A Visual Cortex-Inspired Imaging-Sensor Architecture and Its Application in Real-Time Processing

A Refined-Line-Based Method to Estimate Vanishing Points for Vision-Based Autonomous Vehicles

Helping vehicles estimate vanishing points (VPs) in traffic environments has considerable value in the field of autonomous driving. It has multiple unaddressed issues such as refining extracted lines and removing spurious VP candidates, which suffers from low accuracy and high computational cost in a complex traffic environment. To address these two issues, we present in this study a new model to estimate VPs from a monocular camera. Lines that belong to structured configuration and orientation are refined. At that point, it is possible to estimate VPs through extracting their corresponding vanishing candidates through optimal estimation. The algorithm requires no prior training and it has better robustness to color and illumination on the base of geometric inferences. Through comparing estimated VPs to the ground truth, the percentage of pixel errors were evaluated. The results proved that the methodology is successful in estimating VPs, meeting the requirements for vision-based autonomous vehicles.

Adaptive Grasping of Moving Objects through Tactile Sensing

A robot’s ability to grasp moving objects depends on the availability of real-time sensor data in both the far-field and near-field of the gripper. This research investigates the potential contribution of tactile sensing to a task of grasping an object in motion. It was hypothesised that combining tactile sensor data with a reactive grasping strategy could improve its robustness to prediction errors, leading to a better, more adaptive performance. Using a two-finger gripper, we evaluated the performance of two algorithms to grasp a ball rolling on a horizontal plane at a range of speeds and gripper contact points. The first approach involved an adaptive grasping strategy initiated by tactile sensors in the fingers. The second strategy initiated the grasp based on a prediction of the position of the object relative to the gripper, and provided a proxy to a vision-based object tracking system. It was found that the integration of tactile sensor feedback resulted in a higher observed grasp robustness, especially when the gripper–ball contact point was displaced from the centre of the gripper. These findings demonstrate the performance gains that can be attained by incorporating near-field sensor data into the grasp strategy and motivate further research on how this strategy might be expanded for use in different manipulator designs and in more complex grasp scenarios.

A Signal-Processing Neural Model Based on Biological Retina

Image signal processing has considerable value in artificial intelligence. However, due to the diverse disturbance (e.g., color, noise), the image signal processing, especially the representation of the signal, remains a big challenge. In the human visual system, it has been justified that simple cells in the primary visual cortex are obviously sensitive to vision signals with partial orientation features. In other words, the image signals are extracted and described along the pathway of visual processing. Inspired by this neural mechanism of the primary visual cortex, it is possible to build an image signal-processing model as the neural architecture. In this paper, we presented a method to process the image signal involving a multitude of disturbance. For image signals, we first extracted 4 rivalry pathways via the projection of color. Secondly, we designed an algorithm in which the computing process of the stimulus with partial orientation features can be altered into a process of analytical geometry, resulting in that the signals with orientation features can be extracted and characterized. Finally, through the integration of characterizations from the 4 different rivalry pathways, the image signals can be effectively interpreted and reconstructed. Instead of data-driven methods, the presented approach requires no prior training. With the use of geometric inferences, the method tends to be interpreted and applied in the signal processor. The extraction and integration of rivalry pathways of different colors allow the method to be effective and robust to the signals with the image noise and disturbance of colors. Experimental results showed that the approach can extract and describing the image signal with diverse disturbance. Based on the characterization of the image signal, it is possible to reconstruct signal features which can effectively represent the important information from the original image signal.