Narendra Ahuja

About

Narendra Ahuja is the Donald Biggar Willett Professor Emeritus and Research Professor in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. His research focuses on computer vision, pattern recognition, robotics, image processing, sensors, virtual environments, and intelligent interfaces. He has introduced a new computational approach to automatically extracting the syntax of images for automated image understanding, enabling the discovery, modeling, recognition, and explanation of object categories in arbitrary image sets without supervision, and organizing these categories into taxonomies. Additionally, he developed a Fourier-based formulation for the representation and synthesis of videos of dynamic textures, analyzing dynamic textures in both spatial and temporal domains. Ahuja has co-developed interdisciplinary courses on knowledge networks and image structure, content, and depiction, and has been recognized with numerous awards including the IEEE Emanuel R. Piore Award, SPIE Technology Achievement Award, and fellowships from IEEE, ACM, SPIE, IAPR, and AAAI. His work has significantly contributed to advancing automated image understanding and dynamic texture analysis within the field of computer vision.

Research topics

• Computer Science
• Business
• Systems engineering
• Psychology
• Data science
• Engineering
• Risk analysis (engineering)

Selected publications

• Barriers to computer vision applications in pig production facilities

  Computers and Electronics in Agriculture · 2022 · 38 citations

  Senior authorCorresponding
  PublisherDOI

• Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning

  Journal of Experimental Botany · 2024-10-04 · 15 citations

  reviewOpen access

  Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and l…

  PublisherDOI

• Promote computer vision applications in pig farming scenarios: High-quality dataset, fundamental models, and comparable performance

  Journal of Integrative Agriculture · 2024-08-23 · 12 citations

  articleOpen access

  Computer vision is widely recognized as an influential technology in the field of precision management of animals. Emerging studies have demonstrated the potential to improve pig health and welfare through animal surveillance systems and computer vision (CV) algorithms. However, the lack of benchmark datasets and robust fundamental algorithms restrict CV applications for the commercial use. This study aims to bridge the gap between technology development and commercial applications in pig farmin…

  PublisherDOI

• Improving Multi-label Recognition using Class Co-Occurrence Probabilities

  Lecture notes in computer science · 2024-12-03 · 6 citations

  book-chapterSenior author
  PublisherDOI

• PositiveCoOp: Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations

  2025-02-26 · 3 citations

  articleSenior author

  Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with class presence or absence in the shared vision-text feature space. While this approach improves MLR performance by relying on VLM priors, we hypothesize that learning negative prompts may be suboptimal, as the datasets used to train VLMs lack imagecaptio…

  PublisherDOI

Recent grants

• SGER: Segmentation Trees and Their Robust Matching as Core Technologies for Recognition

  NSF · $100k · 2007–2008

• RI-Small: Discovery, Modeling and Recognition of Objects in Image Sets

  NSF · $611k · 2008–2012

Frequent coauthors

• Ming–Hsuan Yang

  47 shared

• Thomas S. Huang

  34 shared

• Bernard Ghanem

  King Abdullah University of Science and Technology

  33 shared

• Juyang Weng

  29 shared

• Jia‐Bin Huang

  21 shared

• John M. Hart

  University of Illinois Urbana-Champaign

  18 shared

• Qingxiong Yang

  17 shared

• Tianzhu Zhang

  17 shared

Education

• Ph.D., Electrical Engineering

  University of California, Berkeley

  1986

• M.S., Electrical Engineering

  University of California, Berkeley

  1981

• B.S., Electrical Engineering

  Indian Institute of Technology, Kanpur

  1977

Awards & honors

• Best Paper Award from IEEE Transactions on Multimedia, 2006
• Associate in the Center for Advanced Study, 2005-06
• On Incomplete List of Teachers Ranked Excellent by Their Stu…
• 1999 UIUC Campus Award for Guiding Undergraduate Research -…
• 1999 Donald Biggar Willet Professorship of UIUC College of E…

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