You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: K3M: A universal algorithm for image skeletonization and a review of thinning techniques 
Chapter Title:  Algorithm description. 
1. Abstract of Cited Paper (BIB001):  Abstract This paper describes a systematic approach for designing parallel thinning algorithms, in which three new functions, named local connecting, extended local connecting and erosive direction number, are introduced. With these functions as well as two properties of shape invariance of local edges and local straight lines, all the possible cases of 2-subcycle parallel thinning algorithm are constructed and all the corresponding removing conditions are generated and assigned automatically. In addition, the pseudo 1-subcycle parallel thinning algorithm is also presented. Finally, the effects and efficiency of the above proposed algorithms are analyzed and compared with those of some presently well-known algorithms. Experimental results confirm this new approach, and an efficient and effective algorithm has been built for practical applications. 
2. Abstract of Cited Paper (BIB002):  Many algorithms for vectorization by thinning have been devised and applied to a great variety of pictures and drawings for data compression, pattern recognition and raster-to-vector conversion. But parallel thinning algorithms which generate one-pixel-wide skeletons can have difficulty preserving the connectivity of an image. In this paper, we propose a 2-subiteration parallel thinning algorithm with template matching (PTA2T) which preserves image connectivity, produces thinner results, maintains very fast speed and generates one-pixel-wide skeletons. 
3. Abstract of Cited Paper (BIB003):  The authors propose a thinning algorithm based on clustering the image data. They employ the ART2 network which is a self-organizing neural network for the clustering of Arabic characters. The skeleton is generated by plotting the cluster centers and connecting adjacent clusters by straight lines. This algorithm produces skeletons which are superior to the outputs of the conventional algorithms. It achieves a higher data-reduction efficiency and much simpler skeletons with less noise spurs. Moreover, to make the algorithm appropriate for real-time applications, an optimization technique is developed to reduce the time complexity of the algorithm. The developed algorithm is not limited to Arabic characters, and it can also be used to skeletonize characters of other languages.

Literature Review:
K3M consists of an iterative part of seven phases and a single additional phase at the end, which is responsible for producing a one pixelwidth skeleton. The overview of the algorithm is presented in Fig. 17 .  iterative, 2-subcycles does not give a one-pixel-wide skeleton 1989 BIB001 BIB002 1-subcycle, parallel bias-reduced skeletons 1994  force-based acquires a skeleton by using force vectors 1996 BIB003 2-subcycles, parallel preserves connectivity, produces one-pixel-wide skeletons 1998 BIB004