45,456 research outputs found
View synthesis for pose computation
International audienceGeometrical registration of a query image with respect to a 3D model, or pose estimation, is the cornerstone of many computer vision applications. It is often based on the matching of local photometric descriptors invariant to limited viewpoint changes. However, when the query image has been acquired from a camera position not covered by the model images, pose estimation is often not accurate and sometimes even fails, precisely because of the limited invariance of descriptors. In this paper, we propose to add descriptors to the model, obtained from synthesized views associated with virtual cameras completing the covering of the scene by the real cameras. We propose an efficient strategy to localize the virtual cameras in the scene and generate valuable descriptors from synthetic views. We also discuss a guided sampling strategy for registration in this context. Experiments show that the accuracy of pose estimation is dramatically improved when large viewpoint changes makes the matching of classic descriptors a challenging task
Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy
Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the
integration of transrectal ultrasound (TRUS) imaging system which is the most
widely used imaging modelity in prostate imaging is essential. However, manual
manipulation of the ultrasound transducer during the procedure will
significantly interfere with the surgery. Therefore, we propose an image
co-registration algorithm based on a photoacoustic marker method, where the
ultrasound / photoacoustic (US/PA) images can be registered to the endoscopic
camera images to ultimately enable the TRUS transducer to automatically track
the surgical instrument Methods: An optimization-based algorithm is proposed to
co-register the images from the two different imaging modalities. The
principles of light propagation and an uncertainty in PM detection were assumed
in this algorithm to improve the stability and accuracy of the algorithm. The
algorithm is validated using the previously developed US/PA image-guided system
with a da Vinci surgical robot. Results: The target-registration-error (TRE) is
measured to evaluate the proposed algorithm. In both simulation and
experimental demonstration, the proposed algorithm achieved a sub-centimeter
accuracy which is acceptable in practical clinics. The result is also
comparable with our previous approach, and the proposed method can be
implemented with a normal white light stereo camera and doesn't require highly
accurate localization of the PM. Conclusion: The proposed frame registration
algorithm enabled a simple yet efficient integration of commercial US/PA
imaging system into laparoscopic surgical setting by leveraging the
characteristic properties of acoustic wave propagation and laser excitation,
contributing to automated US/PA image-guided surgical intervention
applications.Comment: 12 pages, 9 figure
Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution
The accurate registration of multiview images is of central importance in many advanced image processing applications. Image super-resolution, for example, is a typical application where the quality of the super-resolved image is degrading as registration errors increase. Popular registration methods are often based on features extracted from the acquired images. The accuracy of the registration is in this case directly related to the number of extracted features and to the precision at which the features are located: images are best registered when many features are found with a good precision. However, in low-resolution images, only a few features can be extracted and often with a poor precision. By taking a sampling perspective, we propose in this paper new methods for extracting features in low-resolution images in order to develop efficient registration techniques. We consider, in particular, the sampling theory of signals with finite rate of innovation and show that some features of interest for registration can be retrieved perfectly in this framework, thus allowing an exact registration. We also demonstrate through simulations that the sampling model which enables the use of finite rate of innovation principles is well suited for modeling the acquisition of images by a camera. Simulations of image registration and image super-resolution of artificially sampled images are first presented, analyzed and compared to traditional techniques. We finally present favorable experimental results of super-resolution of real images acquired by a digital camera available on the market
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Recent robotic manipulation competitions have highlighted that sophisticated
robots still struggle to achieve fast and reliable perception of task-relevant
objects in complex, realistic scenarios. To improve these systems' perceptive
speed and robustness, we present SegICP, a novel integrated solution to object
recognition and pose estimation. SegICP couples convolutional neural networks
and multi-hypothesis point cloud registration to achieve both robust pixel-wise
semantic segmentation as well as accurate and real-time 6-DOF pose estimation
for relevant objects. Our architecture achieves 1cm position error and
<5^\circ$ angle error in real time without an initial seed. We evaluate and
benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read
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