303 research outputs found

    Calibrating a photogrammetric digital frame sensor using a test field

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    In this paper a twofold calibration approach for a digital frame sensor has been developed which tries to cope with panchromatic and multispectral calibration separately. Although there have been several improvements and developments in calibration of the digital frame sensor, only limited progresses has been made in the context of multispectral image calibration. To this end, a specific photogrammetric flight was executed to try to calibrate the geometric parameters of a large format aerial digital camera. This photogrammetric flight was performed in the "Principado de Asturias" and it has been designed with a Ground Sample Distance of 6 cm, formed by two strips perpendicular between each other, with five images each one and a longitudinal overlap of 60%. Numerous points have been presignalled over the ground, both check points and control points.En este artículo se presenta un doble enfoque para la calibración de una cámara digital matricial y que trata la calibración pancromática y multiespectral por separado. Aunque ha habido varias mejoras y novedades en la calibración las cámaras digitales matriciales, sólo se han hecho limitados progresos en el contexto de la calibración de imágenes multiespectrales. Con este fin, fue realizado un vuelo fotogramétrico específico para tratar de hacer la calibración de los parámetros geométricos de una cámara aérea digital de gran formato. Este vuelo fotogramétrico se realizó en el "Principado de Asturias", y ha sido diseñado con un tamaño de píxel en el terreno de 6 cm, formado por dos pasadas perpendiculares entre sí, con cinco imágenes cada una y un recubrimiento longitudinal de 60%. Se han tomado numerosos puntos preseñalizados sobre el terreno, tanto para los puntos de control como para los puntos de chequeo

    Affine multi-view modelling for close range object measurement

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    In photogrammetry, sensor modelling with 3D point estimation is a fundamental topic of research. Perspective frame cameras offer the mathematical basis for close range modelling approaches. The norm is to employ robust bundle adjustments for simultaneous parameter estimation and 3D object measurement. In 2D to 3D modelling strategies image resolution, scale, sampling and geometric distortion are prior factors. Non-conventional image geometries that implement uncalibrated cameras are established in computer vision approaches; these aim for fast solutions at the expense of precision. The projective camera is defined in homogeneous terms and linear algorithms are employed. An attractive sensor model disembodied from projective distortions is the affine. Affine modelling has been studied in the contexts of geometry recovery, feature detection and texturing in vision, however multi-view approaches for precise object measurement are not yet widely available. This project investigates affine multi-view modelling from a photogrammetric standpoint. A new affine bundle adjustment system has been developed for point-based data observed in close range image networks. The system allows calibration, orientation and 3D point estimation. It is processed as a least squares solution with high redundancy providing statistical analysis. Starting values are recovered from a combination of implicit perspective and explicit affine approaches. System development focuses on retrieval of orientation parameters, 3D point coordinates and internal calibration with definition of system datum, sensor scale and radial lens distortion. Algorithm development is supported with method description by simulation. Initialization and implementation are evaluated with the statistical indicators, algorithm convergence and correlation of parameters. Object space is assessed with evaluation of the 3D point correlation coefficients and error ellipsoids. Sensor scale is checked with comparison of camera systems utilizing quality and accuracy metrics. For independent method evaluation, testing is implemented over a perspective bundle adjustment tool with similar indicators. Test datasets are initialized from precise reference image networks. Real affine image networks are acquired with an optical system (~1M pixel CCD cameras with 0.16x telecentric lens). Analysis of tests ascertains that the affine method results in an RMS image misclosure at a sub-pixel level and precisions of a few tenths of microns in object space

    Bio-Inspired Multi-Spectral and Polarization Imaging Sensors for Image-Guided Surgery

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    Image-guided surgery (IGS) can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky, costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities to distinguish cancerous from healthy tissue. In my thesis, I have addressed these challenges in IGC by mimicking the visual systems of several animals to construct low power, compact and highly sensitive multi-spectral and color-polarization sensors. I have realized single-chip multi-spectral imagers with 1000-fold higher sensitivity and 7-fold better spatial co-registration accuracy compared to clinical imaging systems in current use by monolithically integrating spectral tapetal and polarization filters with an array of vertically stacked photodetectors. These imaging sensors yield the unique capabilities of imaging simultaneously color, polarization, and multiple fluorophores for near-infrared fluorescence imaging. Preclinical and clinical data demonstrate seamless integration of this technologies in the surgical work flow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes, respectively. Due to its low cost, the bio-inspired sensors will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care

    Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing

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    Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen

    Guidelines for Best Practice and Quality Checking of Ortho Imagery

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    For almost 10 years JRC's ¿Guidelines for Best Practice and Quality Control of Ortho Imagery¿ has served as a reference document for the production of orthoimagery not only for the purposes of CAP but also for many medium-to-large scale photogrammetric applications. The aim is to provide the European Commission and the remote sensing user community with a general framework of the best approaches for quality checking of orthorectified remotely sensed imagery, and the expected best practice, required to achieve good results. Since the last major revision (2003) the document was regularly updated in order to include state-of-the-art technologies. The major revision of the document was initiated last year in order to consolidate the information that was introduced to the document in the last five years. Following the internal discussion and the outcomes of the meeting with an expert panel it was decided to adopt as possible a process-based structure instead of a more sensor-based used before and also to keep the document as much generic as possible by focusing on the core aspects of the photogrammetric process. Additionally to any structural changes in the document new information was introduced mainly concerned with image resolution and radiometry, digital airborne sensors, data fusion, mosaicking and data compression. The Guidelines of best practice is used as the base for our work on the definition of technical specifications for the orthoimagery. The scope is to establish a core set of measures to ensure sufficient image quality for the purposes of CAP and particularly for the Land Parcel Identification System (PLIS), and also to define the set of metadata necessary for data documentation and overall job tracking.JRC.G.3-Agricultur

    Calibrating a photogrammetric digital frame sensor using a test field

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    In this paper a twofold calibration approach for a digital frame sensor has been developed which tries to cope with panchromatic and multispectral calibration separately. Although there have been several improvements and developments in calibration of the digital frame sensor, only limited progresses has been made in the context of multispectral image calibration. To this end, a specific photogrammetric flight was executed to try to calibrate the geometric parameters of a large format aerial digital camera. This photogrammetric flight was performed in the “Principado de Asturias” and it has been designed with a Ground Sample Distance of 6 cm, formed by two strips perpendicular between each other, with five images each one and a longitudinal overlap of 60%. Numerous points have been presignalled over the ground, both check points and control points

    Geometric Accuracy Testing, Evaluation and Applicability of Space Imagery to the Small Scale Topographic Mapping of the Sudan

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    The geometric accuracy, interpretabilty and the applicability of using space imagery for the production of small-scale topographic maps of the Sudan have been assessed. Two test areas have been selected. The first test area was selected in the central Sudan including the area between the Blue Nile and the White Nile and extending to Atbara in the Nile Province. The second test area was selected in the Red Sea Hills area which has modern 1:100,000 scale topographic map coverage and has been covered by six types of images, Landsat MSS TM and RBV; MOMS; Metric Camera (MC); and Large format Camera (LFC). Geometric accuracy testing has been carried out using a test field of well-defined control points whose terrain coordinates have been obtained from the existing maps. The same points were measured on each of the images in a Zeiss Jena Stereocomparator (Stecometer C II) and transformed into the terrain coordinate system using polynomial transformations in the case of the scanner and RBV images; and space resection/intersection, relative/absolute orientation and bundle adjustment in the case of the MC and LFC photographs. The two sets of coordinates were then compared. The planimetric accuracies (root mean square errors) obtained for the scanner and RBV images were: Landsat MSS +/-80 m; TM +/-45 m; REV +/-40 m; and MOMS +/-28 m. The accuracies of the 3-dimensional coordinates obtained from the photographs were: MC:-X=+/-16 m, Y=+/-16 m, Z=+/-30 m; and LFC:- X=+/-14 m, Y=+/-14 m, and Z=+/-20 m. The planimetric accuracy figures are compatible with the specifications for topographic maps at scales of 1:250,000 in the case of MSS; 1:125,000 scale in the case of TM and RBV; and 1:100,000 scale in the case of MOMS. The planimetric accuracies (vector =+/-20 m) achieved with the two space cameras are compatible with topographic mapping at 1:60,000 to 1:70,000 scale. However, the spot height accuracies of +/-20 to +/-30 m - equivalent to a contour interval of 50 to 60 m - fall short of the required heighting accuracies for 1:60,000 to 1:100,000 scale mapping. The interpretation tests carried out on the MSS, TM, and RBV images showed that, while the main terrain features (hills, ridges, wadis, etc.) can be mapped reasonably well, there was an almost complete failure to pick up the cultural features - towns, villages, roads, railways, etc. - present in the test areas. The high resolution MOMS images and the space photographs were much more satisfactory in this respect though still the cultural features are difficult to pick up due to the buildings and roads being built out of local material and exhibiting little contrast on the images

    Determination of the Interior Orientation Parameters of a Non-metric Digital Camera for Terrestrial Photogrammetric Applications

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    AbstractHigh cost of metric photogrammetric cameras has given rise to the utilisation of non-metric digital cameras to generate photogrammetric products in traditional close range or terrestrial photogrammetric applications. For precision photogrammetric applications, the internal metric characteristics of the camera, customarily known as the Interior Orientation Parameters, need to be determined and analysed. The derivation of these parameters is usually achieved by implementing a bundle adjustment with self-calibration procedure. The stability of the Interior Orientation Parameters is an issue in terms of accuracy in digital cameras since they are not built with photogrammetric applications in mind. This study utilised two photogrammetric software (i.e. Photo Modeler and Australis) to calibrate a non-metric digital camera to determine its Interior Orientation Parameters. The camera parameters were obtained using the two software and the Root Mean Square Errors (RMSE) calculated. It was observed that Australis gave a RMSE of 0.2435 and Photo Modeler gave 0.2335, implying that, the calibrated non-metric digital camera is suitable for high precision terrestrial photogrammetric projects. Keywords: Camera Calibration, Interior Orientation Parameters, Non-Metric Digital Camer

    Optical and hyperspectral image analysis for image-guided surgery

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