Bicubic parametric patches are widely used in various geometric applications. These patches are critical in CAD/CAM systems, which are applied in the automotive industry and mechanical and civil engineering. Commonly, Hermite, Bézier, Coons, or NURBS patches are employed in practice. However, the construction of the Hermite bicubic patch is often not easy to explain formally. This contribution presents a new formal method for constructing the Hermite bicubic plate based on the tensor product approach.

Capturing and presenting exciting moments is crucial for the audience's experience in basketball game broadcast cameras. However, traditional radar image processing techniques are limited by various factors and cannot meet the demands of modern audiences for high quality, multi angle, and real-time performance. In response to these challenges, an innovative radar image processing system based on space cloud computing has been proposed. Compared with traditional radar image processing systems, the system proposed by the research institute had the best performance, with accuracy, recall, and F1 value reaching 97.08%, 96.88%, and 97.11%, respectively, and a transmission time of only 2.2 seconds; and the stability was greater than 90%, which was about 10% to 25% higher than other systems. In summary, the system proposed by the research institute has brought revolutionary improvements to basketball game guidance and filming through its efficient processing capabilities, accurate image recognition, fast data processing and transmission, and excellent stability. This not only greatly enriches the audience's viewing experience, but also opens up new directions for the development of sports event broadcasting technology. With the further maturity of technology and the continuous expansion of applications, it is expected that this system will play a more important role in future sports event broadcasting, promoting the development of the entire industry towards higher quality and efficiency.

Falls in the elderly have become one of the major risks for the growing elderly population. Therefore, the application of automatic fall detection system for the elderly is particularly important. In recent years, a large number of deep learning methods (such as CNN) have been applied to such research. This paper proposed a sparse convolution method 3D Sparse Convolutions and the corresponding 3D Sparse Convolutional Neural Network (3D-SCNN), which can achieve faster convolution at the approximate accuracy, thereby reducing computational complexity while maintaining high accuracy in video analysis and fall detection task. Additionally, the preprocessing stage involves a dynamic key frame selection method, using the jitter buffers to adjust frame selection based on current network conditions and buffer state. To ensure feature continuity, overlapping cubes of selected frames are intentionally employed, with dynamic resizing to adapt to network dynamics and buffer states. Experiments are conducted on Multi-camera fall dataset and UR fall dataset, and the results show that its accuracy exceeds the three compared methods, and outperforms the traditional 3D-CNN methods in both accuracy and losses.

Computer vision-based inspection has become widely used in manufacturing industries for part identification, dimensional inspection, and guiding material handling systems. Defect-free production cannot be achieved with sampling inspection methods; therefore, a 100 percentage inspection approach is mandatory to meet the zero-defect goals of manufacturing industries. Achieving this is possible with advanced technologies, such as vision-based inspection systems. In this study, a vision-based inspection system is proposed for part identification, defect detection, and dimensional measurement. The system is validated using machined parts, including a Druck plate, Pressure plate, and Retainer. A part identification algorithm is developed based on a geometry search approach. The inspection algorithm classifies parts based on edge relationships, utilizing edge detection techniques to identify each part's geometric features. Surface defects are identified by analyzing the pixel intensity gradients within defective regions. The system measures part dimensions using a vision system, with results comparable to those obtained from a coordinate measuring machine.

Aerial images are valuable for observing land, allowing detailed examination of Earth's surface features. As remote sensing (RS) imagery becomes more abundant, there is a growing need to fully utilize these images for smarter Earth observation. Understanding large and complex RS images is crucial. Satellite image scenery categorization, which involves labeling images based on their content, has diverse applications. Deep Learning (DL), using neural networks' powerful attribute learning capabilities, has made significant strides in categorizing satellite imagery scenes. However, recent advances in DL for scenery categorization of RS images are lacking. In our study, we employed three transfer learning (TL) models - VGG16, Densenet201 (D-201), and InceptionV3(IV3) - for classifying aerial images. VGG16 achieved 94% accuracy, while D-201 and IV3 reached 97% accuracy. Combining these models into an ensemble (V3DI ensemble model) improved accuracy to an impressive 99%. This ensemble model combines individual models' classification decisions using majority voting. We demonstrate the efficiency of this approach by showing how ensemble classification accuracy surpasses that of training individual models. Additionally, we preprocess the dataset with a Gabor filter for edge enhancement and denoising to enhance the model's overall performance.

Multi-target tracking has important applications in many fields including logistics and transportation, security systems and assisted driving. With the development of science and technology, multi-target tracking has also become a research hotspot in the field of sports. In this study, a multi-attention module is added to compute the target feature information of different dimensions for the leakage problem of the traditional fifth-generation single-view detection algorithm. The study adopts two-stage target detection method to speed up the detection rate, and at the same time, recursive filtering is utilized to predict the position of the athlete in the next frame of the video. The results indicated that the improved fifth generation monovision detection algorithm possessed better results for target tracking of basketball players. The running time was reduced by 21.26% compared with the traditional fifth-generation monovision detection algorithm, and the average number of images that could be processed per second was 49. The accuracy rate was as high as 98.65%, and the average homing rate was 97.21%. During the tracking process of 60 frames of basketball sports video, the computational delay was always maintained within 40 ms. It can be demonstrated that by deeply optimizing the detection algorithm, the ability to identify and locate basketball players can be significantly improved, which provides a solid data support for the analysis of players' behaviors and tactical layout in basketball games.

Brain tumors (BT) are considered the second-principal cause of human death on our planet. They pose significant challenges in the field of medical diagnosis. Early detection is crucial for effective treatment and improved patient outcomes. As a result, researchers’ studies that deal with tumor detection play a vital role in early disease prediction in the field of medicine. Despite advancements in medical imaging technologies, accurate and efficient classification of BT remains a complex task. This study aims to address this challenge by proposing a novel method for brain tumor classification utilizing ensemble learning techniques combined with feature extraction from neuroimaging data. In the present paper, we present a novel approach for brain tumor classification that contains ensemble learning methods following the extraction of important features from brain tumor images. Our methodology involves the preprocessing of neuroimaging data, followed by feature extraction using descriptor techniques. These extracted features are then utilized as inputs to ensemble learning classifiers. Experimental results demonstrate the efficacy of the proposed approach in accurately classifying brain tumors with high precision and recall rates. The ensemble learning framework, combined with feature extraction, outperforms several benchmark models and methods commonly used in brain tumor classification, including AlexNet, VGG-16, and MobileNet, in terms of classification accuracy and computational efficiency. The proposed method that integrates ensemble learning techniques with feature extraction from neuroimaging data offers a promising solution for improving the accuracy and efficiency of brain tumor diagnosis, thereby facilitating timely intervention and treatment planning. The findings of this study contribute to the advancement of medical imaging-based classification systems for brain tumors, with implications for enhancing patient care and clinical decision-making in neuro-oncology.

In computer vision, Convolutional Neural Networks (CNNs) have become a foundation for image analysis. They excel in tasks such as object recognition, classification, and more, semantic segmentation. In order to achieve better accuracy, it is crucial to apply normalization techniques to the network for enhancing overall performance. This paper introduces an innovative approach that incorporates Batch Group Normalization (BGN) into the popular U-Net for binary semantic segmentation, with a particular focus on aerial road detection. Our research primarily focuses on evaluating the BGN-UNet's performance compared to traditional normalization techniques, such as Batch Normalization (BN) and Group Normalization (GN). With a batch size of 2, the U-Net model enhanced with Batch Group Normalization (BGN-UNet) achieves a remarkable Mean IoU of 98.4% in aerial road segmentation, demonstrating its superior accuracy in this task.