Author: ravikiranramesh
A Novel Tele-Operated Flexible Robot Targeted for Minimally Invasive Robotic Surgery
Abstract
In this paper, a novel flexible robot system with a constrained tendon-driven serpentine manipulator (CTSM) is presented. The CTSM gives the robot a larger workspace, more dexterous manipulation, and controllable stiffness compared with the da Vinci surgical robot and traditional flexible robots. The robot is tele-operated using the Novint Falcon haptic device. Two control modes are implemented, direct mapping and incremental mode. In each mode, the robot can be manipulated using either the highest stiffness scheme or the minimal movement scheme. The advantages of the CTSM are shown by simulation and experimental results.
A Novel Tele-Operated Flexible Robot… (PDF Download Available). Available from: https://www.researchgate.net/publication/281389458_A_Novel_Tele-Operated_Flexible_Robot_Targeted_for_Minimally_Invasive_Robotic_Surgery [accessed May 30 2018].
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Parameterized Distortion-Invariant Feature for Robust Tracking in Omnidirectional Vision
Abstract
Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to the involved quadratic mirrors. Therefore, features based on the conventional pin-hole model are hard to achieve satisfactory performances when directly applied to the distorted omnidirectional images. This paper analyzes the catadioptric geometry to facilitate modeling the nonlinear distortions of omnidirectional images. Different to the conventional imaging model, the prior information is considered in catadioptric system. A parameterized neighborhood mapping model is proposed to efficiently calculate the neighborhood of an object based on its measurable radial distance in the image plane. On the basis of the parameterized nonlinear model, a distortion-invariant fragment-based joint-feature mixture model of Gaussian is presented for human target tracking in omnidirectional vision. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into the feature clustering. The joint probability distribution of a joint-feature class is modeled by a mixture of Gaussian. A weight contribution mechanism is designed to flexibly weight the fragments contribution based on their responses, which leads to a robust tracking even under serious partial occlusion. Finally, experiments validate the advantage of the proposed algorithm over other conventional approaches.
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Analysis of Different Sparsity Methods in Constrained RBM for Sparse Representation in Cognitive Robotic Perception
Abstract
Cognitive robotic systems nowadays are intensively involving learning algorithms to achieve highly adaptive and intelligent behaviors, including actuation, sensing, perception and adaptive control. Deep learning has emerged as an effective approach in image-based robotic perception and actions. Towards cognitive robotic perception based on deep learning, this paper focuses the Constrained Restricted Boltzmann Machine (RBM) on visual images for sparse feature representation. Inspired by sparse coding, the sparse constraints are performed on the hidden layer of RBM to obtain sparse and effective feature representation from perceived visual images. The RBM with Sparse Constraint (RBMSC) is proposed with a generalized optimization problem, where the constraints are applied on the probability density of hidden units directly to obtain more sparse representation. This paper presents three novel RBM variants, namely L 1-RBM, L 2-RBM, and L 1/2-RBM constrained by L 1-norm, L 2-norm, and L 1/2-norm on RBM, respectively. A Deep Belief Network with two hidden layers is built for comparison between each RBM variants. The experiments on MNIST database (Mixed National Institute of Standards and Technology database) show that the L 1/2-RBM can obtain more sparse representation than RBM, L 1-RBM, L 2-RBM, and Sparse-RBM (SRBM) in terms of sparseness metric. For further verification, the proposed methods are still tested on MNIST Variations dataset. The recognition results from perceived images in MNIST and MNIST Variations demonstrate that our proposed constrained RBM variants are feasible for object cognitive and perception, and the proposed L 1/2-RBM and L 1-RBM outperforms RBM and SRBM in terms of object recognition.
No-reference Blur Assessment Based on Edge Modeling
Abstract
This paper presents a no-reference objective blur metric based on edge model (EMBM) to address the image blur assessment problem. A parametric edge model is incorporated to describe and detect edges, which can offer simultaneous width and contrast estimation for each edge pixel. With the pixel-adaptive width and contrast estimations, the probability of detecting blur at edge pixels can be determined. Also, unlike previous work, we advocate using only the salient edge pixels to simulate the blur assessment in Human Visual System (HVS). Finally, the blur metric is obtained by cumulating the probability of blur detection. Various images with different blur distortions are tested to demonstrate the effectiveness of the proposed metric.
Development and selection of Asian-specific humeral implants based on statistical atlas: toward planning minimally invasive surgery
Abstract
The commercial humeral implants based on the Western population are currently not entirely compatible with Asian patients, due to differences in bone size, shape and structure. Surgeons may have to compromise or use different implants that are less conforming, which may cause complications of as well as inconvenience to the implant position. The construction of Asian humerus atlases of different clusters has therefore been proposed to eradicate this problem and to facilitate planning minimally invasive surgical procedures [6,31]. According to the features of the atlases, new implants could be designed specifically for different patients. Furthermore, an automatic implant selection algorithm has been proposed as well in order to reduce the complications caused by implant and bone mismatch. Prior to the design of the implant, data clustering and extraction of the relevant features were carried out on the datasets of each gender. The fuzzy C-means clustering method is explored in this paper. Besides, two new schemes of implant selection procedures, namely the Procrustes analysis-based scheme and the group average distance-based scheme, were proposed to better search for the matching implants for new coming patients from the database. Both these two algorithms have not been used in this area, while they turn out to have excellent performance in implant selection. Additionally, algorithms to calculate the matching scores between various implants and the patient data are proposed in this paper to assist the implant selection procedure. The results obtained have indicated the feasibility of the proposed development and selection scheme. The 16 sets of male data were divided into two clusters with 8 and 8 subjects, respectively, and the 11 female datasets were also divided into two clusters with 5 and 6 subjects, respectively. Based on the features of each cluster, the implants designed by the proposed algorithm fit very well on their reference humeri and the proposed implant selection procedure allows for a scenario of treating a patient with merely a preoperative anatomical model in order to correctly select the implant that has the best fit. Based on the leave-one-out validation, it can be concluded that both the PA-based method and GAD-based method are able to achieve excellent performance when dealing with the problem of implant selection. The accuracy and average execution time for the PA-based method were 100 % and 0.132 s, respectively, while those of the GAD- based method were 100 % and 0.058 s. Therefore, the GAD-based method outperformed the PA-based method in terms of execution speed. The primary contributions of this paper include the proposal of methods for development of Asian-, gender- and cluster-specific implants based on shape features and selection of the best fit implants for future patients according to their features. To the best of our knowledge, this is the first work that proposes implant design and selection for Asian patients automatically based on features extracted from cluster-specific statistical atlases.
