Machine Learning Approaches in Developing Expert System for Medical Image Analysis with respect to THR: From Detection to Diagnosis
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Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment.There is a need to find any deviation that can be acquired in position of artificial femur after the log time of surgery, well in advance thereby overcome the adverse socio economic and psychological burden to both the patient as well as the surgeon.The aim of the study is to develop a noninvasive, ultrasound-based, method of diagnosing acetabular cup loosening at an early stage before any major bone erosion has taken place.The proposed study will build on a previously successful technique for the diagnosis of loosing of the femoral stem component of a THR.This paper highlights the steps like box filter section, mainly first order and second order, followed by key points selection, key points extraction, key point matching and finally finding the deviation through motion vector analysis.The data for this research has been collected from different hospitals in Andhra Pradesh and Tamil Nadu.Topics:
A robot needs to localize an unknown object before grasping it. When the robot only has a monocular sensor, how can it get the object pose? In this work, we present a method of localizing the 6-DOF pose of a target object using a robotic arm and a hand-mounted monocular camera. The method includes an object recognition and a localization process. The recognition process uses point features on a surface of the target as a model of the object. The object localization process combines the robotic motion data and image data to calculate the 6-DOF pose of the object. This method can process objects containing textured planes. We verify the method in real tests.
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In robotics object tracking is needed to steer towards objects, check if grasping is successful, or investigate objects more closely by poking or handling them. While many 3D object tracking approaches have been proposed in the past, real world settings pose challenges such as automatically detecting tracking failure, real-time processing, and robustness to occlusion, illumination, and view point changes. This paper presents a 3D tracking system that is capable of overcoming these difficulties using a monocular camera. We present a method of Tracking-State-Detection (TSD) that takes advantage of commercial graphics processors to map textures onto object geometry, to learn textures online, and to recover object pose in real-time. Our system is able to handle 6 DOF object motion during changing lighting conditions, partial occlusion and motion blur while maintaining an accuracy of a few millimetres. Furthermore using TSD we are able to automatically detect occlusions or whether we lost track, and can then trigger a SIFT-based recognition system that is trained during tracking to recover the pose. Evaluations are presented in relation to ground truth pose data and examples present TSD on real-world scenes presented in video sequences.
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Abstract In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.
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The stripe laser based stereo vision is often used in robot vision-guided system in the eye-in-hand configuration. The 3D scene is reconstructed from many 3D stripes obtained in stripe laser based stereo vision. But 3D objects can not be recognized by 3D stripe information. In 3D cluttered scene, the recognition of 3D objects is also difficult due to the object pose and match. In fact, the video from camera of stripe laser based stereo vision can be benefit to recognize 3D objects. This paper proposes an approach of the object-oriented vision-guided robot that video segmentation, tracking and recognition are used to guide robot to reduce the complexity of 3D object detection, recognition and pose estimation. Experimental results demonstrate the effectiveness of the approach.
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We present a 3D tracking method of an object which is moving in a complicated scene by an active stereo vision system. The system uses binocular vision robot, which can simulate the human eye movements. Gaze holding on an target object with the controlled cameras keeps the target's stereo disparity small, and simplifies the visual processing to locate the target for pursuit control. The novel feature of our tracking method is the disparity-based segmentation method of the target object. The method utilizes zero disparity filter and correlation to separate the target object with small disparity from distracting background. Furthermore, using correlation method to estimate stereo disparity makes it possible to fixate on a surface of the target object. We show the experimental results with the complicated scene to demonstrate the effectiveness of the proposed method.< >
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A novel object tracking method based on RGB-D camera is proposed to handle fast appearance change, occlusion, background clutter which may arise for vision-based robot navigation. It makes use of appearance and depth information that are complementary to each other in visual perception to get robust tracking. First, RGB image and depth information are captured by the RGB-D camera. Then, an online updating appearance model is created with features extracted from RGB image. A motion model is created on plan-view map that is drawn from depth information and camera parameters. The estimation of object position and scale is performed on the motion model. Finally, appearance features are combined with position and scale information to track the target. The performance of our method is compared with a state-of-art video tracking method. It shows that our tracking method is more stable and accurate, and has overwhelming superiority when there is a great appearance change. A vision-based robot using our tracking method can navigate in cluttered environment successfully.
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This article proposes a hand-eye calibration using a new and easy method suitable for a camera mounted on the end-effector of an industrial robot using only a single image. The hand-eye calibration information could be used in robotic picking up of cubes using a monocular camera. Images captured from a particular pose of the camera have been segmented using a fusion of multiple methods such that the object information is obtained even in cases when there is less contrast between the object and the background, or in the presence of variation in lighting. The edge information, and subsequently the pose of the object was estimated using minimum number of images. In some of the cases a single image was sufficient but in case only a single edge edge is obtained, an additional image is grabbed after aligning the camera with the detected edge. An additional edge is estimated using a directional thresholding operation. The edge information in 3-D obtained using the calibration information was then used to calculate the pose of the object to facilitate robotic pick up. To ensure safety; a verification of the estimate was done using projection of the computed coordinates, and final pick up was done while monitoring the force to avoid damage due to collisions. The proposed approaches were physically implemented and experimentally validated.
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.
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