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7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Shishido, Hidehiko Kitahara, Itaru Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781538618424

Camera calibration that estimates the projective relationship between 3D and 2D image spaces is one of the most crucial processes for such 3D image processing as 3D reconstruction and 3D tracking. A strong calibration method, which needs to place landmarks with known 3D positions, is a common technique. However, as the target space becomes large, landmark placement becomes more complicated. Although a weak-calibration method does not need known landmarks to estimate a projective transformation matrix from the correspondence information among multi-view images, the estimation precision depends on the accuracy of the correspondence. When multiple cameras are arranged sparsely, detecting sufficient corresponding points is difficult. In this research, we propose a calibration method that bridges sparse multiple cameras with mobile camera images. The mobile camera captures video images while moving among sparse multi-view cameras. The captured video resembles dense multi-view images and includes sparse multi-view images so that weak-calibration is effective. We confirmed the appropriate spacing between the images through comparative experiments of camera calibration accuracy by changing the number of bridging images and applied our proposed method to multiple capturing experiments in a large-scale space and verified its robustness.

关键词： camera calibration multi-view camera bridging image pose estimation badminton image

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A New Pixel Readout Circuit for x-Ray image Sensor 9th

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9th International conference on Robotic, Vision, signal processing and Power applications (RoViSP)

作者： Rhaffor, Nuha Binti A. Zawawi, Ruhaifi Bin Abdullah Mohd, Shukri Bin Korakkottil Kunhi Bin Abd Manaf, Asrulnizam Bin Sidek, Othman Univ Sains Malaysia Sains USM Collaborat Microelect Design Excellence Ctr Level 1Block C10 Persiaran Bukit Jambul Bayan Lepas 11900 Penang Malaysia Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia

ISBN: (纸本)9789811017216;9789811017193

A new high speed Complementary Metal Oxide Semiconductor (CMOS) pixel readout circuitry consuming a small area is proposed. The whole schematic is designed using Silterra 0.18 mu m CMOS technology and consists of 12 small-sized MOSFETs that are one-third smaller than other solutions. The time taken to capture an image of the inner body is better than that in the literature, although the same input current is used. Simulation result shows that at the minimum input current of 3 nA, the image for the three pixels at the same row can be obtained clearly at every clock cycle.

关键词： x-Ray image sensor Pixel readout circuit Fast detection Smaller design

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A Multiple-Lane Vehicle Tracking Method for Forward Collision Warning System applications 9

A Multiple-Lane Vehicle Tracking Method for Forward Collisio...

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9th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Li, Yuan-Fu Tsai, Chia-Chi Lai, Yi-Ting Guo, Jiun-In Natl Chiao Tung Univ Dept Elect Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781538615423

This paper proposes a vehicle tracking method for the Forward Collision Warning System (FCWS). The proposed method applies vehicle detection and feature tracking to each frame in the videos to enhance the detection accuracy and increase the stability of vehicle localization. The proposed method can be applied to three-lane FCWS which includes left, main, and right lanes. The performance of the proposed three-lane FCWS can achieve 720x480 video@183 fps in average when realized in PC with Intel Core i7 processor with average detection accuracy of 94.05% at daytime and 86.90% at night. The proposed method is also implemented on Freescale ***6 embedded platform with a USB webcam to capture the video. Under the D1 (720x480) resolution, the performance of the proposed 3-lane FCWS can achieve 29 fps.

关键词： Feature extraction Microsoft Windows Vehicle detection image color analysis Histograms Videos Error analysis

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image Matching Using GPT Correlation Associated with Simplified HOG Patterns 7

Image Matching Using GPT Correlation Associated with Simplif...

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7th International conference on image processing Theory, Tools and applications (IPTA)

作者： Zhang, Shizhi Wakahara, Tom Yamashita, Yukihiko Tokyo Inst Technol Grad Sch Sci & Engn Meguro Ku 2-12-1 O Okayama Tokyo 1528550 Japan Hosei Univ Fac Comp & Informat Sci 3-7-2 Kajino Cho Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538618424

GAT (Global Affine Transformation) and GPT (Global Projection Transformation) matchings proposed by Wakahara and Yamashita calculate the optimal AT (affine transformation) and PT (2D projection transformation), respectively. These image matching criteria realize deformation-tolerant matchings by maximizing the normalized cross-correlation between a template and a GAT/GPT-superimposed image. In order to shorten the calculation time, Wakahara and Yamashita also proposed the acceleration algorithms for GAT/GPT matchings. Later on, Wakahara et al. proposed the enhanced GPT matching to calculate the optimal PT parameters simultaneously which overcomes the incompatibility during the matching process. Zhang et al. figured out that these matching techniques do not take account of the conservation of the L2 norm, and introduced norm normalization factors that realize accurate and stable matchings. All these correlation-based matching techniques are well suited for "whole-to-whole" image matching, but are weak in "whole-to-part" image matching being cursed by complex backgrounds and noise. This research firstly proposes simplified HOG patterns for the enhanced GPT matching with norm normalization to obtain the robustness against noise and background. Secondly, this research also proposes the acceleration algorithm for the proposed matching criterion by creating several reference tables. Experiments using the Graffiti dataset show that the proposed method exhibits an outstanding matching ability compared with the original GPT correlation matching and the well-known combination of SURF feature descriptor and RANSAC algorithm. Furthermore, the computational complexity of the proposed method is significantly reduced below double figures via the acceleration algorithm.

关键词： Correlation image matching Acceleration Kernel Computational complexity Electronic mail

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Double-Density and Dual-Tree Based Methods for image Super Resolution 7

Double-Density and Dual-Tree Based Methods for Image Super R...

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conference on Geospatial Informatics, Fusion, and Motion Video Analytics VII

作者： Giansiracusa, Mike Blasch, Erik Singerman, Paul Ezekiel, Soundararajan Indiana Univ Penn Comp Sci 210 S 10th St Indiana PA 15701 USA US Air Force Res Lab Name 26 Elect Pkwy Rome NY 13441 USA

ISBN: (数字)9781510609006

ISBN: (纸本)9781510608993;9781510609006

When several low-resolution images are taken of the same scene, they often contain aliasing and differing sub-pixel shifts causing different focuses of the scene. Super-resolution imaging is a technique that can be used to construct high-resolution imagery from these low-resolution images. By combining images, high frequency components are amplified while removing blurring and artifacting. Super-resolution reconstruction techniques include methods such as the Non-Uniform Interpolation Approach, which is low resource and allows for real-time applications, or the Frequency Domain Approach. These methods make use of aliasing in low-resolution images as well as the shifting property of the Fourier transform. Problems arise with both approaches, such as limited types of blurred images that can be used or creating non-optimal reconstructions. Many methods of super-resolution imaging use the Fourier transformation or wavelets but the field is still evolving for other wavelet techniques such as the Dual-Tree Discrete Wavelet Transform (DTDWT) or the Double-Density Discrete Wavelet Transform (DDDWT). In this paper, we propose a super-resolution method using these wavelet transformations for use in generating higher resolution imagery. We evaluate the performance and validity of our algorithm using several metrics, including Spearman Rank Order Correlation Coefficient (SROCC), Pearson's Linear Correlation Coefficient (PLCC), Structural Similarity Index Metric (SSIM), Root Mean Square Error (RMSE), and Peak-signal-Noise Ratio (PSNR). Initial results are promising, indicating that extensions of the wavelet transformations produce a more robust high resolution image when compared to traditional methods.

关键词： image resolution image processing image converters Rendering High definition

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Phase Retrieval – A Deconvolution Perspective

Phase Retrieval – A Deconvolution Perspective

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Abhilash Sainathan Chandra Sekhar Seelamantula Department of Electrical Communication Engineering Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9781728102436;9789881476852

Phase retrieval finds applications in various optical imaging modalities such as x-ray crystallography, holography, frequency-domain optical-coherence tomography, etc. The sensors used in optical imaging can measure only the magnitudes of incoming wavefronts and the phase information is not measured directly. This necessitates developing appropriate phase retrieval algorithms to reconstruct the object as phase contains most of the structural information. The phase retrieval problem naturally arises in the Fourier imaging context, where the measurement is the Fourier magnitude/intensity spectrum. Reconstruction from the Fourier intensity results in the autocorrelation and not the signal. We therefore address the equivalent problem of signal retrieval from the autocorrelation. Since the signal autocorrelation can be expressed as a convolution of the signal with its flipped version, we propose to solve the phase retrieval problem within a deconvolution framework. We consider a non-convex cost in two vector variables, the signal and its flipped version. An alternating minimization (Alt. Min.) strategy is employed to arrive at an optimal estimate of the signal, given the autocorrelation. Due to non-convexity of the cost function, the accuracy of the estimation is critically dependent on the initialization. We establish that the Alt. Min. iterates ensure that the cost is nonincreasing. For the specific case of causal, delta-dominant signals, the proposed framework results in exact reconstruction with an all zero-phase initialization. We shall also consider the effect of random initialization on the estimation accuracy.

关键词： Cost function Correlation Phase measurement Deconvolution image reconstruction Minimization

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FPGA based real-time on-road stereo vision system

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 81卷 32-43页

作者： Dehnavi, M. Eshghi, M. Shahid Beheshti Univ EE Dept Tehran *** Iran

Accurate and Real-Time stereo vision is an essential need for many computer vision applications, such as On Road stereo vision system. In this paper, a fast and accurate system for On-Road stereo vision application is presented. In order to achieve this purpose, first, an algorithm is presented that is optimized for hardware implementation and On-Road application, then an appropriate hardware architecture for this algorithm is proposed. The approach uses gradient and Census transform as initial cost function, then cost is aggregate in cross-based supported reign. LR-check and disparity refinement is also utilized to improve final disparity. The proposed design is a standalone stereo vision system where all steps of algorithm is implemented on a hardware platform. Overall system is implemented on FPGA platform and it is tested on KITTI Database. The proposed system is also tested on Middlebury database without any changes in parameters. Experimental results show that the proposed system has high accuracy in KITTI and Middlebury database. In term of hardware resource, the system occupy %%66 of xC6VLx240T FPGA device for 1920 x 1080 image with 96 disparity levels that operated at 53.5 Fps.

关键词： FPGA Stereo vision On-road image Obstacle Hardware architecture

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Current calibration algorithm for bolometer-type uncooled infrared image sensor using pipeline signal processing 4

Current calibration algorithm for bolometer-type uncooled in...

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conference on image Sensing Technologies - Materials, Devices, Systems, and applications IV

作者： Kim, Sang-Hwan Choi, Byoung-Soo Oh, Chang-Woo Shin, Jang-Kyoo Park, Jae-Hyoun Lee, Kyoung-Il Kyungpook Natl Univ Sch Elect Engn 80 Daehakro Daegu South Korea Kyungpook Natl Univ Dept Sensor & Display Engn 80 Daehakro Daegu South Korea Korea Elect Technol Inst 25 Saenari Ro Seongnam Si Gyeonggi Do South Korea

ISBN: (纸本)9781510609204

In this paper, we propose a pixel averaging current calibration algorithm for reducing fixed pattern noise due to the deviation of bolometer resistance. To reduce fixed pattern noise (FPN), averaging current calibration algorithm by which output current of each bolometer reference pixel is averaged by the averaging current calibration is suggested. The principle of algorithm is that average dark current of reference pixel array is subtracted by a dark current of each active pixel array. After that, the current difference with information of pixel deviation is converted to voltage signal through signal processing. To control the current difference of pixel deviation, a proper calibration current is required. Through this calibration algorithm, nano-ampere order dark currents with small deviations can be obtained. Sensor signal processing is based on a pipeline technique which results in parallel processing leading to very high operation. The proposed calibration algorithm has been implemented by a chip which is consisted of a bolometer active pixel array, a bolometer reference pixel array, average current generators, line memories, buffer memories, current-to-voltage converters (IVCs), a digital-to-analog converters (DACs), and analog-to-digital converters (ADCs). Proposed bolometer-resistor pixel array and readout circuit has been simulated and fabricated by 0.35 mu m standard CMOS process.

关键词： uncooled infrared image sensor calibration algorithm bolometer resistor fixed pattern noise

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Analysis of signal-dependent sensor noise on JPEG 2000-compressed Sentinel-2 multi-spectral images 23

Analysis of signal-dependent sensor noise on JPEG 2000-compr...

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conference on image and signal processing for Remote Sensing xxIII

作者： Uss, M. Vozel, B. Lukin, V. Chehdi, K. Natl Aerosp Univ Dept Transmitters Receivers & Signal Proc Kharkov Ukraine Univ Rennes 1 IETR UMR CNRS 6164 CS 80518 F-22305 Lannion France

ISBN: (数字)9781510613195

ISBN: (纸本)9781510613195;9781510613188

The processing chain of Sentinel-2 MultiSpectral Instrument (MSI) data involves filtering and compression stages that modify MSI sensor noise. As a result, noise in Sentinel-2 Level-1C data distributed to users becomes processed. We demonstrate that processed noise variance model is bivariate: noise variance depends on image intensity (caused by signal-dependency of photon counting detectors) and signal-to-noise ratio (SNR;caused by filtering/compression). To provide information on processed noise parameters, which is missing in Sentinel-2 metadata, we propose to use blind noise parameter estimation approach. Existing methods are restricted to univariate noise model. Therefore, we propose extension of existing vcNI+fBm blind noise parameter estimation method to multivariate noise model, mvcNI+fBm, and apply it to each band of Sentinel-2A data. Obtained results clearly demonstrate that noise variance is affected by filtering/compression for SNR less than about 15. Processed noise variance is reduced by a factor of 2 - 5 in homogeneous areas as compared to noise variance for high SNR values. Estimate of noise variance model parameters are provided for each Sentinel-2A band. Sentinel-2A MSI Level-1C noise models obtained in this paper could be useful for end users and researchers working in a variety of remote sensing applications.

关键词： remote sensing Sentinel-2 MultiSpectral Instrument blind noise parameter estimation multivariate noise variance model signal-dependency processed noise image denoising image compression

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Denoising processing of MIE-Polarization Lidar Singal using Wavelet

Denoising processing of MIE-Polarization Lidar Singal using ...

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Annual conference of the Chinese-Society-for-Optical-Engineering (CSOE) on Applied Optics and Photonics China (AOPC) - Optical Sensing and Imaging Technology and applications

作者： Yang Hui Zhao xuesong Ye Jiesong Sun Yanfei Wang Tiedong New Star Applicat Technol Inst Hefei 230031 Anhui Peoples R China Chinese Acad Sci Key Lab Environm Opt & Technol AIOFM Hefei 230031 Anhui Peoples R China

ISBN: (纸本)9781510614062;9781510614055

The echo signal of MIE scattering and polarization lidar is contanminated by the noises including background noises and detector noises, there exist obvious difference in fourier spectrums of original signals and denoised signals at high frequency parts. In this paper, a new method of lidar signal denoised by scattering wavelets was proposed, and original PRR signals of lidar were denoised by different wavelet basis. The experimental results showed that: 1) Daubechies 4 wavelet basis (db4) met the requirements of denoising;2) wavelet denoising could better preserve peak features of lidar echo signal than moving average method;3) the original and detailed characteristics of atmospheric aerosol extinction profile was maintained, and the accuracy of atmospheric aerosol extinction coefficient inversion was improved. For lidar echo signals with low SNR, this method improved data processing method of Mie-scattering & polarization lidar and improved the accuracy of the retrieval of atmospheric aerosol extinction coefficient, the signal contamination from electric noise and the background light noise can be reduced by the proposed wavelet method.

关键词： lidar signal wavelet de-noising

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