Flex Conference (Physical / Digital)

International Conference on Machine Learning for Image Processing and Computer Vision - (ICMLIPCV-26)

21st - 22nd September 2026 , Rio de Janeiro - Brazil

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Call For Papers

The (ICMLIPCV-26) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Computational Science,Data Science are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Deep learning techniques for image recognition
  • Computer vision applications in healthcare
  • Image processing for autonomous vehicles
  • AI in facial recognition technology
  • Ethics of AI in image processing
  • Real-time image analysis in security systems
  • Machine learning for augmented reality applications
  • Data augmentation techniques in image processing
  • Predictive modeling for image classification
  • Challenges in computer vision research
  • AI-driven image enhancement techniques
  • Applications of image processing in agriculture
  • Human-computer interaction and computer vision
  • Visual data analytics for social media
  • Future of AI in image processing
  • Machine learning for satellite imagery analysis
  • Image segmentation techniques in AI
  • Collaborative approaches in computer vision
  • Impact of AI on visual arts
  • Interdisciplinary research in image processing

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.