Descent image based landing area terrain reconstruction technology for lunar landing mission

In this paper, we present a descent image based extraterrestrial terrain reconstruction technology, which has been successfully applied in China's first soft landing Chang'E-3 mission on moon. When scouting on the moon surface, the lunar rover (Jade Rabbit rover) requires reliable terrain information for navigation and positioning tasks. The descent image sequence acts as a link between the orbital images and the surface images. The proposed approach collects multilevel descent images, as well as inertial information from an inertial measurement unit and surface stereo vision information, to generalise a consistent surface model. A data fusion strategy is proposed to track the camera trajectory, and refined plane sweep stereo matching and 3D alignment technologies are analysed in this article. The onboard result, as well as on-earth simulation experiments, has shown that the discussed techniques are reliable to reconstruct the terrain information for the lunar rover's localisation and navigation.     

向小天体着陆的自主导航研究

研究了空间飞行器向小天体着陆阶段基于导航相机和激光测距仪的自主光学导航方案。采用扩展卡尔曼滤波方法设计了导航滤波器。利用数值仿真验证了其系统性能，并着重分析了测量误差、初始状态误差、参数误差等各种误差源对导航滤波器的影响。仿真表明滤波器的性能对目标天体的自旋角速度误差较为敏感。     

Hybrid sampling approach for imaging Fourier-transform spectrometry

A new approach to interferogram sampling is demonstrated for Fourier-transform spectrometry. Sampling of the infrared channel is triggered at equidistant optical path differences while samples are time-referenced with a high-resolution digital clock. This hybrid method exploits the advantages of both time and position-sampling techniques. It minimizes the dataload while allowing a postcorrection scheme to remove sampling errors. It is therefore highly adapted to imaging spectrometers designed for massively parallel spatial sampling. Also, this technique is particularly interesting for spectrometers equipped with an integrating detector, such as a CCD camera, since it can account for the inevitable delay caused by camera integration.     

Penalized-likelihood image reconstruction for digital holography

Conventional numerical reconstruction for digital holography using a filter applied in the spatial-frequency domain to extract the primary image may yield suboptimal image quality because of the loss in high-frequency components and interference from other undesirable terms of a hologram. We propose a new numerical reconstruction approach using a statistical technique. This approach reconstructs the complex field of the object from the real-valued hologram intensity data. Because holographic image reconstruction is an ill-posed problem, our statistical technique is based on penalized-likelihood estimation. We develop a Poisson statistical model for this problem and derive an optimization transfer algorithm that monotonically decreases the cost function at each iteration. Simulation results show that our statistical technique has the potential to improve image quality in digital holography relative to conventional reconstruction techniques.     

Experimental Study of an Intensity Modulated Fiber-Optic Position Sensor With a Novel Readout System

An intensity modulated fiber-optic position sensor, based on a fiber to bundle coupling and a readout system using a CMOS image camera together with fast routines for position extraction and calibration, is experimentally demonstrated. Further, a simulation of the complete sensor system setup is made, and possible sources of error are analyzed experimentally. Our analysis indicates the theoretical limit of the system and identifies the causes of system errors. The results are encouraging for further development of the system setup.     

Efficient fringe image enhancement based on dual-tree complex wavelet transform

In optical phase shift profilometry (PSP), parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest in real time three-dimensional (3D) modeling applications because it enables 3D reconstruction using just a few image captures. When using this approach in a real life environment, however, the noise in the captured images can greatly affect the quality of the reconstructed 3D model. In this paper, a new image enhancement algorithm based on the oriented two-dimenional dual-tree complex wavelet transform (DT-CWT) is proposed for denoising the captured fringe images. The proposed algorithm makes use of the special analytic property of DT-CWT to obtain a sparse representation of the fringe image. Based on the sparse representation, a new iterative regularization procedure is applied for enhancing the noisy fringe image. The new approach introduces an additional preprocessing step to improve the initial guess of the iterative algorithm. Compared with the traditional image enhancement techniques, the proposed algorithm achieves a further improvement of 7.2 dB on average in the signal-to-noise ratio (SNR). When applying the proposed algorithm to optical PSP, the new approach enables the reconstruction of 3D models with improved accuracy from 6 to 20 dB in the SNR over the traditional approaches if the fringe images are noisy.     

基于未校准照片构造全景图的新方法

提出一种基于未校准的系列照片构造全景图纳新方法，该组照片可采用普通三脚架/数码相机在固定地点拍摄。采用普通三角架通过旋转角度拍摄的照片难以保证轴心一致性，可能出现图像扭曲、交叠和倾斜，照片之间可能有一定色差，因此，在图像的拼接和建立全景图方面难度大。文中采用特征匹配技术实现照片的拼接，通过照片的定位拼接、相邻照片颜色亮度的协调实现全景图的构建。该算法所生成的全景图效果好，计算速度快，巳得到实际应用。它可广泛应用于Web环境下的基于图像的虚拟场景构造等方面。     

Demonstration of a single-wavelength spectral-imaging-based Thai jasmine rice identification

A single-wavelength spectral-imaging-based Thai jasmine rice breed identification is demonstrated. Our nondestructive identification approach relies on a combination of fluorescent imaging and simple image processing techniques. Especially, we apply simple image thresholding, blob filtering, and image subtracting processes to either a 545 or a 575 nm image in order to identify our desired Thai jasmine rice breed from others. Other key advantages include no waste product and fast identification time. In our demonstration, UVC light is used as our exciting light, a liquid crystal tunable optical filter is used as our wavelength seclector, and a digital camera with 640 active pixels × 480 active pixels is used to capture the desired spectral image. Eight Thai rice breeds having similar size and shape are tested. Our experimental proof of concept shows that by suitably applying image thresholding, blob filtering, and image subtracting processes to the selected fluorescent image, the Thai jasmine rice breed can be identified with measured false acceptance rates of <22.9% and <25.7% for spectral images at 545 and 575 nm wavelengths, respectively. A measured fast identification time is 25 ms, showing high potential for real-time applications.     

Three-dimensional remote sensing by optical scanning holography

A technique is presented by which holograms can be recorded when an object or scene is scanned with an optically heterodyned Fresnel zone pattern. The experimental setup, based on optical scanning holography, is described and experimental results are presented. We apply the scanning holography technique to three-dimensional reflective objects for the first time to our knowledge and address the unique requirements for such a system. We discuss holographic recording and numerical image reconstruction using a system point-spread function (PSF) approach. We demonstrate numerical image reconstruction of experimentally recorded holograms by two techniques: deconvolution with a simulated PSF and an experimentally acquired PSF.     

Shape connection by pattern recognition and laser metrology

Shape connection based on the pattern recognition of three-dimensional shapes is presented. In this technique, the object shape is reconstructed by laser scanning and image processing. The object is reconstructed from multiple views when an object occlusion appears. From this process, multiple parts of the object are reconstructed. Then, these parts are assembled to obtain the complete object shape. To perform the assembling, a matching procedure is applied to a transverse section of the multiple views by Hu moments. The depth of the transverse section is computed by an approximation network based on the behavior of the laser line and the camera position. Also, vision parameters are deduced by the network and image processing. In this manner, the shape connection is achieved automatically by computational algorithms. Therefore, errors of physical measurement are not passed to the reconstruction system. Thus, the performance and the accuracy of the reconstruction system are improved. This is elucidated by the comparison between the obtained results by the proposed technique and the obtained results by a contact method. Thus, a contribution in laser metrology for shape connection is achieved.     

Multiple‐view Shape and Deformation Measurement by Combining Fringe Projection and Digital Image Correlation

Abstract: We present a new method that combines the fringe projection and the digital image correlation (DIC) techniques on a single hardware platform to simultaneously measure both shape and deformation fields of three‐dimensional (3‐D) surfaces with complex geometries. The method in its basic form requires only a single camera and single projector, but this can be easily extended to a multi‐camera multi‐projector system to obtain complete 360° measurements. Multiple views of the surface profile and displacement field are automatically co‐registered in a unified global coordinate system, thereby avoiding the significant errors that can arise through the use of statistical point cloud stitching techniques. Experimental results from a two‐camera two‐projector sensor are presented and compared with results from both a standard stereo‐DIC approach and a finite element model.     

Metrological aspects of image analysis

A universal approach to the detection of drawbacks in techniques used to perform measurements based on the use of systems for automatic image analysis is considered. A regression model that makes it possible to estimate the contribution of errors characterizing the photography conditions and camera setting adjustment is constructed. The accuracy indicators (correctness and precision indicators) are estimated in accordance with an existing standard. __________ Translated from Izmeritel’naya Tekhnika, No. 2, pp. 25–28, February, 2008.     

High-accuracy three-dimensional shape acquisition of a large-scale object from multiple uncalibrated camera views

Acquiring a high-accuracy three-dimensional (3D) shape of a large-scale object from multiple uncalibrated camera views remains a big challenge, since a considerable number of images is required to cover the entire surface; the use of multiple images could, however, result in accumulative errors from each processed image. Here error propagation rules in the 3D reconstruction process have been deduced on the basis of the traditional dual-view reconstruction method. We propose a method that can control the accumulative errors by reducing the times of coordinate transformation with common-view-based dual-view reconstruction. This method involves constructing an image network composed of many image groups, each of which contains a common view. A baseline threshold method is introduced to construct a high-quality image network, and the sums or reprojection residual of all the common points is proposed to assess the validity of the solutions of the orientation. Experiments carried out with both synthetic and real images demonstrate that the proposed method can handle the accumulative error problem with robust and highly accurate results.     

Colour filter array demosaicking over compression through modified grey wolf optimization technique

The three colour intensity values red, green and blue are acquired in a single pixel on using a multi-sensor camera. The single sensor camera uses the colour filter array to grab only one intensity value in a pixel location. The reconstruction of the so-called raw image into a full colour image is called demosaicking. This paper proposes a novel framework for effective demosaicking combined with the compression over the input image using a modified grey wolf optimization algorithm. The demosaicking process is efficient in applying the Modified Grey Wolf Optimized wavelet compression to optimally select the coefficients and Modified Huffman Coding technique to the image before demosaicking. The proposed technique is evaluated by the Peak Signal Noise Ratio, Structural Similarity Index Measure and Feature Similarity Index Measure values and has proved both subjectively and objectively to have better demosaicking results.     

A new instrument to measure the shape of the cornea based onpseudorandom color coding

We have developed a prototype instrument providing a new way to measure the shape of the cornea. Our approach exploits properties of pseudorandom binary arrays (PRBAs). Encoded in a colored pattern (stimulus) that is mirrored to the eye, a large PRBA allows for unique identification of unambiguously characterized positions both in stimulus and reflected image. This is used to come to an integral reconstruction of the cornea. It is demonstrated that the new technique contributes to a very robust measurement method     

Three dimension reconstruction through measure-based image selection

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.     

Quantitative evaluation technique of Polyvinyl Alcohol (PVA) fiber dispersion in engineered cementitious composites

The fiber dispersion in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion in the composite Polyvinyl Alcohol-Engineered Cementitious Composite (PVA-ECC) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, a new evaluation technique is developed and demonstrated. Using a fluorescence technique on PVA-ECC, PVA fibers are observed as green dots in the cutting plane of the composite. After capturing the fluorescence image with a Charged Couple Device (CCD) camera through a microscope, the fiber dispersion is evaluated using image processing and statistical tools. In the image processing step, the fibers are more accurately detected by employing a series of processes based on categorization, watershed segmentation, and morphological reconstruction. Test results showed that the dispersion coefficient α_f was calculated reasonably and the fiber-detection performance was enhanced.     

Thin infrared imaging systems through multichannel sampling

The size of infrared camera systems can be reduced by collecting low-resolution images in parallel with multiple narrow-aperture lenses rather than collecting a single high-resolution image with one wide-aperture lens. We describe an infrared imaging system that uses a three-by-three lenslet array with an optical system length of 2.3 mm and achieves Rayleigh criteria resolution comparable with a conventional single-lens system with an optical system length of 26 mm. The high-resolution final image generated by this system is reconstructed from the low-resolution images gathered by each lenslet. This is accomplished using superresolution reconstruction algorithms based on linear and nonlinear interpolation algorithms. Two implementations of the ultrathin camera are demonstrated and their performances are compared with that of a conventional infrared camera.     

AHSPP Annual Conference

Abstract

A calibration and three-dimensional (3D) reconstruction method is presented based on images reflected from planar mirrors and acquired with one camera. The geometric model of the camera–mirror set and a method of calibrating it are described. The calibration technique computes the model parameters using linear equations, and it is proved that the calibration is possible with the knowledge of only six 3D points. The reconstruction method is based on a volumetric representation. The 3D reconstruction is based on a space carving algorithm and the calibration method described in the paper. The results of the calibration and reconstruction method show the efficiency of both techniques. This set-up enables a simple and inexpensive multi-ocular system to be built to recover the 3D structure of volumes.     

HJ-1B卫星红外扫描图像的几何定位和精校正方法研究

根据环境与灾害监测预报小卫星1B星(HJ-1B)携带的红外扫描相机的成像几何特性,提出了一种基于成像过程模拟的几何定位和精纠正方法.该方法首先根据共线方程对HJ-1B红外相机的图像初定位,再基于一幅更高分辨率的已校正的遥感图像,按照红外相机的成像几何模拟生成若干图像块,与待纠正的红外相机图像匹配,用最小二乘修正偏移量,最后根据修正量去校正有误差的星历参数,从而获得整幅图像的精确定位信息.经过模拟数据和MODIS数据的检验,所提出的算法是可行的,定位精度有明显的提高.