4,914 research outputs found
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.Comment: Submitted to ICRA 201
Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images
Deep learning models have witnessed depth and pose estimation framework on
unannotated datasets as a effective pathway to succeed in endoscopic
navigation. Most current techniques are dedicated to developing more advanced
neural networks to improve the accuracy. However, existing methods ignore the
special properties of endoscopic images, resulting in an inability to fully
unleash the power of neural networks. In this study, we conduct a detail
analysis of the properties of endoscopic images and improve the compatibility
of images and neural networks, to unleash the power of current neural networks.
First, we introcude the Mask Image Modelling (MIM) module, which inputs partial
image information instead of complete image information, allowing the network
to recover global information from partial pixel information. This enhances the
network' s ability to perceive global information and alleviates the phenomenon
of local overfitting in convolutional neural networks due to local artifacts.
Second, we propose a lightweight neural network to enhance the endoscopic
images, to explicitly improve the compatibility between images and neural
networks. Extensive experiments are conducted on the three public datasets and
one inhouse dataset, and the proposed modules improve baselines by a large
margin. Furthermore, the enhanced images we proposed, which have higher network
compatibility, can serve as an effective data augmentation method and they are
able to extract more stable feature points in traditional feature point
matching tasks and achieve outstanding performance
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Multi-view Face Pose Classification by Boosting with Weak Hypothesis Fusion Using Visual and Infrared Images
This paper proposes a novel method for multi-view face pose classification through sequential learning and sensor fusion. The basic idea is to use face images observed in visual and thermal infrared (IR) bands, with the same sampling weight in a multi-class boosting structure. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from visual and infrared bands interactively complement each other. This is achieved by learning weak hypothesis for visual and IR band independently and then fusing the optimized hypothesis sub-ensembles. In addition, an effective feature descriptor is introduced to thermal IR images. Experiments are conducted on a visual and thermal IR image dataset containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm
Image Classification by Multi-Class Boosting of Visual and Infrared Fusion with Applications to Object Pose Recognition
This paper proposes a novel method for multiview object pose classification through sequential learning and sensor fusion. The basic idea is to use images observed in visual and infrared (IR) bands, with the same sampling weight under a multi-class boosting framework. The main contribution of this paper is a multi-class AdaBoost classification framework where visual and infrared information interactively complement each other. This is achieved by learning hypothesis for visual and infrared bands independently and then fusing the optimized hypothesis subensembles. Experiments are conducted on several image datasets including a set of visual and thermal IR images containing 4844 face images in 5 different poses. Results have shown significant increase in classification rate as compared with an existing multi-class AdaBoost algorithm SAMME trained on visual or infrared images alone, as well as a simple baseline classification-fusion algorithm
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