In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road a...
In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.
In this paper, we propose a method to estimate 3D pose information of an object in a randomly piled-up environment by using image data obtained from an RGB-D camera. The proposed method consists of two modules: object...
详细信息
In this paper, different dictionary learning and sparse coding algorithms are studied namely k-medoid, sparse non-negative matrix factorization (sNMF), active newton set algorithm (ASNA) and supervised non-negative ma...
详细信息
This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks fo...
详细信息
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification ...
详细信息
High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of pro...
High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.
This paper analyzes the problem of detection and isolation of faults in the actuators of a 3-Degree-of-Freedom (3-DOF) helicopter by a residual-based approach. A third-order sliding mode differentiator is designed for...
详细信息
Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A...
详细信息
Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road a...
详细信息
暂无评论