## 2 Related Work
### Feature Recognition  
Feature recognition is a process of reinterpreting a design model for manufacturing activities automatically [10], and is widely investigated for the segmentation of B-rep model, mesh, and a freeform surface. A number of feature recognition algorithms have been designed in the past few years, and a critical review of feature recognition was presented [10]. [PERSON] et al. [11] presented a surface-based approach for geometric feature recognition for the purposed of automating the process planning of freeform surface machining. The freeform surface is represented by trimmed NURBS, and five geometric features are defined and classified according to the surface normal. [PERSON] and [PERSON] [12] proposed an efficient method for identifying machining features of B-rep model, and different kinds of 3-axis toolpath can be automatically generated for various machining features. [PERSON] and [PERSON] [13] proposed a novel approach for automatic feature recognition from freeform surface represented in STL format. The geometric properties of curvature and normal are employed to define the fundamental shapes of the surface, and two-stage segmentation (dense/coarse mesh segmentation) for the freeform surface is applied. [PERSON] et al. [14] presented an efficient method for feature extraction of the B-rep model based on Gauss and Mean curvature for moldability analysis. [PERSON] et al. [15] described a method of machining feature recognition for a freeform surface based on critical points extracted by Morse theory and the relationship between machining patches. [PERSON] and [PERSON] [16] proposed an algorithm for extracting freeform surface features from the geometry and topology information of a surface automatically. [PERSON] et al. [17] defined a new type of manufacturing feature generally used in aircraft structural model called ribs, and feature recognition for the ribs feature was presented that can be easily used for CNC toolpath programming. [PERSON] et al. [18] proposed a concept of multi-perspective dynamic feature, which can divide a freeform surface into a set of sub-features with dynamic information to support adaptive NC machining applications. Various methods or technologies have been proposed to demonstrate the capability and efficiency of feature recognition for freeform surface applications from the existent literature. In this paper, watershed technology is introduced for feature recognition of T-spline surface due to its robust and successful application for image and mesh segmentation [19; 20; 21; 22; 23; 24; 25].  
Watershed technology has been first defined for image processing [19], and has been extended for mesh segmentation in the last two decades. [PERSON] and [PERSON] [20] firstly extended watershed technology from image processing to mesh segmentation. The total curvature of the surface is employed in this method to divide the surface into regions that are bounded by higher curvature. The proposed method is sensitive to the user-specified threshold. [PERSON] et al. [21] improved the quality of mesh segmentation by increasing the accuracy of the curvature estimates by use of mean, root-mean-square, and absolute curvatures. The program is semi-automatic since the result is also dependent on the user input threshold. [PERSON] and [PERSON] [22] introduced the fast marching watersheds algorithm for mesh segmentation based on human vision theory known as the minima rule. [PERSON] et al. [23] outlined an approach based on mesh connectivity relationships that apply to watershed algorithms to detect prong features. [PERSON] and [PERSON] [24] described a new method for terrain surface segmentation using the mean-curvature watershed, which can produce two sets of terrain units, depressions, and hills. [PERSON] et al. [25] presented a new approach for discrete Morse gradient construction on mesh surface endowed by a scalar function starting from watershed decomposition.