Brain–computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain–computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

Background and Objectives: Depth perception of the human visual system in three-dimensional (3D) space plays an important role in human–computer interaction and artificial intelligence (AI) areas. It mainly employs binocular disparity and motion parallax cues. This study aims to systemically summarize the related studies about depth perception specified by these two cues. Materials and Methods: We conducted a literature investigation on related studies and summarized them from aspects like motivations, research trends, mechanisms, and interaction models of depth perception specified by these two cues. Results: Development trends show that depth perception research has gradually evolved from early studies based on a single cue to quantitative studies based on the interaction between these two cues. Mechanisms of these two cues reveal that depth perception specified by the binocular disparity cue is mainly influenced by factors like spatial variation in disparity, viewing distance, the position of visual field (or retinal image) used, and interaction with other cues; whereas that specified by the motion parallax cue is affected by head movement and retinal image motion, interaction with other cues, and the observer’s age. By integrating these two cues, several types of models for depth perception are summarized: the weak fusion (WF) model, the modified weak fusion (MWF) model, the strong fusion (SF) model, and the intrinsic constraint (IC) model. The merits and limitations of each model are analyzed and compared. Conclusions: Based on this review, a clear picture of the study on depth perception specified by binocular disparity and motion parallax cues can be seen. Open research challenges and future directions are presented. In the future, it is necessary to explore methods for easier manipulating of depth cue signals in stereoscopic images and adopting deep learning-related methods to construct models and predict depths, to meet the increasing demand of human–computer interaction in complex 3D scenarios.

Automobile license plate image generation represents a pivotal technology for the development of intelligent transportation systems. However, existing methods are constrained by their inability to simultaneously preserve geometric structure and radiometric properties of both license plates and characters. To overcome this limitation, we propose a novel framework for generating geometrically and radiometrically consistent license plate images. The proposed radiometric enhancement framework integrates two specialized modules, which are precise geometric rectification and radiometric property learning. The precise geometric rectification module exploits the perspective transformation consistency between character regions and license plate boundaries. By employing a feature matching algorithm based on character endpoint correspondence, this module achieves precise plate rectification, thereby establishing a geometric foundation for maintaining character structural integrity in generated images. The radiometric property learning module implements a precise character inpainting strategy with fluctuation compensation inpainting to reconstruct background regions, followed by a character-wise style transfer approach to ensure both geometric and radiometric consistency with realistic automobile license plates. Furthermore, we introduce a physical validation and evaluation method to quantitatively assess image quality. Comprehensive evaluation on real-world datasets demonstrate that our method achieves superior performance, with a peak signal-to-noise ratio (PSNR) of 13.83 dB and a structural similarity index measure (SSIM) of 0.57, representing significant improvements over comparative methods in preserving both structural integrity and radiometric properties. This framework effectively enhances the visual fidelity and reliability of generated automobile license plate images, thereby providing high-quality data for intelligent transportation recognition systems while advancing license plate image generation technology.