Actual Camera Research Articles

Spatial characterization of cameras for low-uncertainty radiometric measurements

Radiometrically characterizing a camera means relating the response of every pixel of the camera to the radiance emitted by a light source. In order to accomplish this task, not only the responsivities of the pixels have to be characterized, but, in addition, their non-ideal response (dark response or non-linearity) or the radiant flux impinging on the pixel from outside its corresponding viewing solid angle. An extended uniform source developed at the PTB was used to spatially characterize a camera by determining the parameters involved in a response model. The uniformity of the source was optimized using the actual camera to be characterized, and two different procedures were used to estimate the non-uniformity of the source. These procedures are based on varying the distance from the source to the camera and, in consequence, the field-of-view area, allowing the non-uniformity of the source to be identified as the non-correlated spatial variations in the images.

Camera navigation support in a virtual environment

Abstract Artificial camera navigation is the most ubiquitous and principal interaction task within a virtual environment. Efficient and intuitive virtual scene navigation affects other tasks completion performance. Though many scientists have elaborated invaluable navigation techniques design guidelines, it is still, especially for novice users, the really challenging and demanding process. The user interface input hardware imprecision, interface operation cognitive burden put on users and deficiency of direct mapping between user physical movement and virtual camera navigation evoke discrepancy between user desired and actual camera position and orientation. The provided paper concentrates on the new potential field based camera navigation support method. Originally designed and exploited potential fields support not only collisions solving, but goal profiled attraction and camera manoeuvring as well. It works both in static and dynamic environments. It can be easily boosted by the GPU approach and eventually can be easily adapted for advanced or novice interface users for a miscellaneous navigation task completion.

AUTOMATIC ISOLATION OF BLURRED IMAGES FROM UAV IMAGE SEQUENCES

Abstract. Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated filtering process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. A “shaking table” was used to create images with known blur during a series of laboratory tests. This platform can be moved in one direction by a mathematical function controlled by a defined frequency and amplitude. The shaking table was used to displace a Nikon D80 digital SLR camera with a user defined frequency and amplitude. The actual camera displacement was measured accurately and exposures were synchronized, which provided the opportunity to acquire images with a known blur effect. Acquired images were processed digitally to determine a quantifiable measure of image blur, which has been created by the actual shaking table function. Once determined for a sequence of images, a user defined threshold can be used to differentiate between “blurred” and "acceptable" images. A subsequent step is to establish the effect that blurred images have upon the accuracy of subsequent measurements. Both of these aspects will be discussed in this paper and future work identified.

Follow-Based Forward Obstacle Detection Using Vision Insensitive Feature for Road Cycling

Paper proposes a method for detecting general obstacles on a road by subtracting present and past in road cycling camera images. The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning based methods detect only specific obstacles. To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-road cycling camera image sequences, and then registries road surfaces between the frames. Finally, obstacles are detected by applying image subtraction to the redistricted road surface regions with a vision insensitive feature for robust detection. Experiments were conducted by using several image sequences captured by an actual in-road cycling camera to confirm the effectiveness of the proposed method. The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even with different situations.

Estimating how the dynamic range and noise of the recording cameras affect the quality of digital holograms

This paper discusses how the characteristics of the recording cameras affect the reconstruction quality of digital holograms. Numerical modelling, using the characteristics of actual cameras, shows that the signal-to-noise ratio for diffuse objects is independent of the distance to the object and the form of the object and is determined only by the characteristics of the cameras and the relative area of the object. Quantitative estimates are obtained.

View planning and mesh refinement effects on a semi-automatic three-dimensional photorealistic texture mapping procedure

A novel three-dimensional (3-D) photorealistic texturing process is presented that applies a view-planning and view-sequencing algorithm to the 3-D coarse model to determine a set of best viewing angles for capturing the individual real-world objects/building's images. The best sequence of views will generate sets of visible edges in each view to serve as a guide for camera field shots by either manual adjustment or equipment alignment. The best view tries to cover as many objects/building surfaces as possible in one shot. This will lead to a smaller total number of shots taken for a complete model reconstruction requiring texturing with photo-realistic effects. The direct linear transformation method (DLT) is used for reprojection of 3-D model vertices onto a two-dimensional (2-D) images plane for actual texture mapping. Given this method, the actual camera orientations do not have to be unique and can be set arbitrarily without heavy and expensive positioning equipment. We also present results of a study on the texture-mapping precision as a function of the level of visible mesh subdivision. In addition, the control points selection for the DLT method used for reprojection of 3-D model vertices onto 2-D textured images is also investigated for its effects on mapping precision. By using DLT and perspective projection theories on a coarse model feature points, this technique will allow accurate 3-D texture mapping of refined model meshes of real-world buildings. The novel integration flow of this research not only greatly reduces the human labor and intensive equipment requirements of traditional methods, but also generates a more appealing photo-realistic appearance of reconstructed models, which is useful in many multimedia applications.

Robust Automatic Focus Algorithm for Low Contrast Images Using a New Contrast Measure

Low contrast images, suffering from a lack of sharpness, are easily influenced by noise. As a result, many local false peaks may be generated in contrast measurements, making it difficult for the camera’s passive auto-focus system to perform its function of locating the focused peak. In this paper, a new passive auto-focus algorithm is proposed to address this problem. First, a noise reduction preprocessing is introduced to make our algorithm robust to both additive noise and multiplicative noise. Then, a new contrast measure is presented to bring in local false peaks, ensuring the presence of a well defined focused peak. In order to gauge the performance of our algorithm, a modified peak search algorithm is used in the experiments. The experimental results from an actual digital camera validate the effectiveness of our proposed algorithm.

Enhanced visualisation for minimally invasive surgery

Endoscopes used in minimally invasive surgery provide a limited field of view, thus requiring a high degree of spatial awareness and orientation. Attempts at expanding this small, restricted view with previously observed imagery have been made by researchers and is generally known as image mosaicing or dynamic view expansion. For minimally invasive endoscopy, SLAM-based methods have been shown to have potential values but have yet to address effective visualisation techniques. The live endoscopic video feed is expanded with previously observed footage. To this end, a method that highlights the difference between actual camera image and historic data observed earlier is proposed. Old video data is faded out to grey scale to mimic human peripheral vision. Specular highlights are removed with the help of texture synthesis to avoid distracting visual cues. The method is further evaluated on in vivo and phantom sequences by a detailed user study to examine the ability of the user in discerning temporal motion trajectories while visualising the expanded field of view, a feature that is of practical value for enhancing spatial awareness and orientation. The difference between historic data and live video is integrated effectively. The use of a single texture domain generated by planar parameterisation is demonstrated for view expansion. Specular highlights can be removed through texture synthesis without introducing noticeable artefacts. The implicit encoding of motion trajectory of the endoscopic camera visualised by the proposed method facilitates both global awareness and temporal evolution of the scene. Dynamic view expansion provides more context for navigation and orientation by establishing reference points beyond the camera's field of view. Effective integration of visual cues is paramount for concise visualisation.

A Study of Changes in Production by Domestic Broadcasters Using Virtual Studio

This paper is for investigation and analysis of how a visual studio is widely used by domestic broadcasting companies and broadcasting companies' production environment. The history of a visual studio goes back to start of computer graphic. A visual studio is a way to produce a program using graphic sets created by a computer rather than actual setting, and it allows to express beyond limitations of actual studio settings and various imagery visual effects can be created by a computer. A visual studio can create 3-dimensional graphic and these graphics can be inter-locked with actual camera images to make visual spaces for various programs. These flows are aiming to achieve very natural image which is hard to distinguish it is artificially created rather than just to produce programs with simple image synthesis, and this paper analyzes the producing changes of domestic broadcasting visual studios as well as its usage and suggests the idealist direction for developing the production.

LOCALIZACIÓN E IDENTIFICACIÓN DE ACTIVOS SOTERRADOS MEDIANTE GEORADAR Y PROCESAMIENTO DE IMAGEN.

There is a growing industrial interest, covering from the sector of Engineering to the Environmental Management, among others, in the location of assets and anomalies in the subsoil in an efficient way and with a reduced error rate. It is aimed to increase the security and precision in the inspection of the subsoil by means of non-invasive and fast methods that could replace more traditional methods of direct digging.Nowadays, there is a wide range of sensors that can be used. One of them is the georadar, Ground Penetrating Radar, from now on GPR, used in many different disciplines such as archaeology, hydrology, forensics, engineering and civil engineering, among others. The geophysical prospection with GPR is a technology capable of solving the problem inherent to the knowledge of the subsoil. It allows characterising perfectly the subsoil and the structures inside, in an efficient and accurate way, with a minimum impact. As long as the technology goes forward, in many aspects of our life, it is possible to accomplish several tasks in a fast, efficient and automated way. Image processing has consolidated as a reliable technology installed in a wide number of applications for different problems. The high accuracy and resolution of the actual cameras, aslong as with the fast communication protocols and the services of the computers make possible to solve difficult problems, usually hand-crafted in the near past.In this article, we present an image processing application whose aim is to locate and identify the buried assets that can be found in a subsoil survey. Therefore, image processing techniques are applied to interpret the electromagnetic signals coming from georadar.First, the problem to be solved is shown, and some of the approaches to the problem are enumerated, which have been tackled during last years. Next, the proposed solution is thoroughly detailed together with their constituting elements, also the obtained results and the conclusions extracted from this work are exposed.

Yarn simulations with sharp edges

This paper describes a new method for collision handling of particle based systems moving on sharp edges using intermediate points and correction methods for particles passing these edges. It aims at facilitating the simulation of physically realistic movement of the particles in contact with surfaces, even if those surfaces contain sharp edges. The method was developed in order to increase the range of surfaces that allow valid simulations. The method has for the moment only been applied to the simulation of yarn, which is represented as a one-dimensional system of particles connected by springs and dampers. This could be extended however to two-dimensional systems such as cloth, although the technique is developed for explicit integration schemes. Validation has been done by comparing with surfaces with smoothed edges as well as by comparing with actual high speed camera results. From the results, it was shown that physically realistic movement of yarn over a sharp edge can now successfully be simulated.

Application of computerised correction method for optical distortion of two-dimensional facial image in superimposition between three-dimensional and two-dimensional facial images

The applicability of computerised correction of optical distortion to two-dimensional (2D)/three-dimensional (3D) facial image superimposition was investigated. Two-dimensional (2D) facial images of 10 male volunteers were taken with a commercially available closed circuit device (CCD) camera (reference camera) at four areas of the lens field: the centre, top, upper right and right. Correction was made by computer by calculating differences vis-à-vis the co-ordinates of dots on a test chart. Discrepancies in facial outlines between the 3D and 2D images decreased following correction in all lens fields and were below the threshold for true positive. The correction method was also tested using an actual surveillance camera and video recorder installed in a bank. The method was found to be effective for the correction of facial images, especially those taken in the top and right lens fields. Since the total error (observed error) remaining after correction appeared close to the random error (real error), systematic error was thought to be minimised by correction. Therefore, the present method was thought to display high fidelity, and could be useful for supplementary examination of conventional superimposition.

3-D Retinal Curvature Estimation

We study 3-D retinal curvature estimation from multiple images that provides the fundamental geometry of the human retina and could be used for 3-D retina visualization and disease diagnosis purposes. An affine camera model is used for 3-D reconstruction due to its simplicity, linearity, and robustness. A major challenge is that a series of optics is involved in the retinal imaging process, including an actual fundus camera, a digital camera, and the optics of the human eye, all of which cause significant nonlinear distortions in retinal images. In this paper, we develop a new constrained optimization method that considers both the geometric shape of the human retina and nonlinear lens distortions. Moreover, we examine a variety of lens distortion models to approximate the optics of the human eye in order to create a smooth spherical surface for curvature estimation. The experimental results on both synthetic data and real retinal images validate the proposed algorithm.

Visual Servoing in the Large

In this paper we consider the problem of maneuvering an autonomous robot in complex unknown environments using vision. The goal is to accurately servo a wheeled vehicle to a desired posture using only feedback from an on-board camera, taking into account the nonholonomic nature of the vehicle kinematics and the limited field-of-view of the camera. With respect to existing visual servoing schemes, which achieve similar goals locally (i.e. when the desired and actual camera views are sufficiently similar), we propose a method to visually navigate the robot through an extended visual map before eventually reaching the desired goal. The map comprises a set of images, previously stored in an exploratory phase, that convey both topological and metric information regarding the connectivity through feasible robot paths and the geometry of the environment, respectively. Experimental results on a laboratory setup are reported showing the practicality of the proposed approach.

Error Analysis for Image-Based Rendering With Depth Information

We propose a new approach to quantitatively analyze the rendering quality of image-based rendering (IBR) algorithms with depth information. The resulting error bounds for synthesized views depend on IBR configurations including the depth and intensity estimate errors, the scene geometry and texture, the number of actual cameras, their positions and resolution. Specifically, the IBR error is bounded by the summation of three terms, highlighting the impact of using multiple actual cameras, the impact of the noise level at the actual cameras, and the impact of the depth accuracy. We also quantify the impact of occlusions and intensity discontinuities. The proposed methodology is applicable to a large class of common IBR algorithms and can be applied locally. Experiments with synthetic and real scenes show that the developed error bounds accurately characterize the rendering errors. In particular, the error bounds correctly characterize the decay rates of synthesized views' mean absolute errors as O(lambda(-1)) and O(lambda(-2)), where lambda is the local density of actual samples, for 2-D and 3-D scenes, respectively. Finally, we discuss the implications of the proposed analysis on camera placement, budget allocation, and bit allocation.

Target based SNR analysis of spaceborne panchromatic TDICCD camera

TDICCD 是近几年发展起来的一种新型光电传感器, 作为焦平面器件已广泛应用于空间相机的设计和制造. 本文分析了待观测目标太阳高度角及亮度的影响因素, 建立了光能量从待观测目标到空间相机入瞳的亮度传递数学模型, 提出了一种基于目标的全色TDICCD 空间相机信噪比快速预估计算方法. 以模拟极轨卫星所携带的TDICCD 相机为例进行了预估分析. 结果显示, 此模拟北纬20°待观测目标全年星下点太阳高度角范围为41.5°~76.7°, <EM>F </EM>数为15 的TDICCD 相机可以获得的最大信噪比大于40dB. 本文的推导可用于TDICCD 空间相机的预研和总体设计, 同时达到验证卫星任务规划与轨道设计合理性的目的.

1A2-B14 拡張現実技術を使ったトラクタナビゲーションシステムの開発

An intuitive tractor navigation system using augmented reality was developed by superimposing a virtual 3-dimensional (3-D) image on an actual camera image. Two real-time kinematic (RTK) global positioning system (GPS) units and an inertial measurement unit determined tractor position and direction, and were used to create a dynamic, virtual 3D terrain of the area. Static tractor positions, including roll and pitch, were changed to compare the virtual and actual images. In static positions, position errors were less than 6 cm within 5 m of the tractor front, and directional errors were less than 1 degree.

Real-time face-priority auto focus for digital and cell-phone cameras

Auto-focus (AF) has been a key feature in consumer level digital and cell-phone cameras allowing users to focus automatically on a particular plane in a scene in order to get a sharp image. Face priority AF has become of interest lately due to the fact that most pictures captured by consumers are of human faces. In face-priority AF, the focusing decision is made based on a detected face area in the image, thus capturing a sharp picture of the face. While many face detection algorithms exist in the literature, very few of them are actually suitable for real-time software deployment on resource limited digital or cell-phone camera processors. In this paper, a fast face-detection algorithm is introduced by combining a skin color model cluster with a computationally efficient shape processing scheme. Comparison results with a standard algorithm in terms of robustness, speed and accuracy are provided. This face detection algorithm is incorporated into our previously developed rule-based AF method to achieve real-time face-priority AF on an actual digital camera platform.

From the Editors

From the Editors

Real-time predictive zoom tracking for digital still cameras

Zoom tracking is becoming a standard feature in digital still cameras (DSCs). It involves keeping an object of interest in focus during the zooming-in or zooming-out operation. Zoom tracking is normally achieved by moving the focus motor in real-time according to the so-called trace curves in response to changes in the zoom motor position. A trace curve denotes in-focus motor positions versus zoom motor positions for a specific object distance. A zoom tracking approach is characterized by the way these trace curves are estimated and followed. In this paper, a new zoom tracking approach, named predictive zoom tracking (PZT), is introduced based on two prediction models: auto-regressive and recurrent neural network. The performance of this approach is compared with the existing zoom tracking approaches commonly used in DSCs. The real-time implementation results obtained on an actual digital camera platform indicate that the developed PZT approach not only achieves higher tracking accuracies but also effectively addresses the key challenge of zoom tracking, namely the one-to-many mapping problem.