The mid-level vision toolbox for computing structural properties of real-world images
Mid-level vision is the intermediate visual processing stage for generating representations of shapes and partial geometries of objects. Our mechanistic understanding of these operations is limited, in part, by a lack of computational tools for analyzing image properties at these levels of represent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2023.1140723/full |
| _version_ | 1797687109748785152 |
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| author | Dirk B. Walther Delaram Farzanfar Seohee Han Morteza Rezanejad |
| author_facet | Dirk B. Walther Delaram Farzanfar Seohee Han Morteza Rezanejad |
| author_sort | Dirk B. Walther |
| collection | DOAJ |
| description | Mid-level vision is the intermediate visual processing stage for generating representations of shapes and partial geometries of objects. Our mechanistic understanding of these operations is limited, in part, by a lack of computational tools for analyzing image properties at these levels of representation. We introduce the Mid-Level Vision (MLV) Toolbox, an open-source software that automatically processes low- and mid-level contour features and perceptual grouping cues from real-world images. The MLV toolbox takes vectorized line drawings of scenes as input and extracts structural contour properties. We also include tools for contour detection and tracing for the automatic generation of vectorized line drawings from photographs. Various statistical properties of the contours are computed: the distributions of orientations, contour curvature, and contour lengths, as well as counts and types of contour junctions. The toolbox includes an efficient algorithm for computing the medial axis transform of contour drawings and photographs. Based on the medial axis transform, we compute several scores for local mirror symmetry, local parallelism, and local contour separation. All properties are summarized in histograms that can serve as input into statistical models to relate image properties to human behavioral measures, such as esthetic pleasure, memorability, affective processing, and scene categorization. In addition to measuring contour properties, we include functions for manipulating drawings by separating contours according to their statistical properties, randomly shifting contours, or rotating drawings behind a circular aperture. Finally, the MLV Toolbox offers visualization functions for contour orientations, lengths, curvature, junctions, and medial axis properties on computer-generated and artist-generated line drawings. We include artist-generated vectorized drawings of the Toronto Scenes image set, the International Affective Picture System, and the Snodgrass and Vanderwart object images, as well as automatically traced vectorized drawings of set architectural scenes and the Open Affective Standardized Image Set (OASIS). |
| first_indexed | 2024-03-12T01:13:35Z |
| format | Article |
| id | doaj.art-42f18ca06848478e8046df126285f357 |
| institution | Directory Open Access Journal |
| issn | 2624-9898 |
| language | English |
| last_indexed | 2024-03-12T01:13:35Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj.art-42f18ca06848478e8046df126285f3572023-09-13T19:44:33ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982023-09-01510.3389/fcomp.2023.11407231140723The mid-level vision toolbox for computing structural properties of real-world imagesDirk B. WaltherDelaram FarzanfarSeohee HanMorteza RezanejadMid-level vision is the intermediate visual processing stage for generating representations of shapes and partial geometries of objects. Our mechanistic understanding of these operations is limited, in part, by a lack of computational tools for analyzing image properties at these levels of representation. We introduce the Mid-Level Vision (MLV) Toolbox, an open-source software that automatically processes low- and mid-level contour features and perceptual grouping cues from real-world images. The MLV toolbox takes vectorized line drawings of scenes as input and extracts structural contour properties. We also include tools for contour detection and tracing for the automatic generation of vectorized line drawings from photographs. Various statistical properties of the contours are computed: the distributions of orientations, contour curvature, and contour lengths, as well as counts and types of contour junctions. The toolbox includes an efficient algorithm for computing the medial axis transform of contour drawings and photographs. Based on the medial axis transform, we compute several scores for local mirror symmetry, local parallelism, and local contour separation. All properties are summarized in histograms that can serve as input into statistical models to relate image properties to human behavioral measures, such as esthetic pleasure, memorability, affective processing, and scene categorization. In addition to measuring contour properties, we include functions for manipulating drawings by separating contours according to their statistical properties, randomly shifting contours, or rotating drawings behind a circular aperture. Finally, the MLV Toolbox offers visualization functions for contour orientations, lengths, curvature, junctions, and medial axis properties on computer-generated and artist-generated line drawings. We include artist-generated vectorized drawings of the Toronto Scenes image set, the International Affective Picture System, and the Snodgrass and Vanderwart object images, as well as automatically traced vectorized drawings of set architectural scenes and the Open Affective Standardized Image Set (OASIS).https://www.frontiersin.org/articles/10.3389/fcomp.2023.1140723/fullmid-level visionperceptual groupinggestalt grouping rulescontour drawingsmedial axis transformsymmetry |
| spellingShingle | Dirk B. Walther Delaram Farzanfar Seohee Han Morteza Rezanejad The mid-level vision toolbox for computing structural properties of real-world images Frontiers in Computer Science mid-level vision perceptual grouping gestalt grouping rules contour drawings medial axis transform symmetry |
| title | The mid-level vision toolbox for computing structural properties of real-world images |
| title_full | The mid-level vision toolbox for computing structural properties of real-world images |
| title_fullStr | The mid-level vision toolbox for computing structural properties of real-world images |
| title_full_unstemmed | The mid-level vision toolbox for computing structural properties of real-world images |
| title_short | The mid-level vision toolbox for computing structural properties of real-world images |
| title_sort | mid level vision toolbox for computing structural properties of real world images |
| topic | mid-level vision perceptual grouping gestalt grouping rules contour drawings medial axis transform symmetry |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2023.1140723/full |
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