Modelling Multi-Feature Active Contours

Active contours are a powerful method used in image and video processing as well as computer vision for applications such as segmentation and tracking of non-rigid objects. Formally, active contours are deformable curves that evolve in the image plane from an initial position to the foreground boundaries, characterizing then the shape and the location of target objects. Hence, this technique is well suited for accurate delineation of deformable objects evolving in scenes captured by either static or mobile cameras. However, most of the current approaches rely on one single feature, and then they do not usually offer enough robustness in highly-complex environments. In these difficult situations, combining several types of information could potentially increase global system performance. Few works on active contours use multiple features. Mostly, those approaches suffer from a lack of generality and/or flexibility as they propose some solutions for only some specific feature associations. Other methods try to sequentially pre/post incorporate features to the active contour technique. This leads to an incomplete or redundant use of information carried in the different features and a considerable rise in computational load.

My presented approach for multi-feature active contours mainly differs in that it proposes the combination of multiple features into a unified, generic, and mathematical framework I called Multi-Feature Vector Flow (MFVF). By means of MFVF, features of different structure, nature, and level could be homogeneously integrated into the core itself of the active contour process. The resulting multi-feature active contours were successfully tested for detection and extraction of objects with highly-changing shape and appearance in real-world image and video sequences. As demonstrated, the proposed system presents high accuracy and strong robustness in complex natural situations, while being computationally efficient.