I. INTRODUCTION

II. TAXONOMY OF SEGMENTATION ALGORITHMS
A. Allowed classes
B. Class affiliation of pixels
D. Operation state

III. EVALUATION AND DATASETS
A. Quality measures for evaluation
B. Datasets

IV. SEGMENTATION PIPELINE

V. TRADITIONAL APPROACHES
A. Features and Preprocessing methods
B. Unsupervised Segmentation
D. SVMs
F. Conditional Random Fields
G. Post-processing methods

VI. NEURAL NETWORKS FOR SEMANTIC SEGMENTATION

VII. POSSIBLE PROBLEMS IN THE DATA FOR SEGMENTATION ALGORITHMS
A. Lens Flare
B. Vignetting
C. Blurred images
D. Other Problems

VIII. DISCUSSION