In this paper a global vision scheme for estimation of positions and orientations of mobile robots is presented. It is applied to robot soccer application which is a fast dynamic game and therefore needs an efficient and robust vision system implemented. General applicability of the vision system can be found in other robot applications such as mobile transport robots in production, warehouses, attendant robots, fast vision tracking of targets of interest and entertainment robotics. Basic operation of the vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find corresponding regions belonging to one of the classes. In the second step, all the regions are examined. Selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty is focused on optimization of the processing time needed to finish the estimation of possible object positions. Better results of the vision system are achieved by implementing camera calibration and shading correction algorithm. The former corrects camera lens distortion, while the latter increases robustness to irregular illumination conditions.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/s0019-0578(07)60152-9 | DOI Listing |
J Neural Transm (Vienna)
January 2025
Postgraduate Program in Physical Therapy (PPGFT), Department of Physical Therapy (DFisio), University of São Carlos (UFSCar), Washington Luis Road, Km 235, São Carlos, São Paulo, 13565-905, Brazil.
The cerebellum is a structure in the suprasegmental nervous system classically known for its involvement in motor functions such as motor planning, coordination, and motor learning. However, with scientific advances, other functions of the cerebellum, such as cognitive, emotional, and autonomic processing, have been discovered. Currently, there is a body of evidence demonstrating the involvement of the cerebellum in nociception and pain processing.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.
Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities-RGB, thermal, and depth-using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Computer-Aided Design and Test (CADT) Research Group, McMaster University, Hamilton, ON L8S 4L8, Canada.
A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Industrial Systems Institute, Athena Research and Innovation Center, 26504 Patras, Greece.
Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop coherent and robust systems that efficiently perform HAR. Although there have been many efficient systems developed to date, still, there are many issues to be addressed.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!