Accuracy of Reconstruction with Short baseline from a Single-frame Multispectral Camera

In many offshore operations involving deep water or large horizontal ranges, long-baseline acoustic positioning systems are required to obtain the-needed accuracies. A long-baseline system, however, requires a transponder grid calibration procedure that can take from several hours to over a day. In many operations, the time required for calibration will result in sufficient added operating cost to lead to the decision to use a short-baseline system and accept the lower accuracies. This paper describes how the time necessary to deploy and calibrate the transponders can be significantly reduced with a combined ultra short and long-baseline system. A procedure is presented which allows the transponders to be deployed and calibrated in the time previously required for deployment alone. This procedure eliminates the operating costs associated with calibration. Results of an analysis are presented comparing the calibration accuracies obtainable via this new calibration procedure with those obtainable under the more traditional procedures. The results of this analysis indicate that this new calibration method will meet resolution requirements for many offshore applications. INTRODUCTION Acoustic position reference systems currently in use are based on two approaches: the short (and ultra short) baseline systems and the long baseline system. A short-baseline acoustic position reference system is characterized by the use of a single subsea beacon or transponder. Acoustic signals from this subsea unit are received by multiple, widely separated hydrophones mounted on the vessel (Figure la). The direction to the acoustic signal source is calculated from a measurement of the relative pulse arrival times at the hydrophones. Position, in 3-D coordinates, is determined from this direction information and from knowledge of either the depth or slant range to the subsea unit. A variation of the short-baseline concept is the ultra short- baseline or phase-comparison system (Figure 1b). This system determines direction from a measurement of the relative phases of the acoustic signal received by three closely spaced sensors within a single hydrophone assembly. An alternative to the short-baseline concept is the long-baseline system (Figure 1c). This system determines position by measuring the slant range to three or more widely separated reference transponders which have precisely known relative positions on the sea floor. Determining these transponder locations is generally a costly and time-consuming process. The required calibration procedure may take from several hours to more than a day. The long baseline system is generally more accurate than the short-baseline system and permits operation over a wider operating area. In the past, short-baseline systems could satisfy the requirements of most offshore operations. Current offshore operating requirements, however, have broadened dramatically from those of the station-keeping operations of the past to include such operations as pipe laying, submersible tracking, subsea construction, and deep ocean mining. These applications often require a position reference system that can operate accurately in deep water or over a large range of vessel positions. For these requirements to be met, long-baseline systems are often required.

This paper aims to answer the questions whether human height measurement from video is affected by human motion and how accurately human static height can be measured from a video or from a single image frame that captures a walking subject. In this paper, we present a new approach for measuring human height based on the cross ratio. This approach is performed in parallel with the vanishing point based approach for comparison. The accuracy of each approach is examined by comparing the estimated value to the actual static height value which is measured directly. For the height measurement from video, our analyses show that human height varies significantly during human motion and the highest point in a human stride is the most accurate measurement of the static height. We also model the height variation as a sinusoidal pattern to maximize the accuracy of the estimated height. For the height measurement from a single frame, a correction method is developed in which the length of the human torso with human head and the length of the human leg are separately estimated. We conclude that static human height can be measured accurately, even though the variation of human height is significant during human motion.

Utility of 3D Surface Laser Scanning in Brachytherapy of Lip Cancer

Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.

A system for simultaneously measuring surface normal vectors and BTF (bidirectional texture function) profiles of an object is proposed. This system consists of a correlation camera and a spherical scanning light source. The system can compress BTF measurement in one dimension of incidence direction to a single image frame, as well as measure a profile of the BTF under the scanning light source just from a single image frame. Experimental results on an implemented system demonstrate the feasibility of the proposed system.

Traditional trajectory analysis methods are based on time-series multi-frame image data, target shape and other information. But they do not work well for single frame or sparse time-series multi-target image data. In this paper, a simple and effective sparse trajectory analysis method, based on extended perception domain and skeleton extraction, is proposed for multitarget single frame image data. First of all, the convex hull region feature of sparse points is extracted. The second step, these points are connected into different lines. Then, the skeleton of each trajectory and the coordinate points that make up the trajectory is extracted and recorded respectively. Finally, all kinds of curves are classified and connected by the extending perception domain. The experimental results show that the method can extract multitarget trajectories well under time series data and target features free.

In the tasks of pose estimation and behavior analysis in computer vision, conventional models are often constrained by various factors or complex environments (such as multiple targets, small targets, occluded targets, etc.). To address this problem, this paper proposes a symmetric cascaded additive network (MulAG) to improve the accuracy of posture estimation and behavior analysis in complex environments. MulAG consists of two modules, MulA and MulG. The MulA module is designed based on a cascaded symmetric network structure and incorporates the addition operation. MulA extracts the posture spatial features of the target from a single frame image. And, the MulG module is designed based on three continuous GRUs (gated recurrent unit). Based on the MulA, MulG extracts the posture temporal features from the posture spatial features of the moving target and predicts the posture temporal features of the moving target. The paper firstly demonstrates the feasibility of addition operations in pose estimation tasks by comparing with MobileNet-v3 in ablation experiments. Secondly, on the HiEve and CrowdPose datasets, MulA achieves accuracy of 79.6% and 80.4%, respectively, outperforming the PTM model by 12.0% and 21.2%. And detection speed of MulA achieves the best value at 8.6ms, which is 1 times higher than HDGCN. The result demonstrates the effectiveness of MulA in multi-target pose estimation in complex scenes. Finally, on the HDMB-51 and UCF-101 datasets, MulAG achieves accuracy of 74.8% and 86.3%, respectively, outperforming HDGCN by 9.6% and 9.5%. Compared with SKP and GIST, the fps of MulAG (44.8s-1) is improved by 8.2% and 8.9%. These experiments highlight the generalizability and superiority of MulAG in behavior analysis and pose estimation tasks.

Effective measurement of the reflective or transparent surface of an object has always been a disadvantage in laser scanning modeling. We propose a fast and complete three-dimensional (3D) reconstruction method for small static objects using laser scanning and the structure from motion (SFM) algorithm. Meanwhile, a complete reconstruction workflow is designed and a multi-angle 3d reconstruction system is set up. To generate the complete point cloud model of the object, the SFM algorithm is used to reconstruct the surface part of the object, the data for which cannot be obtained by the laser measuring instrument. The experimental results show that this method not only improves the speed, accuracy, integrity, and visual effect of 3D reconstruction of small objects, but also extends the scope of 3D reconstruction of laser measurement.

Integration of multi-modality imaging for accurate 3D reconstruction of human coronary arteries in vivo

The three-dimensional (3D) reconstruction of the coronary artery from angiographic images is crucial for diagnosing and treating coronary artery disease. However, accurate reconstruction is challenging due to the non-simultaneous acquisition of angiographic images and the complex motion patterns of coronary arteries. State-of-the-art methods typically involve a two-stage process: manual selection of corresponding point pairs for spatial geometric calibration, followed by centerline reconstruction. However, overlap and foreshortening in 2D images complicate point selection, often requiring repeated adjustments, and the lack of sufficient point pairs can lead to reconstruction failure. In this paper, we propose a one-stage automatic approach that integrates calibration and 3D centerline reconstruction, eliminating the need for manual calibration. For each angiographic image, we constructed a 3D deformable curve corresponding to the 2D vessel centerline, strictly constrained by the projection lines. Unlike traditional methods that minimize 2D reprojection errors, our approach minimizes the 3D spatial distance between two 3D curves, simultaneously optimizing the spatial transformation and the two deformable 3D curves. The transformation is optimized through iterative curves registration, while the curves are evolved based on a cosine representation method. Both processes occur simultaneously and mutually reinforce each other, resulting in high-precision 3D reconstruction without manual calibration. The proposed approach was validated on 45 phantom and 107 clinical data. The mean space error was $0.085~\pm ~0.085$ mm for phantom data; and the mean reprojection error was $0.060~\pm ~0.027$ mm for clinical data. Results demonstrated that our approach achieves state-of-the-art accuracy while eliminating the need for manual intervention.

In three-dimensional reconstruction of the human spine obtained from stereo-radiographic set-ups (two radiographs or more), it is extremely difficult to identify exactly the same landmarks on all radiographs. The effect of these identification errors was investigated with simulations made on points of known three-dimensional co-ordinates and compared with three-dimensional reconstructions of real spines obtained with the direct linear transformation algorithm. Results showed that radiographic identification errors of up to 2 mm were common, causing reconstruction errors of up to 5 mm. These reconstruction errors may be noticed in the form of geometrical inaccuracies in the graphical representation of three-dimensional reconstructions of the spine. Successive displacements were then imposed on image point co-ordinates to minimise the identification error and increase the reconstruction accuracy. The improvement on the three-dimensional reconstruction results was negligible. Three-dimensional reconstructions obtained from three radiographs were also investigated. They showed slightly more accurate reconstructions than those obtained from two radiographs. However, the increase of X-ray exposure on the patient may not be worthwhile.

Reports on some simple experiments with a pointwise spatiotemporal correlator. The correlator is demonstrated to be potentially useful for edge and corner detection in single image frames, and for generating image motion fields from temporal image sequences. In contrast to previous proposals, the correlations described are computed for single pixels rather than for image windows. This avoids unnecessary smoothing, which for motion field estimation is particularly undesirable across occluding edges. Another novel feature of this work is that the pointwise correlations between a pixel in one image frame and those in an adjoining frame are not computed over a uniform image grid: the grid is finer for smaller image displacements, and coarser for larger ones. >

Endoscope size is a major design constraint that must be managed with the clinical demand for high-quality illumination and imaging. Existing commercial endoscopes most often use an arc lamp to produce bright, incoherent white light, requiring large-area fiber bundles to deliver sufficient illumination power to the sample. Moreover, the power instability of these light sources creates challenges for computer vision applications. We demonstrate an alternative illumination technique using red-green-blue laser light and a data-driven approach to combat the speckle noise that is a byproduct of coherent illumination. We frame the speckle artifact problem as an image-to-image translation task solved using conditional Generative Adversarial Networks (cGANs). To train the network, we acquire images illuminated with a coherent laser diode, with a laser diode source made partially- coherent using a laser speckle reducer, and with an incoherent LED light source as the target domain. We train networks using laser-illuminated endoscopic images of ex-vivo, porcine gastrointestinal tissues, augmented by images of laser-illuminated household and laboratory objects. The network is then benchmarked against state of-the-art optical and image processing speckle reduction methods, achieving an increased peak signal-to-noise ratio (PSNR) of 4.1 db, compared to 0.7 dB using optical speckle reduction, 0.6 dB using median filtering, and 0.5 dB using non-local means. This approach not only allows for endoscopes with smaller, more efficient light sources with extremely short triggering times, but it also enables imaging modalities that require both coherent and incoherent sources, such as combined widefield and speckle ow contrast imaging in a single image frame.

Conventional (Doppler-based) blood flow velocity measurement methods using ultrasound are capable of resolving the axial component (i.e., that aligned with the ultrasound propagation direction) of the blood flow velocity vector. However, these methods are incapable of detecting blood flow in the direction normal to the ultrasound beam. In addition, these methods require repeated pulse-echo interrogation at the same spatial location. A new method has been introduced which estimates the lateral component of blood flow within a single image frame using the observation that the speckle pattern corresponding to blood reflectors (typically red blood cells) stretches (i.e., is smeared) if the blood is moving in the same direction as the electronically-controlled transducer line selection in a 2-D image. The situation is analogous to the observed distortion of a subject photographed with a moving camera. The results of previous research showed a linear relationship between the stretch factor (increase in lateral speckle size) and blood flow velocity. However, errors exist in the estimation when used to measure blood flow velocity. In this paper, the relationship between speckle size and blood flow velocity is investigated further with both simulated flow data and measurements from a blood flow phantom. It can be seen that: 1) when the blood flow velocity is much greater than the scan velocity (spatial rate of A-line acquisition), the velocity will be significantly underestimated because of speckle decorrelation caused by quick blood movement out of the ultrasound beam; 2) modeled flow gradients increase the average estimation error from a range between 1.4% and 4.4%, to a range between 4.4% and 6.8%; and 3) estimation performance in a blood flow phantom with both flow gradients and random motion of scatterers increases the average estimation error to between 6.1% and 7.8%. Initial attempts at a multiple-scan strategy for estimating flow by a least-squares model suggest the possibility of increased accuracy using multiple scan velocities.

A comparative study of two generation partial light intensity imager based on liquid crystal