HDRFusion:HDR SLAM using a low-cost auto-exposure RGB-D sensor

We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure. It is based on a normalised radiance measure which is invariant to exposure changes and not only robustifies the tracking under changing lighting conditions, but also enables the following exposure compensation perform accurately to allow online building of high dynamic range (HDR) maps. The latter facilitates the frame-to-model tracking to minimise drift as well as better capturing light variation within the scene. Results from experiments with synthetic and real data demonstrate that the method provides both improved tracking and maps with far greater dynamic range of luminosity.Comment: 14 page

Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery

Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as using monocular or binocular laparoscopes [1,2]. More recently, advances in deep learning have enabled depth estimation via Convolutional Neural Networks (CNNs) [3], but training requires a large image dataset with ground truth depths. Inspired by [4], we propose a deep learning framework for surgical scene depth estimation using self-supervision for scalable data acquisition. Our framework consists of an autoencoder for depth prediction, and a differentiable spatial transformer for training the autoencoder on stereo image pairs without ground truth depths. Validation was conducted on stereo videos collected in robotic partial nephrectomy.Comment: A two-page short report to be presented at the Hamlyn Symposium on Medical Robotics 2017. An extension of this work is on progres

HDRFusion:HDR SLAM using a low-cost auto-exposure RGB-D sensor

We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure. It is based on a normalised radiance measure which is invariant to exposure changes and not only robustifies the tracking under changing lighting conditions, but also enables the following exposure compensation perform accurately to allow online building of high dynamic range (HDR) maps. The latter facilitates the frame-to-model tracking to minimise drift as well as better capturing light variation within the scene. Results from experiments with synthetic and real data demonstrate that the method provides both improved tracking and maps with far greater dynamic range of luminosity.Comment: 14 page

Factors affecting commencement and cessation of betel quid chewing behaviour in Malaysian adults

<p>Abstract</p> <p>Background</p> <p>Betel quid chewing is a common habit widely practiced in Southern Asian populations. However, variations are seen in the content of a betel quid across the different countries. Factors associated with commencement and cessation of this habit has been numerously studied. Unfortunately, data on Malaysian population is non-existent. This study aims to determine the factors associated with the inception and also cessation of betel quid chewing behaviour among Malaysian adults.</p> <p>Method</p> <p>This study is part of a nationwide survey on oral mucosal lesions carried out among 11,697 adults in all fourteen states in Malaysia. The questionnaire included sociodemographic information and details on betel quid chewing habit such as duration, type and frequency. The Kaplan-Meier estimates were calculated and plotted to compare the rates for the commencement and cessation of betel quid chewing behaviour. Cox proportional hazard regression models were used to calculate the hazard rate ratios for factors related to commencement or cessation of this habit.</p> <p>Results</p> <p>Of the total subjects, 8.2% were found to be betel quid chewers. This habit was more prevalent among females and, in terms of ethnicity, among the Indians and the Indigenous people of Sabah and Sarawak. Cessation of this habit was more commonly seen among males and the Chinese. Females were found to be significantly more likely to start (p < 0.0001) and less likely to stop the quid chewing habit. Females, those over 40 years old, Indians and a history of smoking was found to significantly increase the likelihood of developing a quid chewing habit (p < 0.0001). However, those who had stopped smoking were found to be significantly more likely to promote stopping the habit (p = 0.0064). Cessation was also more likely to be seen among those who chewed less than 5 quids per day (p < 0.05) and less likely to be seen among those who included areca nut and tobacco in their quid (p < 0.0001).</p> <p>Conclusion</p> <p>Factors that influence the development and cessation of this behaviour are gender, age, ethnicity, and also history of smoking habit while frequency and type of quid chewed are important factors for cessation of this habit.</p