3D Motion Estimation Using a Camera and Proprioceptive Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 791
Special Issue Editors
Interests: deep learning; biometrics; computer vision; machine learning; artificial intelligence
Interests: artificial intelligence; networking; blockchain; engineering applications
Special Issue Information
Dear Colleagues,
Across many embedded applications, delicate forms of flexible and malleable composites are common, yet the proprioceptive detection of these entities has traditionally been a concern. To put it another way, there has not really been a means of determining and characterizing the highly detailed, three-dimensional forms of delicate substances with intrinsic sensors. Using integrated cameras, designers reveal a system for measuring the precise 3D forms of soft objects in actual environments. A convolutional neural network (CNN) creates a residual program, encoding the curvature state from the different images captured by the camera systems inside a leathery texture. These data are then used by an additional neural network to rebuild the three-dimensional shape. Toward the domain of computer vision, the prediction of camera 3D motion is a well-established and complex subject. The past ten years have seen an increase in studies into the use of sensing devices with video cameras; however, most of this work has focused on picture stabilization.
Gyrostabilizer cameras are now offered by the majority of major camcorder brands. Furthermore, there is almost no indication that any effort has been applied in the past to equip a video camera with a full gyroscope design parameter in order to retrieve advanced 3D motion characteristics. The IOME Cam system's ability to create three-dimensional representations of landscapes is perhaps its most fundamental and all-inclusive implementation objective. The 3D motions and designs of components on the optical axis can be examined in relation to the camera motion, whereas if the 3D motion of the camera is specified from vertical to horizontal, the foregoing is the anticipated 3D motion prediction, which is possible to compute in this survey. Finally, the incoming texture pattern is divided into interpersonal and forecast features. Similar to versus, intra-frames are transformed into architecture and pigment video. Using the 3D motion coordinates derived from the pursuit, the anticipated frames are transformed into either a motion movie or a delta video. Given that a 3D domain in a pixel shader chassis is not entirely specified by feature clusters, the pursuit is employed to carry out a 3D motion pursuit that is appropriate for a fragment shader cascade.
In this Special Issue, cameras with a broad field of view are the superior image devices for 3D motion assessment. As a conclusion, designers offer design specifications for cameras and highlight an algorithm for such a camera that accurately and robustly quantifies its 3D motion, regardless of the camera.
The topics relevant to this Special Issue include but are not limited to:
- Real-time 3D slam for autonomous robot using input from the pattern generator;
- Utilizing various 3D motion estimation methods to improve performance on webcams;
- Virtual vehicle motion computation with stereoscopic vision and texture recognition;
- Regarding camera IMU systems, 3D motion analysis, and interactive contextual adaptation;
- Computer vision-based appraisal employing rigid transformation and timeframe interpretation;
- Proprioceptive sensing premised estimating tip position of a soft expandable devices;
- Partial field detection depending on homography with a solitary on-board camera;
- Proprioceptive sensors with repetitive sensors for reliable computation;
- Employing incident cameras and impartial motion sensors;
- Estimating reliable camera motion with convergent programming;
- Standard deblurring and intensity video image approximation;
- Motion analysis in autonomous vehicles using a generic camera.
Dr. Hammam Alshazly
Dr. Hela Elmannai
Dr. Amir Benzaoui
Guest Editors
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