A function that takes (Formula presented.) numbers as input and outputs one number is said to be homogeneous whenever the result of multiplying each input by a certain factor (Formula presented.) yields the original output multiplied by that same factor. This concept has been extended by the notion of abstract homogeneity, which generalizes the product in the expression of homogeneity by a general function (Formula presented.) and the effect of the factor (Formula presented.) by an automorphism. However, the effect of parameter (Formula presented.) remains unchanged for all the input values. In this study, we generalize further the condition of abstract homogeneity by introducing (Formula presented.) -homogeneity, which is defined with respect to a family of functions, enabling a different behavior for each of the inputs. Next, we study the properties that are satisfied by this family of functions and, moreover, we link this concept with the condition of directional monotonicity, which is a trendy property in the framework of aggregation functions. To achieve that, we generalize directional monotonicity by (Formula presented.) directional monotonicity, which is defined with respect to a family of functions (Formula presented.) and a family of vectors (Formula presented.). Finally, we show how the introduced concepts could be applied in two different problems of computer vision: a snow detection problem and image thresholding improvement. © 2022 The Authors. International Journal of Intelligent Systems published by Wiley Periodicals LLC.

This study was supported by National Council for Scientific and Technological Development (CNPq) within the project 312053/2018‐5, Coordination for the Improvement of Higher Education Personnel (CAPES) within the project Capes‐Print 88887.363001/2019‐00, the Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project PID2019‐108392GB‐I00, financed by MCIN/AEI/10.13039/501100011033, VEGA 1/0267/21, and APVV‐18‐0052.