Edge Detection method better than Canny Edge detection

There are different types of "edges", it depends on your task. Have a look at the recent paper "Which edges matters?" from ICCV-2013, with comparison of several methods:

• ultrametric contour map - "Contour Detection and Hierarchical Image Segmentation" by P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik - best results in comparison above.
• normalized cuts - "Normalized cuts and image segmentation" by J. Shi and J. Malik.
• mean shift - "Mean shift: A robust approach toward feature space analysis" by D. Comanicu and P. Meer.
• Felzenszwalb and Huttenlocher approach - "Efficient graph-based image segmentation" by Felzenszwalb and Huttenlocher.
• BiCE - "Binary coherent edge descriptors" by C. L. Zitnick.
• N4-Fields - "N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms" by Ganin et.al
• RDS - "Learning relaxed deep supervision for better edge detection" by Liu and Lew
• COB - "Convolutional Oriented Boundaries" by Maninis et.al.

Hope this helps future reader

Active Canny: Edge Detection and Recovery with Open Active Contour Models

Here is an image showing its performance

Implementing it is a pain. I'm trying to implement it using OpenCV and Python

Here's another paper I found.

Anisotropic Edge-Based Balloon Eikonal Active Contours