10,424 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
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
Emergency events involving fire are potentially harmful, demanding a fast and
precise decision making. The use of crowdsourcing image and videos on crisis
management systems can aid in these situations by providing more information
than verbal/textual descriptions. Due to the usual high volume of data,
automatic solutions need to discard non-relevant content without losing
relevant information. There are several methods for fire detection on video
using color-based models. However, they are not adequate for still image
processing, because they can suffer on high false-positive results. These
methods also suffer from parameters with little physical meaning, which makes
fine tuning a difficult task. In this context, we propose a novel fire
detection method for still images that uses classification based on color
features combined with texture classification on superpixel regions. Our method
uses a reduced number of parameters if compared to previous works, easing the
process of fine tuning the method. Results show the effectiveness of our method
of reducing false-positives while its precision remains compatible with the
state-of-the-art methods.Comment: 8 pages, Proceedings of the 28th SIBGRAPI Conference on Graphics,
Patterns and Images, IEEE Pres
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Intraoperative segmentation and tracking of minimally invasive instruments is
a prerequisite for computer- and robotic-assisted surgery. Since additional
hardware like tracking systems or the robot encoders are cumbersome and lack
accuracy, surgical vision is evolving as promising techniques to segment and
track the instruments using only the endoscopic images. However, what is
missing so far are common image data sets for consistent evaluation and
benchmarking of algorithms against each other. The paper presents a comparative
validation study of different vision-based methods for instrument segmentation
and tracking in the context of robotic as well as conventional laparoscopic
surgery. The contribution of the paper is twofold: we introduce a comprehensive
validation data set that was provided to the study participants and present the
results of the comparative validation study. Based on the results of the
validation study, we arrive at the conclusion that modern deep learning
approaches outperform other methods in instrument segmentation tasks, but the
results are still not perfect. Furthermore, we show that merging results from
different methods actually significantly increases accuracy in comparison to
the best stand-alone method. On the other hand, the results of the instrument
tracking task show that this is still an open challenge, especially during
challenging scenarios in conventional laparoscopic surgery
Marshall Space Flight Center Research and Technology Report 2019
Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come
- …