Xiaobao Yang | Computer Vision | Research Excellence Award

Research Excellence Award

Xiaobao Yang
Xi’an University of Posts & Telecommunications, China
Xiaobao Yang
Affiliation Xi’an University of Posts & Telecommunications
Country China
Google Scholar ID
ubUno0kAAAAJ
h-index 7
Citations 289
10h-index 6
Subject Area Computer Vision
Event Computer Scientists Award
ORCID
0000-0003-1515-8663

Xiaobao Yang is a researcher affiliated with Xi’an University of Posts & Telecommunications, China, whose scholarly activities are associated with the field of computer vision and intelligent image analysis. His academic profile reflects contributions to visual computing methodologies, machine learning applications, and image processing research within contemporary computational science environments. This academic recognition article has been prepared in relation to the Research Excellence Award under the Computer Scientists Award initiative.[1]

Abstract

This academic article presents a structured recognition profile of Xiaobao Yang, emphasizing scholarly contributions to computer vision research and intelligent computational methodologies. The profile evaluates academic visibility through citation performance, publication activity, and interdisciplinary engagement in visual computing systems. Particular attention is given to computer vision applications, machine learning integration, and image interpretation technologies relevant to contemporary computational science research.[2][3]

Keywords

Computer Vision; Image Processing; Machine Learning; Visual Computing; Artificial Intelligence; Deep Learning; Pattern Recognition; Computational Imaging; Academic Recognition; Research Excellence Award.

Introduction

Computer vision has become a foundational discipline within artificial intelligence and computational science, enabling automated interpretation of visual information through machine learning and pattern recognition techniques. Researchers in this field contribute to applications involving intelligent systems, visual analytics, autonomous technologies, and digital image understanding.[3]

Xiaobao Yang’s academic profile reflects engagement with research themes associated with visual computing, image analysis methodologies, and intelligent information processing. His scholarly activities contribute to the broader advancement of computer vision research and interdisciplinary computational technologies.[1]

Research Profile

Xiaobao Yang is affiliated with Xi’an University of Posts & Telecommunications, an academic institution engaged in engineering, communication technologies, and computational sciences research. His academic profile demonstrates participation in computer vision studies and intelligent image processing investigations within contemporary scientific environments.[1]

Citation indicators associated with the researcher suggest measurable scholarly visibility within computer science and visual computing domains. The recorded h-index and citation count reflect continuing academic engagement and research dissemination across indexed scientific publications.[1]

The researcher’s ORCID registration additionally supports international academic discoverability and standardized scholarly identification across research databases and publication systems.[4]

Research Contributions

The research contributions associated with Xiaobao Yang are connected with computational image analysis, visual information processing, and machine learning integration within computer vision systems. Such contributions are relevant to the development of intelligent recognition frameworks and automated visual interpretation technologies.[2]

Research in computer vision frequently involves deep learning methodologies, feature extraction systems, and pattern recognition techniques designed to improve the performance and reliability of intelligent computational models. These studies support technological innovation in image classification, object detection, and data-driven visual analytics.[5]

His scholarly activities contribute to the broader scientific dialogue surrounding intelligent computing systems and interdisciplinary artificial intelligence research applications.[3]

Publications

Xiaobao Yang has contributed to scientific publications associated with computer vision and computational imaging research. His publication activity reflects participation in scholarly communication within artificial intelligence and intelligent systems research domains.[1]

  • Research publications related to computer vision algorithms and intelligent image analysis systems.[2]
  • Studies concerning machine learning integration in visual computing and pattern recognition applications.[5]
  • Academic works contributing to image processing methodologies and artificial intelligence research communication.[3]

The publication profile demonstrates continued engagement with scientific dissemination and interdisciplinary collaboration within modern computational research environments.[1]

Research Impact

Research impact within computer vision is frequently evaluated through publication accessibility, citation performance, and interdisciplinary applicability. Xiaobao Yang’s scholarly indicators suggest continued engagement within visual computing research networks and computational science communities.[1]

Computer vision methodologies contribute substantially to advancements in intelligent automation, digital imaging systems, autonomous technologies, and data interpretation frameworks. Research activities in this domain support innovation across engineering, healthcare, communication systems, and artificial intelligence applications.[5]

The researcher’s academic visibility is additionally strengthened through indexed citation systems, ORCID registration, and scholarly dissemination within internationally accessible research platforms.[4]

Award Suitability

The academic profile of Xiaobao Yang reflects several characteristics associated with research excellence recognition frameworks, including scholarly publication activity, measurable citation performance, and engagement with interdisciplinary computer vision research initiatives.[1]

His work in visual computing and intelligent image analysis aligns with the objectives commonly emphasized by international scientific award platforms that recognize innovation, computational research quality, and technological advancement.[6]

The researcher’s institutional affiliation, publication activity, and integration within global scholarly indexing systems collectively support consideration for recognition through the Research Excellence Award initiative.[6]

Conclusion

Xiaobao Yang represents an active academic presence within the field of computer vision and intelligent computational systems. His scholarly contributions, citation profile, and publication activities demonstrate sustained engagement with visual computing research and interdisciplinary artificial intelligence methodologies.[1]

This recognition article highlights the researcher’s academic profile within modern computational science environments and emphasizes the continuing significance of computer vision technologies in contemporary research and technological innovation frameworks.[3]

References

  1. Google Scholar. (n.d.). Scholar profile: Xiaobao Yang.
    https://scholar.google.com/citations?hl=fr&user=ubUno0kAAAAJ
  2. Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer.
    https://doi.org/10.1007/978-3-030-34372-9
  3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.https://doi.org/10.1109/CVPR.2016.90

Mr. Sina Rezaei | Computer Vision | Research Excellence Award

Mr. Sina Rezaei | Computer Vision | Research Excellence Award

Engineer | University of Tehran | Iran

Mr. Sina Rezaei Nafar is a researcher in photogrammetry, computer vision, and geomatics engineering, with a strong focus on 3D reconstruction, semantic segmentation, and AI-driven analysis of spatial data. His work integrates spherical and fisheye camera systems, low-cost imaging sensors, and machine learning models to enhance point cloud accuracy, urban mapping, and cultural heritage documentation. His research has been published in high-impact international journals, including Sensors, addressing camera calibration, network design, and point cloud quality assessment. According to Google Scholar, his scholarly output includes multiple indexed documents with 12 citations and an h-index of 2, reflecting growing academic impact across photogrammetry and computer vision research.

Citation Metrics (Google Scholar)

15

10

5

0

Citations
12
i10-index
0
h-index
2
🟦 Citations    🟥 i10-index    🟩 h-index

View Google Scholar Profile
View ORCID Profile

Featured Publications

Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Associate Professor | Shanghai University | China

Prof. Mengmeng Liao is an accomplished researcher in artificial intelligence, computer vision, pattern recognition, and image processing, with a strong record of contributions to both foundational and applied aspects of visual computing. His work focuses on developing robust algorithms for face recognition, multi-resolution modeling, adaptive subspace learning, and representation learning, addressing complex challenges in real-world environments such as noise interference, limited samples, and multi-pose variation. He has authored more than 20 SCI/EI-indexed research papers, including publications in leading international journals such as Information Sciences, Neurocomputing, Expert Systems with Applications, Electronics, and IEEE Signal Processing Letters. His research impact is reflected in Scopus metrics, with 170 citations across 159 citing documents and an h-index of 6, alongside a growing presence on Google Scholar. Prof. Liao has also contributed to several major national research initiatives, securing competitive funding from programs such as the National Natural Science Foundation and the Postdoctoral Innovative Talent Support Program. His active engagement with the global academic community includes serving as a technical committee member, session chair, and program chair for numerous international conferences. Through his interdisciplinary approach and sustained research output, Prof. Liao continues to advance the field of artificial intelligence, particularly in intelligent visual perception, pattern learning, and computational recognition systems.

Publication Profile

Scopus

Publications

Fan, X., Liao, M., Chen, L., & Hu, J. (2023). Few-shot learning for multi-POSE face recognition via hypergraph de-deflection and multi-task collaborative optimization. Electronics.

Liao, M., Fan, X., Li, Y., & Gao, M. (2023). Noise-related face image recognition based on double dictionary transform learning. Information Sciences.

Fan, X., Liao, M., Xue, J., Wu, H., Jin, L., Zhao, J., & Zhu, L. (2023). Joint coupled representation and homogeneous reconstruction for multi-resolution small sample face recognition. Neurocomputing.

Liao, M., Li, Y., & Gao, M. (2022). Graph-based adaptive and discriminative subspace learning for face image clustering. Expert Systems with Applications.

Jiang, W., Li, Y., Liao, M., & Wang, S. (2021). An improved LPI radar waveform recognition framework with LDC-Unet and SSR-Loss. IEEE Signal Processing Letters.

 

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Associate Professor | Changwon National University | South Korea

Prof. Joongrock Kim is an accomplished researcher and Associate Professor in Artificial Intelligence Convergence Engineering at Changwon National University, Republic of Korea. His expertise spans computer vision, 3D scene understanding, deep learning-based perception, and intelligent systems for automotive and consumer applications. Over his distinguished career, he has contributed significantly to the development of advanced AI technologies, including driver monitoring systems, 3D reconstruction, food recognition, and smart V2X perception systems. His research focuses on integrating multimodal sensing, neural rendering, and adaptive feature extraction for robust real-world perception, bridging academia and industry to advance AI deployment in smart vehicles and appliances. Dr. Kim’s prolific output includes numerous high-impact publications and international patents on AI-based sensing and perception systems. According to Scopus, he has achieved 212 citations across 207 documents with an h-index of 7, while his Google Scholar profile reflects broader academic engagement and influence. His work continues to drive innovation in perception AI, human–machine interaction, and computational imaging, establishing him as a leading figure in applied artificial intelligence and computer vision research.

Profile

Scopus

Featured Publications

Park, M., Do, M., Shin, Y. J., Yoo, J., Hong, J., Kim, J., & Lee, C. (2024). H2O-SDF: Two-phase learning for 3D indoor reconstruction using object surface fields. International Conference on Learning Representations (ICLR).

Kim, J., Yu, S., Kim, D., Toh, K.-A., & Lee, S. (2017). An adaptive local binary pattern for 3D hand tracking. Pattern Recognition.

Kim, J., Yoon, C. (2016). Three-dimensional head tracking using adaptive local binary pattern in depth images. International Journal of Fuzzy Logic and Intelligent Systems.

Kim, K., Kim, J., Choi, J., Kim, J., & Lee, S. (2015). Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions. Sensors.

Kim, J., Yu, S., & Lee, S. (2014). Random-profiles-based 3D face recognition system. Sensors.

Yinlei Cheng | Computer Vision | Best Researcher Award

Dr. Yinlei Cheng | Computer Vision | Best Researcher Award

Beijing Institute Of Fashion Technology | China

Dr. Yinlei Cheng is a dedicated postgraduate researcher at the Beijing Institute of Fashion Technology, specializing in artificial intelligence and innovative design. With a strong academic foundation in engineering and computing, he has developed expertise in deep learning, computer vision, and intelligent image processing. His research journey is marked by active involvement in collaborative projects bridging academia and industry, where he has focused on real-world challenges such as intelligent fabric recognition and fault diagnosis systems. Driven by a passion for research and innovation, he continues to explore advanced computational methods that contribute to both theoretical understanding and practical applications.

Publication Profile

Scopus

Education Background

Dr. Yinlei Cheng completed his undergraduate engineering studies at Shandong Jiaotong University, where he established a strong base in technology and problem-solving. He is currently pursuing a master’s degree at the School of Liberal Arts and Sciences, Beijing Institute of Fashion Technology, advancing his academic career with a focus on artificial intelligence applications. His educational path highlights a consistent pursuit of excellence, blending technical knowledge with practical applications in computer vision and image processing. Through this background, he has been able to integrate academic learning with innovative research contributions, strengthening his expertise in both theory and practice.

Professional Experience

Dr. Yinlei Cheng has been actively engaged in research-driven projects with direct industry relevance, showcasing his ability to apply cutting-edge methods to solve complex problems. His work on the intelligent fabric piece grasping system demonstrated his skill in combining deep learning and machine vision for non-rigid object recognition and automation. He also contributed to developing a portable fault diagnosis software system designed to provide real-time monitoring and predictive analysis of industrial equipment. These experiences reflect his growing professional maturity and highlight his potential to bridge academic research with practical industry solutions, ensuring his contributions have both scientific and applied value.

Awards and Honors

While Dr. Yinlei Cheng is still at an early stage in his research career, he has already achieved recognition through his publication in a peer-reviewed international journal indexed in high-ranking databases. His academic contributions, particularly in advancing activation functions for convolutional neural networks, have been cited by other researchers, reflecting the growing impact of his work. His dedication to refining theoretical insights and combining them with rigorous experimental validation has positioned him as a promising researcher. Although formal awards may not yet fully represent his contributions, his publication record and involvement in impactful projects underline his academic excellence.

Research Focus

The central focus of Dr. Yinlei Cheng’s research lies in computer vision, deep learning, and image processing, with a particular interest in designing intelligent systems for real-world applications. His work explores innovative activation functions to enhance the performance of convolutional neural networks, contributing both theoretical advancements and practical improvements. He also applies these concepts to industrial applications, such as automation in flexible manufacturing and predictive fault detection systems. By balancing theoretical depth with practical deployment, his research adds value to both academia and industry. His ongoing efforts aim to extend these methodologies to more advanced architectures and transformative technologies.

Publication Notes

Title: A Periodic Mapping Activation Function: Mathematical Properties and Application in Convolutional Neural Networks
Published Year: 2025
Citation: 1

Conclusion

Dr. Yinlei Cheng’s academic journey reflects a balance of solid educational grounding, active participation in significant projects, and meaningful contributions to the field of artificial intelligence. His work demonstrates the ability to translate theoretical research into applied solutions that address complex industry challenges. With an expanding publication record and growing recognition, he shows strong potential to emerge as a leading researcher in computer vision and deep learning. His commitment to rigorous research, clarity in academic writing, and focus on future innovations position him as a deserving candidate for recognition in the Best Researcher Award category.

Mr. Ayush Roy | Computer Vision | Young Researcher Award

Mr. Ayush Roy | Computer Vision | Young Researcher Award

PhD, University at Buffalo, United States

Ayush Roy is an emerging researcher and innovator in the field of Electrical Engineering with a deep interest in AI, computer vision, and biomedical image analysis. Currently pursuing his B.E. at Jadavpur University, he has demonstrated exceptional potential through interdisciplinary research, AI-driven solutions, and impactful contributions to both academia and real-world applications. With multiple international publications and recognitions, Ayush is a dynamic force in the intersection of deep learning, signal processing, and intelligent systems.

Publication Profile

Google Scholar

🎓 Education Background

Ayush Roy is a final-year undergraduate student at Jadavpur University, West Bengal, India, enrolled in the Bachelor of Engineering (Electrical) program with an SGPA of 8.1/10 (2020–2024). He completed his schooling from Bhartiya Vidya Bhavan, West Bengal under the CBSE board, scoring 90.6% in Class 12 and a perfect CGPA of 10 in Class 10.

💼 Professional Experience

Ayush’s research journey began at Jadavpur University, working under renowned professors in Audio Signal Processing, Reinforcement Learning, and Image Segmentation. As a research intern at the Indian Statistical Institute, he contributed to dataset development and text detection models. He furthered his research as an intern at the University of Malaya on transformer-based networks and at IISc Bangalore on CLIP for image quality assessment. His work integrates deep learning models like YOLO, Swin Transformer, UNet, and CLIP with novel architectures and real-world applications.

🏆 Awards and Honors

Ayush has earned several accolades such as the Most Innovative Solution award at Hack-a-Web by NIT Bhopal (2021), 3rd Prize at FrostHack, IIT Mandi (2022), Top 10 in Cloud Community Hackday by GDG Cloud, and became a Finalist in both the IEEE R10 Robotics Competition and 404 Resolved hackathon at IIT Delhi.

🔬 Research Focus

His primary research areas include computer vision, medical image segmentation, scene text detection, and real-time AI systems. He is especially focused on lightweight models, attention mechanisms, domain adaptation, and hybrid approaches combining deep learning and signal processing. He has created multiple datasets for benchmarking including those for drone license plate detection, underwater text, water meter digit recognition, and circuit component recognition.

📌 Conclusion

Ayush Roy stands as a committed and creative researcher, blending electrical engineering fundamentals with cutting-edge AI methodologies. His work not only adds value to academic literature but also paves the way for practical, socially impactful AI systems. With an impressive early-career portfolio, Ayush continues to show immense promise for future contributions to science and technology.

📚 Top Publication Notes 

AWGUNet: Attention-aided Wavelet Guided U-net for nuclei segmentation in histopathology images

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 2 articles (Google Scholar)

A Wavelet Guided Attention Module for Skin Cancer Classification

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 1 article (Google Scholar)

A New Lightweight Attention-based Model for Emotion Recognition Using Distorted Social Media Images

Year: 2023

Journal/Conference: ACPR 2023

Cited By: 3 articles

Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

Year: 2023

Conference: ICDAR WML 2023

Cited By: 1 article

A Lightweight Script Independent Scene Text Style Transfer Network

Year: 2024

Journal: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Cited By: 1 article

Identification and Classification of Human Mental Stress using Physiological Data

Year: 2022

Conference: IEEE CATCON 2022

Cited By: 4 articles

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 2 articles

An Attention-based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 1 article

DAU-Net: Dual Attention-aided U-Net for Segmenting Tumor Region in Breast Ultrasound Images

Year: 2023

Journal: PLOS ONE

Cited By: 6 articles