3,789 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering
For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction
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3D Automatic Target Recognition for Future LIDAR Missiles
We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches
Application of augmented reality and robotic technology in broadcasting: A survey
As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper
Twofold Structured Features-Based Siamese Network for Infrared Target Tracking
Nowadays, infrared target tracking has been a critical technology in the
field of computer vision and has many applications, such as motion analysis,
pedestrian surveillance, intelligent detection, and so forth. Unfortunately,
due to the lack of color, texture and other detailed information, tracking
drift often occurs when the tracker encounters infrared targets that vary in
size or shape. To address this issue, we present a twofold structured
features-based Siamese network for infrared target tracking. First of all, in
order to improve the discriminative capacity for infrared targets, a novel
feature fusion network is proposed to fuse both shallow spatial information and
deep semantic information into the extracted features in a comprehensive
manner. Then, a multi-template update module based on template update mechanism
is designed to effectively deal with interferences from target appearance
changes which are prone to cause early tracking failures. Finally, both
qualitative and quantitative experiments are carried out on VOT-TIR 2016
dataset, which demonstrates that our method achieves the balance of promising
tracking performance and real-time tracking speed against other out-of-the-art
trackers.Comment: 13 pages,9 figures,references adde
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