, which uses virtual binocular disparity. It is called virtual because the environment uses one camera but the theory is about two cameras. Virtual binocular disparity algorithm regards moving targets as static targets under two cameras. It uses two neighboring images, the current frame image and the next frame image. The algorithm looks the two images as two static images under two cameras, then uses stereo match theory to calculate the disparity of the neighboring images. Because the background of the two images is the same, the disparity of their background is about zero. And the disparity of moving targets is positive or negative. The sign of disparity means the direction of targets moving. So we can use the sign of disparity to distinguish the targets moving in different directions. The direction detected out will be helpful for subsequent work, for example, targets tracking and counting etc. This algorithm fully uses the continuity property of moving targets, so it can have good effect. In the end of the paper, experiments are used to prove the algorithm.

关键词： Virtual binocular disparity Stereo match Moving targets image processing

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Simple Black-Box Adversarial Attacks on Deep Neural Networks 30

Simple Black-Box Adversarial Attacks on Deep Neural Networks

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

作者： Narodytska, Nina Kasiviswanathan, Shiva VMware Res Palo Alto CA 94304 USA Samsung Res Amer Mountain View CA USA

ISBN: (纸本)9781538607336

Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our attacks utilize a novel local-search based technique to construct numerical approximation to the network gradient, which is then carefully used to construct a small set of pixels in an image to perturb. We demonstrate how this underlying idea can be adapted to achieve several strong notions of misclassification. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.

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

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Real-time analysis on ensemble SVM scores to reduce P300-Speller intensification time 39

Real-time analysis on ensemble SVM scores to reduce P300-Spe...

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39th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

作者： Vo, Kha Nguyen, Diep N. Kha, Ha Hoang Dutkiewicz, Eryk Global Big Data Technologies Centre University of Technology Sydney Sydney Australia Faculty of Electrical and Electronics Engineering Ho Chi Minh City University of Technology VNU-HCM Viet Nam

ISBN: (纸本)9781509028092

In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a real-time signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers' highest scores. Our experiments were conducted on the dataset of the BCI Competition iii and resulted in a successful character rate of over 96% with just approximately 15 to 20 seconds for each character spelling session. As compared with the fixed 31.5 seconds of the best original approach of the competition, our proposed method significantly reduces the required spelling time by over 30% while maintaining the desired classification accuracy. © 2017 IEEE.

关键词： Support vector machines Real-time systems Training Tuning Electroencephalography Electronic mail Signal processing algorithms

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A Multilevel Technique for Automatic Foreground Extraction 4

A Multilevel Technique for Automatic Foreground Extraction

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IEEE International conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Yang, Yi-Min Lee, Ming-Sui Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781509040179

Foreground extraction is an important and challenging problem in many applications of computer vision. Most existing algorithms either require user intervention as hard constraints or demand special inputs for extra information. Thus, an automatic foreground extraction algorithm from a single image is proposed in this paper. A Gaussian image pyramid is constructed and the gradient vector flow (GVF) snake is adopted at the coarsest level to generate a rough contour of the object which is then upsampled and serves as the initial input to GVF snake in the next level. This process repeats until the estimated contour is propagated to the finest level. Based on the result in the finest level, a binary mask can be generated accordingly and becomes the initial constraint in the segmentation. The proposed segmentation step includes two novel schemes, which simulate the Latent Dirichlet Allocation (LDA) generative model and a probabilistic stochastic process called Chinese restaurant process. With these mechanisms, the Gaussian Mixture Models adaptively determine the number of components for foreground and background individually. As a result, the proposed method is expected to not only produce a satisfactory result of foreground extraction automatically with more robustness and adaptation but also serve as a good preprocessing to improve the performance and accuracy for tasks in computer vision.

关键词： image segmentation Sun Videos Shape Eigenvalues and eigenfunctions Covariance matrices Maximum likelihood estimation

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A Low-IF Bandpass ΣΔ ADC for fully-integrated CMOS Magnetic Resonance Imaging Receivers 60

A Low-IF Bandpass ΣΔ ADC for fully-integrated CMOS Magneti...

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60th IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Paton, S. Prefasi, E. Dominguez-Suarez, M. Portela-Garcia, M. Carlos III Univ Madrid Elect Technol Dept Madrid Spain

ISBN: (纸本)9781509063895

This work presents a 180-nm CMOS bandpass Sigma Delta Analog-to-Digital Converter (ADC) developed to fulfill the specifications of a fully-integrated receiver for Magnetic Resonance Imaging (MRI). CMOS integration of a multichannel digital receiver would increase the quality of the image without the need of using many coaxial cables to connect the RF coils (located close to the patient) with the digitizing and processing hardware. A very simple Low-IF receiver is proposed with digital IQ demodulation, where a continuous time Low-IF bandpass ADC is the most efficient architecture. The continuous time circuitry can be used to remove the classical filter, as in this application no interferers are expected, and an anti-alias filter is enough. The bandpass loop filter attenuates the quantization noise in the narrow input bandwidth, while stability is easily achieved, as in many low-pass loop filters, due to the selected Low-IF. Measurements show 82dB DR over 1MHz bandwidth, centered at 5MHz. The measured IMD3 product is 74dB, which is good enough for the application.

关键词： Analog-to-Digital Converter ADC Magnetic Resonance Imaging MRI

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