Common Coordinate System Research Articles

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

V4. Focal slow wave in patients after epilepsy surgery with and without seizure recurrence

Background Recurrence of seizure after epilepsy surgery occurs in approx. 40% of operated patients ( Rosenow and Luders, 2001 ). Due to skull defects, the resection volume and scarification, reevaluation for potential epilepsy surgery is challenging. Conductivity differences caused by such alterations has an almost negligible effect on magnetoencephalography (MEG). MEG may thus provide valuable localizing information in this patient group. In addition to epileptic spikes, focal slow wave activity in the delta and theta frequency range have been utilized for epileptic focus localizations in patients without previous surgery only ( Kaltenhauser et al., 2007 ). In the presented study, we use an automatic procedure to investigate focal delta in patients with and without recurrent seizures after surgery to differentiate delta related to epilepsy from delta caused by surgery. Patients and methods We investigated 15 patients with recurrent seizures and 10 seizure free patients after surgery, as well as 15 healthy controls. MEG was recorded for 10 min in supine positions using a WHS3600 magnetometer system (4D Neuroimaging, San Diego, USA) with 248 channels. Distribution of delta activity (1–4 Hz) in source space was calculated (DICS) using the Fieldtrip ( Oostenveld et al., 2011 ) Matlab toolbox (The Mathworks, Natick, CA, USA). Source activity was morphed onto a common coordinate system (MNI). Delta activity in patients was then related to healthy controls by calculating location specific z-values. Results Both patient groups showed significantly increased delta activity compared to healthy controls (p 3) in all patients. Conclusions In patients with recurrent seizures after epilepsy surgery, focal delta may be utilized for epileptic focus localization and complement MEG spike analysis. Furthermore, the significant difference between patients with and without recurrent seizures indicates a potential role of focal delta activity in the relapse of epilepsy.

Automatic Registration of Satellite Images

Registration is a fundamental stage in 3D and 2D reconstruction process, which is used to match two or more images that can be taken in different moments, from different sensors, or different viewpoints. Two images are aligned into a common coordinate system in order to monitor the subtle changes between them. Registration algorithms compute transformations to set correspondence between the two images. . This approach provides a good basis for classifying different techniques, however it does little to illuminate the conditions important to the problem of registration. Understanding the image registration problem space and the conditions under which specific algorithms perform best allows for further automation of image registration, and makes the field more accessible to non-experts. This paper presents manual and automatic registration and their comparison is made.

SU‐E‐J‐171: The Dosimetric Consequence of a Failed Breath Hold for Left‐Sided DIBH Breast Treatment

Purpose:The purpose of this work is to determine the dosimetric consequence of a failed breath hold which may arise out of failure to prompt the patient before initiating treatment or lack of compliance from the patient in taking or maintaining the breath hold. In this context, the patient is assumed to be aligned properly for treatment to the breath hold surface.Methods:Ten left‐sided breast cancer patients were simulated for treatment in the free‐breathing (FB‐CT) and deep‐inspiration breath hold (DIBH‐CT) positions using a bellows device to monitor respiration. The CT scans were fused using a common rigid coordinate system in the underlying couch allowing the analysis of the identical setup position for either FB‐CT or DIBH‐CT. The breast tissue, lumpectomy cavity, heart, and lungs were contoured on each CT. Patients were planned for treatment to 50Gy using a tangent field‐in‐field technique on the DIBH CT scan. The treatment plan was subsequently copied to the free‐breathing scan in the same geometric position and recalculated.Results:No statistically significant dose difference was found for the heart (V30, V20, mean, and maximum) or lumpectomy cavity (mean, maximum, minimum, and V95). However, in two cases, the lumpectomy cavity V95 was 20% less for FB‐CT. The lung V20 decreased by an average of 3.7±1.0% (p=0.004) for FB‐CT. No statistically significant dose difference was found for the mean and maximum breast dose. The minimum breast dose (16.9±3.4 for DIBH) decreased by an average of 11±3.4% (p=0.0001), and the breast V95 (84.7±2.0% for DIBH) decreased by an average of 13.9±7.2% (p=0.003).Conclusion:Our results show that a failed breath hold would not negatively affect the surrounding organs and increased lung sparing. In our study, the lumpectomy cavity remained satisfactorily targeted in 8 out of 10 cases. However, the minimum and V95 breast doses decreased considerably.

TU‐G‐BRA‐03: Predicting Radiation Therapy Induced Ventilation Changes Using 4DCT Jacobian Calculations

Purpose:Longitudinal changes in lung ventilation following radiation therapy can be mapped using four‐dimensional computed tomography(4DCT) and image registration. This study aimed to predict ventilation changes caused by radiation therapy(RT) as a function of pre‐RT ventilation and delivered dose.Methods:4DCT images were acquired before and 3 months after radiation therapy for 13 subjects. Jacobian ventilation maps were calculated from the 4DCT images, warped to a common coordinate system, and a Jacobian ratio map was computed voxel‐by‐voxel as the ratio of post‐RT to pre‐RT Jacobian calculations. A leave‐one‐out method was used to build a response model for each subject: post‐RT to pre‐RT Jacobian ratio data and dose distributions of 12 subjects were applied to the subject's pre‐RT Jacobian map to predict the post‐RT Jacobian. The predicted Jacobian map was compared to the actual post‐RT Jacobian map to evaluate efficacy. Within this cohort, 8 subjects had repeat pre‐RT scans that were compared as a reference for no ventilation change. Maps were compared using gamma pass rate criteria of 2mm distance‐to‐agreement and 6% ventilation difference. Gamma pass rates were compared using paired t‐tests to determine significant differences. Further analysis masked non‐radiation induced changes by excluding voxels below specified dose thresholds.Results:Visual inspection demonstrates the predicted post‐RT ventilation map is similar to the actual map in magnitude and distribution. Quantitatively, the percentage of voxels in agreement when excluding voxels receiving below specified doses are: 74%/20Gy, 73%/10Gy, 73%/5Gy, and 71%/0Gy. By comparison, repeat scans produced 73% of voxels within the 6%/2mm criteria. The agreement of the actual post‐RT maps with the predicted maps was significantly better than agreement with pre‐RT maps (p<0.02).Conclusion:This work validates that significant changes to ventilation post‐RT can be predicted. The differences between the predicted and actual outcome are similar to differences between repeat scans with equivalent ventilation.This work was supported by NIH grant CA166703 and a Pilot Grant from University of Iowa Carver College of Medicine

Novel automated methods for coarse and fine registrations of point clouds in high precision metrology

Several measuring systems can be combined to perform accurate assessments at the sub-micrometer level in dimensional metrology. The obtained data are fused into a common coordinate system using registration methods for which the optimal transformation parameters from the common parts of the data called correspondences are computed. New original automated coarse and fine registration methods are proposed here using discrete curvatures: an improved Hough transformation method for the coarse registration and three iterative closest point (ICP) variants for the fine registration. The enhancement of Hough consists of exploiting the curvature parameters in order to minimize the basic algorithm complexity. Thus, local transformation parameters are only computed for points presenting similar precalculated surface type. While the rough alignment of the scene data and the model data is thereafter optimized through the fine registration using common ICP algorithm, the first ICP variant includes the curvedness and surface type similarity constraints, especially to reduce the searching area during the matching step. For the proposed second ICP variant, correspondences are searched using a specific distance criterion involving curvature feature similarity measure defined from principal curvatures. The third ICP variant combines both point-to-point and point-to-plane minimizations automatically weighted in the objective function, with the use of moving least squares (MLS) surface technique to determine the corresponding point in point-to-point part. The three developed methods are tested on simulated and real data obtained from a computer tomography (CT) system. The results reveal the benefit of the proposed new automated coarse and fine registration approaches.

Incorporating 3-dimensional models in online articles.

The aims of this article are to introduce the capability to view and interact with 3-dimensional (3D) surface models in online publications, and to describe how to prepare surface models for such online 3D visualizations. Three-dimensional image analysis methods include image acquisition, construction of surface models, registration in a common coordinate system, visualization of overlays, and quantification of changes. Cone-beam computed tomography scans were acquired as volumetric images that can be visualized as 3D projected images or used to construct polygonal meshes or surfaces of specific anatomic structures of interest. The anatomic structures of interest in the scans can be labeled with color (3D volumetric label maps), and then the scans are registered in a common coordinate system using a target region as the reference. The registered 3D volumetric label maps can be saved in .obj, .ply, .stl, or .vtk file formats and used for overlays, quantification of differences in each of the 3 planes of space, or color-coded graphic displays of 3D surface distances. All registered 3D surface models in this study were saved in .vtk file format and loaded in the Elsevier 3D viewer. In this study, we describe possible ways to visualize the surface models constructed from cone-beam computed tomography images using 2D and 3D figures. The 3D surface models are available in the article's online version for viewing and downloading using the reader's software of choice. These 3D graphic displays are represented in the print version as 2D snapshots. Overlays and color-coded distance maps can be displayed using the reader's software of choice, allowing graphic assessment of the location and direction of changes or morphologic differences relative to the structure of reference. The interpretation of 3D overlays and quantitative color-coded maps requires basic knowledge of 3D image analysis. When submitting manuscripts, authors can now upload 3D models that will allow readers to interact with or download them. Such interaction with 3D models in online articles now will give readers and authors better understanding and visualization of the results.

Leader election and gathering for asynchronous fat robots without common chirality

This paper proposes a deterministic gathering algorithm for n≥5 autonomous, homogeneous, asynchronous, oblivious unit disc robots (fat robots). The robots do not have common coordinate system and chirality. A robot can sense or observe its surroundings by collecting information about the positions of all the robots. Based on this information, they compute their destinations for moving and move there. Initially, the robots are stationary and separated. Robots are assumed to be transparent but solid. The algorithm for gathering is designed in such a way that the robots do not collide. In order to avoid collision we do not allow all the robots to move at a time. A unique robot, called leader is elected to move to its destination. No other robot moves till the leader reaches its destination. When the leader reaches its destination, another leader is selected from the remaining robots. However, leader election may not be possible in an arbitrary configuration. In this paper, we characterize all geometric configurations where leader election is possible and present an algorithm for leader election in such a case. An important property of our leader election algorithm is that it is possible to elect a leader from the remaining set of robots also.

Physically motivated global alignment method for electron tomography

AbstractElectron tomography is widely used for nanoscale determination of 3-D structures in many areas of science. Determining the 3-D structure of a sample from electron tomography involves three major steps: acquisition of sequence of 2-D projection images of the sample with the electron microscope, alignment of the images to a common coordinate system, and 3-D reconstruction and segmentation of the sample from the aligned image data. The resolution of the 3-D reconstruction is directly influenced by the accuracy of the alignment, and therefore, it is crucial to have a robust and dependable alignment method. In this paper, we develop a new alignment method which avoids the use of markers and instead traces the computed paths of many identifiable ‘local’ center-of-mass points as the sample is rotated. Compared with traditional correlation schemes, the alignment method presented here is resistant to cumulative error observed from correlation techniques, has very rigorous mathematical justification, and is very robust since many points and paths are used, all of which inevitably improves the quality of the reconstruction and confidence in the scientific results.

INTEGRATION OF POINT CLOUDS ORIGINATED FROM LASER SCANER AND PHOTOGRAMMETRIC IMAGES FOR VISUALIZATION OF COMPLEX DETAILS OF HISTORICAL BUILDINGS

Abstract. Three-dimensional (3D) models of historical buildings are created for documentation and virtual realization of them. Laser scanning and photogrammetry are extensively used to perform for these aims. The selection of the method that will be used in threedimensional modelling study depends on the scale and shape of the object, and also applicability of the method. Laser scanners are high cost instruments. However, the cameras are low cost instruments. The off-the-shelf cameras are used for taking the photogrammetric images. The camera is imaging the object details by carrying on hand while the laser scanner makes ground based measurement. Laser scanner collect high density spatial data in a short time from the measurement area. On the other hand, image based 3D (IB3D) measurement uses images to create 3D point cloud data. The image matching and the creation of the point cloud can be done automatically. Historical buildings include more complex details. Thus, all details cannot be measured by terrestrial laser scanner (TLS) due to the blocking the details with each others. Especially, the artefacts which have complex shapes cannot be measured in full details. They cause occlusion on the point cloud model. However it is possible to record photogrammetric images and creation IB3D point cloud for these areas. Thus the occlusion free 3D model is created by the integration of point clouds originated from the TLS and photogrammetric images. In this study, usability of laser scanning in conjunction with image based modelling for creation occlusion free three-dimensional point cloud model of historical building was evaluated. The IB3D point cloud was created in the areas that could not been measured by TLS. Then laser scanning and IB3D point clouds were integrated in the common coordinate system. The registration point clouds were performed with the iterative closest point (ICP) and georeferencing methods. Accuracy of the registration was evaluated by convergency and its standard deviations for the ICP and residuals on the control points for the georeferencing method.

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci.

Georeferencing of ground-based LIDAR data using continuously operating reference stations

Terrestrial laser scanning has been used for various outdoor visualizations such as urban, construction, excavations, and land topography. Since laser scanning data have their own local coordinates in each station, a three-dimensional point cloud model of the object of interest is created in the local coordinate system by the combination of these measurements. For spatial queries and computations, the point cloud and other spatial data should be combined in a common coordinate system. In this study, a terrestrial laser scanner (TLS) and global navigation satellite system (GNSS) receiver were integrated for the registration of the laser scanner measurements into the geodetic coordinate system. Two georeferencing methods based on the continuously operating reference stations in the network Turkey (CORS-TR) were introduced. After the building was modelled by integrating the TLS and the GNSS receiver, the point cloud model that was created was registered to the international terrestrial reference frame. The registration was performed with 0.05 m root mean square error for the two georeferencing methods.

Kinects and human kinetics: A new approach for studying pedestrian behavior

Microscopic pedestrian simulation models can be used to investigate pedestrian movement at the urban block and building model scale. In order to develop, calibrate and validate such microscopic models, highly accurate and detailed data on pedestrian movement and interaction behavior (e.g. collision avoidance) is required. We present a data collection approach for studying pedestrian behavior which uses the increasingly popular low-cost sensor Microsoft Kinect. The Kinect captures both standard camera data and a three-dimensional depth map. Our human detection and tracking algorithm is based on agglomerative clustering of privacy-preserving Kinect depth data captured from an elevated view – in contrast to the lateral view used for gesture recognition in Kinect gaming applications. Our approach transforms local Kinect 3D data to a common world coordinate system in order to obtain human trajectories from multiple Kinects, which allows for a scalable and flexible capturing area. At a testbed with real-world pedestrian traffic we demonstrate that our approach can provide accurate trajectories from three Kinects with a Pedestrian Detection Rate of up to 94% and a Multiple Object Tracking Precision of 4cm. Using a comprehensive dataset of 2674 captured human trajectories we calibrate three variations of the Social Force model. Data for model calibration and validation was recorded without any script and without actors behaving according to scripted situations. Various conditions have been covered in the dataset, such as walking at different densities, walking-stopping-walking, abrupt changes of direction and random movement. The results of our model validations indicate their particular ability to reproduce the observed pedestrian behavior in microscopic simulations.

Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011

Abstract. In this study, we present first results of RAPIDMAP, a project funded by European Union in a framework aiming to foster the cooperation of European countries with Japan in R&D. The main objective of RAPIDMAP is to construct a Decision Support System (DSS) based on remote sensing data and WebGIS technologies, where users can easily access real-time information assisting with disaster analysis. In this paper, we present a case study of the Tohoku Tsunami Disaster 2011. We address two approaches namely change detection based on SAR data and co-registration of optical and SAR satellite images. With respect to SAR data, our efforts are subdivided into three parts: (1) initial coarse change detection for entire area, (2) flood area detection, and (3) linearfeature change detection. The investigations are based on pre- and post-event TerraSAR-X images. In (1), two pre- and post-event TerraSAR-X images are accurately co-registered and radiometrically calibrated. Data are fused in a false-color image that provides a quick and rough overview of potential changes, which is useful for initial decision making and identifying areas worthwhile to be analysed further in more depth. However, a bunch of inevitable false alarms appear within the scene caused by speckle, temporal decorrelation, co-registration inaccuracy and so on. In (2), the post-event TerraSAR-X data are used to extract the flood area by using thresholding and morphological approaches. The validated result indicates that using SAR data combining with suitable morphological approaches is a quick and effective way to detect flood area. Except for usage of SAR data, the false-color image composed of optical images are also used to detect flood area for further exploration in this part. In (3), Curvelet filtering is applied in the difference image of pre- and post-event TerraSAR-X images not only to suppress false alarms of irregular-features, but also to enhance the change signals of linear-features (e.g. buildings) in settlements. Afterwards, thresholding is exploited to extract the linear-feature changes. In rapid mapping of disasters various sensors are often employed, including optical and SAR, since they provide complementary information. Such data needs to be analyzed in an integrated fashion and the results from each dataset should be integrated in a GIS with a common coordinate reference system. Thus, if no orthoimages can be generated, the images should be co-registered employing matching of common features. We present results of co-registration between optical (FORMOSAT-2) and TerraSAR-X images based on different matching methods, and also techniques for detecting and eliminating matching errors.

C13 Mr-based Stereotaxic Standard Brain Atlas Of The Lib chov Minipig

<h3>Background</h3> In the TRACK TgHD minipig study, we aim to investigate characteristics of the transgenic Libéchov minipig as a model for HD. Annual MRI scans are performed, including anatomical, spectroscopic and diffusion weighted imaging (DWI) sequences. To be able to perform quantitative analysis like the fractional anisotropy (FA), a standard brain is needed as target for registration of imaging data. To date, no standard brain atlas for the Libéchov minipig exists. <h3>Aims</h3> To provide a stereotaxic standard brain atlas of the Libéchov minipig that serves primarily as target for registration of diffusion weighted MR images. <h3>Methods/techniques</h3> 36 T1 weighted, sagittal acquired MRI volumes of 32 Libéchov minipigs were transformed into a common coordinate system and skull-stripped with a BET algorithm. The extracted brains were coregistered with a 12 parameter affine registration within multiple iteration steps. To define the anatomical structures, an available MR-based statistical atlas of the Göttinger minipig brain was registered on the averaged standard brain and the different regions were carefully compared. Binary maps of 34 segmented brain structures were created. The T1 weighted images were acquired with a 3T Philips Achieva scanner. <h3>Results/outcome</h3> T1-weighted 3D volumes of brains of 32 minipigs have successfully been acquired. A standard brain template could be created as target for registration. The mapping of structural regions to create a brain atlas is still in progress and will first be presented at the EHDN meeting. <h3>Conclusions</h3> Most quantitative analysis methods for MR images require registration of the images to a standard volume. Such templates already exist for several species, but not for the Libéchov minipig. The resulting brain template serves as registration target for diffusion weighted MR images and is the first step in creating a Libéchov minipig FA template for quantitative analysis of cross sectional and longitudinal DWI data. This study was supported by a grant of the CHDI Foundation.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

SU‐E‐J‐44: Design a Platform and Phantom Model for Photoacoustic Imaging in Combination with CT

Purpose:Our (long‐term) objective is to develop a US manipulator that will provide in situ radiation response and image‐guided therapy for bladder cancer based on photoacoustic molecular imaging.Methods:A platform was devised to provide a reproducible positional frame of reference for targeting anatomic structure between MDCT and US scans, in lieu of CBCT, and to fuse photoacoustic molecular imaging. US and photoacoustic scans are taken of a patient while in the CT scanner and IRMT. Through co‐registration, based on anatomical positions, we identified a common coordinate system to be used in Eclipse. A bladder phantom was constructed to validate anatomical tracking via US and photoacoustic imaging. We tested the platform using phantom model to demonstrate validity once moved from the CT couch to the linear accelerator couch.Results:This platform interlocks with Varian exact couch index points for reproducibility of positioning. Construction from low Z material and sized appropriately to fit in CT/IMRT gantry. Error in conversion from cylindrical coordinates of the manipulator to X, Y, Z coordinates of the treatment couch was less than 1mm. We measured the bladder size in 3 different directions in both Eclipse from the CT and Acuson from US. The error was less than 2mm in all directions. CT and US images were co‐registered in MATLAB. Co‐registration of photoacoustic images is still being developed.Conclusion:For Linear Accelerators without on board imaging, MV portal images are not a viable option for the localization of soft tissue anatomy. We believe our manipulator provides an alternative using US imaging, which will be examined in an upcoming clinical trial. We plan to examine the value of hypoxia guided treatment through photoacoustic imaging during this trial.

3D ear identification based on sparse representation.

Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person’s identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point)-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm.

Coastal erosion as a major sediment supplier to continental shelves: example from the abandoned Old Huanghe (Yellow River) delta

The Old Huanghe (Yellow River) delta in Jiangsu Province, China, has suffered intense erosion since the river mouth changed from the Yellow Sea to the Bohai Sea in 1855, and this erosion has since been the dominant sediment source to the Yellow Sea and East China Sea. To analyze the topographic and bathymetric changes to the Old Huanghe delta area and derive a regional sediment budget, we extracted shoreline positions and bathymetry from charts and maps published since 1875 and georectified these data into a common coordinate system, combining them with extensive single-beam bathymetric survey data acquired between 2002 and 2008 off Jiangsu Province. The results show continuous seabed erosion throughout the 20th century, consisting of the retreat of shoreface morphology in the nearshore zone (mostly <15-m depth) and deepening of the flat seabed offshore (15 to ~20m). Off the abandoned river mouth of the Old Huanghe, historically persistent sandbanks disappeared and other recent sandbanks shrank. Sediment yielded by coastal erosion from depths of <20m has amounted to >790 Mt annually on average for the last 100 years. Of this very large amount of sediment, ~25% is deposited on the offshore slope in the western Yellow Sea, another 20–25% is deposited along the south Jiangsu coast as far as the Changjiang (Yangtze) river delta, and the rest is transported southeastward along the mud belt that crosses the central Yellow Sea to south of Cheju Island in the East China Sea. This amount is an order of magnitude larger than the sediment supply from the modern Huanghe River to the Yellow Sea and also greatly exceeds the present sediment discharge of the Changjiang River.

Extrinsic Calibration of Camera and Rotating LIDAR Using Two Planes

Light detection and ranging (LIDAR) sensors are widely used in robotics. In this paper, we deal with the extrinsic calibration between camera and rotating LIDAR. It is necessary to fuse information of each system into one common coordinate system. LIDAR sensors give dense and accurate depth information than stereo system. But, camera can give more diverse information including brightness distribution and color about scene. We present extrinsic calibration algorithm using two planes where they are configured vertically compared to each other. A LIDAR is rotated using a motor to have full 3D information about the calibration structure. We find the extrinsic parameter of LIDAR with respect to the world on vertical plane using 3D information. Extrinsic parameter of camera with respect to the world can be found using traditional calibration algorithm. Finally, we can compute the extrinsic parameter between camera and LIDAR. Experimental results show the feasibility of presented algorithm.