RESUMEN

Owing to the limited field of view (FOV) and depth of field (DOF) of a conventional camera, it is quite difficult to employ a single conventional camera to simultaneously measure high-precision displacements at many points on a bridge of dozens or hundreds of meters. Researchers have attempted to obtain a large FOV and wide DOF by a multi-camera system; however, with the growth of the camera number, the cost, complexity and instability of multi-camera systems will increase exponentially. This study proposes a multi-point displacement measurement method for bridges based on a low-cost Scheimpflug camera. The Scheimpflug camera, which meets the Scheimpflug condition, can enlarge the depth of field of the camera without reducing the lens aperture and magnification; thus, when the measurement points are aligned in the depth direction, all points can be clearly observed in a single field of view with a high-power zoom lens. To reduce the impact of camera motions, a motion compensation method applied to the Scheimpflug camera is proposed according to the characteristic that the image plane is not perpendicular to the lens axis in the Scheimpflug camera. Several tests were conducted for performance verification under diverse settings. The results showed that the motion errors in x and y directions were reduced by at least 62% and 92%, respectively, using the proposed method, and the measurements of the camera were highly consistent with LiDAR-based measurements.

RESUMEN

Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. However, the evaluation, classification, and quantification of findings from colonoscopy are subject to inter-observer variation. Automated assessment of colonoscopy is of interest considering the subjectivity present in qualitative human interpretations of colonoscopy findings. Localization of the camera is essential to interpreting the meaning and context of findings for diseases evaluated by colonoscopy. In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments. The camera localization system begins with non-informative frame detection and removal. Then a self-training end-to-end convolutional neural network is built to estimate the camera motion, where several strategies are proposed to improve its robustness and generalization on endoscopic videos. Using the estimated camera motion a camera trajectory can be derived and a relative location index calculated. Based on the estimated location index, anatomical colon segment classification is performed by constructing a colon template. The proposed motion estimation algorithm was evaluated on an external dataset containing the ground truth for camera pose. The experimental results show that the performance of the proposed method is superior to other published methods. The relative location index estimation and anatomical region classification were further validated using colonoscopy videos collected from routine clinical practice. This validation yielded an average accuracy in classification of 0.754, which is substantially higher than the performances obtained using location indices built from other methods.

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RESUMEN

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

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Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.

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PURPOSE: Optical colonoscopy is a prominent procedure by which clinicians examine the surface of the colon for cancerous polyps using a flexible colonoscope. One of the main concerns regarding the quality of the colonoscopy is to ensure that the whole colonic surface has been inspected for abnormalities. In this paper, we aim at estimating areas that have not been covered thoroughly by providing a map from the internal colon surface. METHODS: Camera parameters were estimated using optical flow between consecutive colonoscopy frames. A cylinder model was fitted to the colon structure using 3D pseudo stereo vision and projected into each frame. A circumferential band from the cylinder was extracted to unroll the internal colon surface (band image). By registering these band images, drift in estimating camera motion could be reduced, and a visibility map of the colon surface could be generated, revealing uncovered areas by the colonoscope. Hidden areas behind haustral folds were ignored in this study. The method was validated on simulated and actual colonoscopy videos. The realistic simulated videos were generated using a colonoscopy simulator with known ground truth, and the actual colonoscopy videos were manually assessed by a clinical expert. RESULTS: The proposed method obtained a sensitivity and precision of 98 and 96 % for detecting the number of uncovered areas on simulated data, whereas validation on real videos showed a sensitivity and precision of 96 and 78 %, respectively. Error in camera motion drift could be reduced by almost 50 % using results from band image registration. CONCLUSION: Using a simple cylindrical model for the colon and reducing drift by registering band images allows for the generation of visibility maps. The current results also suggest that the provided feedback through the visibility map could enhance clinicians' awareness of uncovered areas, which in return could reduce the probability of missing polyps.

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RESUMEN

Externally observing the experience of a participant in a virtual environment is generally accomplished by viewing an egocentric perspective. Monitoring this view can often be difficult for others to watch due to unwanted camera motions that appear unnatural and unmotivated. We present a novel method for reducing the unnaturalness of these camera motions by minimizing camera movement while maintaining the context of the participant's observations. For each time-step, we compare the parts of the scene viewed by the virtual participant to the parts of the scene viewed by the camera. Based on the similarity of these two viewpoints we next determine how the camera should be adjusted. We present two means of adjustment, one which continuously adjusts the camera and a second which attempts to stop camera movement when possible. Empirical evaluation shows that our method can produce paths that have substantially shorter travel distances, are easier to watch and maintain the original observations of the participant's virtual experience.