Multi-object tracking (MOT) is one of the most important problems in computervision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
详细信息
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate bot...
详细信息
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computervision. In this paper, we presented a traffic sign classification system implemented using a hybrid qu...
详细信息
Glaucoma is one of the major reasons for visual impairment all across the globe. The recent advancements in machine learning techniques have greatly facilitated ophthalmologists in the early diagnosis of ocular diseas...
详细信息
3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
详细信息
Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous image...
详细信息
ISBN:
(数字)9798350365474
ISBN:
(纸本)9798350365481
Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.
This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance s...
详细信息
Inverse Kinematics (IK) is one of the most fundamental challenges in robotics. It refers to the process of determining the joint configurations required to achieve the desired position and orientation (pose) of a robo...
详细信息
ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Inverse Kinematics (IK) is one of the most fundamental challenges in robotics. It refers to the process of determining the joint configurations required to achieve the desired position and orientation (pose) of a robot end-effector. Although numerous Data-Driven (DD) IK solvers have demonstrated encouraging results, they have not achieved the same accuracy when compared to other IK methods for complex robot configurations (e.g., numerical methods for higher Degrees of Freedom (DoF)). In this work, we propose a new Learning-by-Example method, and show that such a scheme considerably improves the IK learning results when compared to other DD learners. In our approach, the network input incorporates an example of joint-pose pair along with the query pose to predict the desired robot joint configuration. We show that the example joint-pose pair does not need to be too close to the query – i.e. example and query can be as far as 20 degrees apart in the joint configuration space. Furthermore, we investigate the utilization of residual and dense skip connections in Multilayer Perceptron for DDIK solvers and employ the resulting networks for two redundant robotic manipulators: a 7-DoF-7R commensurate robot and a 7DoF-2RP4R incommensurate robot. Our experimental results show that the resulting DDIK solver can reliably predict IK solutions with accuracy better than 1mm in position and 1deg in orientation.
In recent years, event cameras (DVS - Dynamic vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolu...
详细信息
Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open...
详细信息
暂无评论