Lequan Min

About

Lequan Min has made foundational contributions to the theory and design of Cellular Neural/Nonlinear Networks (CNNs), a powerful paradigm for image and video signal processing, robotic vision, and biological vision. His research centers on developing robust, universally applicable CNN templates—the core algorithmic “programs” that govern a CNN’s behavior. Min’s key contribution is a systematic, mathematically rigorous framework for designing CNN templates that are not only effective but also robust to parameter variations, a critical requirement for real-world hardware implementation. His most-cited work, “Design for CNN Templates with Performance of Global Connectivity Detection” (2004, 20 citations), established a general method using parameter inequalities to guarantee global connectivity detection. He extended this approach to edge-gray detection, dilation/erosion operations, and fingerprint feature extraction, providing engineers with provably stable designs. By introducing theorems for robustness in uncoupled CNNs and gray-scale processing, Min bridged the gap between theoretical CNN dynamics and practical, reliable applications. His body of work, accumulating over 75 citations, has directly enabled the deployment of CNNs in robust image analysis tasks, solidifying his reputation as a key architect of practical CNN template design.

Research Focus

Key Achievements

Top Papers

1. 1
   Design for CNN Templates with Performance of Global Connectivity Detection

   20 citations · 2004
2. 2
   Design for robustness edgegray detection CNN

   15 citations · 2004
3. 3
   Robustness Designs of a Kind of Uncoupled CNNs with Applications

   13 citations · 2005
4. 4
   ROBUST DESIGNS FOR GRAY-SCALE GLOBAL CONNECTIVITY DETECTION CNN TEMPLATES

   12 citations · 2007
5. 5
   Two Theorems on the Robust Designs for Dilation and Erosion CNNs

   6 citations · 2007
6. 6
   Robust Designs for Fingerprint Feature Extraction CNN with Von Neumann Neighborhood

   4 citations · 2008
7. 7
   Robustness Design for CNN Templates with Performance of Extracting Closed Domain

   3 citations · 2006
8. 8
   Two Theorems on the Robust Designs of a Kind of Uncoupled CNNs with Applications

   3 citations · 2007
9. 9
   Robust Template Designs for Selected Objects Extraction and Masked Object CNNs with Applications

   2 citations · 2007

Key Collaborators

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