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Grouping in vision can be seen as the process that organizes image entities into higher-level structures. Despite its importance, there is little consistency in the statement of the grouping problem in literature. In addition, most grouping algorithms in vision are inspired on a specific technique, rather than being based on desired characteristics, making it cumbersome to compare the behavior of various methods. We discuss six precisely formulated considerations for the design of generic grouping algorithms in vision: proper definition, invariance, multiple interpretations, multiple solutions, simplicity and robustness. We observe none of the existing algorithms for grouping in vision meet all the considerations. We present a simple algorithm as an extension of a classical algorithm, where the extension is based on taking the considerations into account. The algorithm is applied to three examples: grouping point sets, grouping poly-lines, and grouping flow-field vectors. The complexity of the greedy algorithm is O(nOG), where OG is the complexity of the grouping measure.