Virtual viewpoint three-dimensional panorama

Conventional panoramic images are known to provide for an enhanced field of view in which the scene always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual viewpoint creation to generate different panoramic images of the same scene with three-dimensional component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of panoramic images

Face tracking using a hyperbolic catadioptric omnidirectional system

In the first part of this paper, we present a brief review on catadioptric omnidirectional systems. The special case of the hyperbolic omnidirectional system is analysed in depth. The literature shows that a hyperboloidal mirror has two clear advantages over alternative geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the image resolution is uniformly distributed along the mirror’s radius [2]. In the second part of this paper we show empirical results for the detection and tracking of faces from the omnidirectional images using Viola-Jones method. Both panoramic and perspective projections, extracted from the omnidirectional image, were used for that purpose. The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection of the image. In order to avoid losing or duplicating detections, the panoramic projection was extended horizontally. Duplications were eliminated based on the ROIs established by previous detections. After a confirmed detection, faces were tracked from perspective projections (which are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt of each virtual camera was determined by the ROIs previously computed on the panoramic image. The results show that, when using a careful combination of the two projections, good frame rates can be achieved in the task of tracking faces reliably

3D modeling of indoor environments by a mobile platform with a laser scanner and panoramic camera

One major challenge of 3DTV is content acquisition. Here, we present a method to acquire a realistic, visually convincing D model of indoor environments based on a mobile platform that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the simultaneous lo- calization and mapping problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the gen- erated textures. The scene is further enriched by 3D-geometry cal- culated from a graph cut stereo technique. We present experimental results from a moderately large real environment.

Panoramic Depth Imaging: Single Standard Camera Approach

In this paper we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a setoff of the camera’s optical center from the rotational center of the system we are able to capture the motion parallax effect which enables stereo reconstruction. The camera is rotating on a circular path with a step defined by the angle, equivalent to one pixel column of the captured image. The equation for depth estimation can be easily extracted from the system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric pixel columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. The search space on the epipolar line can be additionaly constrained. The focus of the paper is mainly on the system analysis. Results of the stereo reconstruction procedure and quality evaluation of generated depth images are quite promissing. The system performs well for reconstruction of small indoor spaces. Our finall goal is to develop a system for automatic navigation of a mobile robot in a room

Endoscopic measurements using a panoramic annular lens

The objective of this project was to design, build, demonstrate, and deliver a prototype system for making measurements within cavities. The system was to utilize structured lighting as the means for making measurements and was to rely on a stationary probe, equipped with a unique panoramic annular lens, to capture a cylindrical view of the illuminated cavity. Panoramic images, acquired with a digitizing camera and stored in a desk top computer, were to be linearized and analyzed by mouse-driven interactive software

Basic knowledge and new advances in panoramic radiography imaging techniques: A narrative review on what dentists and radiologists should know

Objectives: A panoramic radiograph (PAN) is the most frequently diagnostic imaging technique carried out in dentistry and oral surgery. The correct performance of image acquisition is crucial to obtain adequate image quality. The aim of the present study is to (i) review the principles of PAN image acquisition and (ii) describe positioning errors and artefacts that may affect PAN image quality. Methods: Articles regarding PAN acquisition principles, patient’s positioning errors, artefacts, and image quality were retrieved from the literature. Results: Head orientation is of the utmost importance in guaranteeing correct image acquisition. Symmetry, occlusal plane inclination, mandibular condyles localization, cervical spine position, aspect of upper teeth root apexes, exposure parameters, and metal and motion artefacts are factors that greatly affect the image quality of a successful PAN. Conclusions: Several factors are the basis for PAN performance; therefore, a systematic approach that takes into account correct patient positioning and preparation is strongly suggested to improve overall examination quality

Capturing Panoramic Depth Images with a Single Standard Camera

In this paper we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a setoff of the camera’s optical center from the rotational center of the system we are able to capture the motion parallax effect which enables the stereo reconstruction. The camera is rotating on a circular path with the step defined by an angle equivalent to one column of the captured image. The equation for depth estimation can be easily extracted from system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. We focused mainly on the system analysis. The system performs well in the reconstruction of small indoor spaces

Towards Accurate Camera Geopositioning by Image Matching

In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90% for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50% at the cost of only a small (approximately 3%) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20%