The Mechanism of Orientation Detection Based on Artificial Visual System

As an important part of the nervous system, the human visual system can provide visual perception for humans. The research on it is of great significance to improve our understanding of biological vision and the human brain. Orientation detection, in which visual cortex neurons respond only to linear stimuli in specific orientations, is an important driving force in computer vision and biological vision. However, the principle of orientation detection is still unknown. This paper proposes an orientation detection mechanism based on dendrite calculation of local orientation detection neurons. We hypothesized the existence of orientation detection neurons that only respond to specific orientations and designed eight neurons that can detect local orientation information. These neurons interact with each other based on the nonlinearity of dendrite generation. Then, local orientation detection neurons are used to extract local orientation information, and global orientation information is deduced from local orientation information. The effectiveness of the mechanism is verified by computer simulation, which shows that the machine can perform orientation detection well in all experiments, regardless of the size, shape, and position of objects. This is consistent with most known physiological experiments.

Simple contextual cueing prevents retroactive interference in short-term perceptual training of orientation detection tasks

Perceptual training of multiple tasks suffers from interference between the trained tasks. Here, we conducted four psychophysical experiments with separate groups of participants to investigate the possibility of preventing the interference in short-term perceptual training. We trained the participants to detect two orientations of Gabor stimuli in two adjacent days at the same retinal location and examined the interference of training effects between the two orientations. The results showed significant retroactive interference from the second orientation to the first orientation (Experiments 1 and 2). Introducing a 6-hour interval between the pre-test and training of the second orientation did not eliminate the interference effect, excluding the interpretation of disrupted reconsolidation as the pre-test of the second orientation may reactivate and destabilize the representation of the first orientation (Experiment 3). Finally, the training of the two orientations was accompanied by fixations in two colors, each served as a contextual cue for one orientation. The results showed that the retroactive interference was not evident after introducing these passively perceived contextual cues (Experiment 4). Our findings suggest that the retroactive interference effect in short-term perceptual training of orientation detection tasks was likely the result of higher-level factors such as shared contextual cues embedded in the tasks. The effect of multiple perceptual training could be facilitated by associating the trained tasks with different contextual cues.

A cooperative mobile robot and manipulator system (Co-MRMS) for transport and lay-up of fibre plies in modern composite material manufacture

Composite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper presents a case study that investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-based and Fourier transform-based machine vision approaches are used to achieve high accuracy capability in localisation and fibre orientation detection respectively. Moreover, a particle-based approach is adopted to model material deformation during manipulation within robotic simulations. As a case study, a vacuum suction-based end-effector model is developed to deal with sagging effects and to quickly evaluate different gripper designs, comprising of an array of multiple suction cups. Comprehensive simulations and physical experiments, conducted with a 6-DOF serial manipulator and a two-wheeled differential drive mobile robot, demonstrate the efficient interaction and high performance of the Co-MRMS for autonomous material transportation, material localisation, fibre orientation detection and grasping of deformable material. Additionally, the experimental results verify that the presented machine vision approach achieves high accuracy in localisation (the root mean square error is 4.04 mm) and fibre orientation detection (the root mean square error is 1.84∘) and enables dealing with uncertainties such as the shape and size of fibre plies.

A Robust Hand Silhouette Orientation Detection Method for Hand Gesture Recognition

The computer vision approach is most widely used for research related to hand gesture recognition. The detection of the image orientation has been discovered to be one of the keys to determine its success. The degree of freedom for a hand determines the shape and orientation of a gesture, which further causes a problem in the recognition methods. This article proposed evaluating orientation detection for silhouette static hand gestures with different poses and orientations without considering the forearm. The longest chord and ellipse were the two popular methods compared. The angles formed from two wrist points were selected as ground truth data and calculated from the horizontal axis. The performance was analyzed using the error values obtained from the difference in ground truth data angles compared to the method's results. The method has errors closer to zero that were rated better. Moreover, the method was evaluated using 1187 images, divided into four groups based on the forearm presence, and the results showed its effect on orientation detection. It was also discovered that the ellipse method was better than the longest chord. This study's results are used to select hand gesture orientation detection to increase accuracy in the hand gesture recognition process.

A Cooperative Mobile Robot and Manipulator System (Co-MRMS) for Transport and Lay-up of Fibre Plies in Modern Composite Material Manufacture

Composite materials are widely used in industry due to their light weight and specific performance. Currently, composite manufacturing mainly relies on manual labour and individual skills, especially in transport and lay-up processes, which are time-consuming and prone to errors. As part of a preliminary investigation into the feasibility of deploying autonomous robotics for composite manufacturing, this paper investigates a cooperative mobile robot and manipulator system (Co-MRMS) for material transport and composite lay-up, which mainly comprises a mobile robot, a fixed-base manipulator and a machine vision sub-system. In the proposed system, marker-based and Fourier transform-based machine vision approaches are used to achieve high accuracy capability in localisation and fibre orientation detection respectively. Moreover, a particle based approach is adopted to model material deformation during manipulation within robotic simulations. As a case study, a vacuum suction based end-effector model is developed to deal with sagging effects and to quickly evaluate different gripper designs, comprising of an array of multiple suction cups. Comprehensive simulations and physical experiments, conducted with a 6-DOF serial manipulator and a two-wheeled differential drive mobile robot, demonstrate the efficient interaction and high performance of the Co-MRMS for autonomous material transportation, material localisation, fibre orientation detection and grasping of deformable material. Additionally, the experimental results verify that the presented machine vision approach achieves high accuracy in localisation (the root mean square error is 4.04 mm) and fibre orientation detection (the root mean square error is 1.84°).