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Perceptual influence of elementary three-dimensional geometry: (2) fundamental object parts.

Tamosiunaite M, Sutterlütti RM, Stein SC, Wörgötter F - Front Psychol (2015)

Bottom Line: Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability.In addition, we observed that using other image-segmentation methods will not yield nameable entities.This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Physics - Biophysics and Bernstein Center for Computational Neuroscience, University of Göttingen Göttingen, Germany ; Department of Informatics, Vytautas Magnus University Kaunas, Lithuania.

ABSTRACT
Objects usually consist of parts and the question arises whether there are perceptual features which allow breaking down an object into its fundamental parts without any additional (e.g., functional) information. As in the first paper of this sequence, we focus on the division of our world along convex to concave surface transitions. Here we are using machine vision to produce convex segments from 3D-scenes. We assume that a fundamental part is one, which we can easily name while at the same time there is no natural subdivision possible into smaller parts. Hence in this experiment we presented the computer vision generated segments to our participants and asked whether they can identify and name them. Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability. In addition, we observed that using other image-segmentation methods will not yield nameable entities. This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities. It appears that other or further subdivisions do not carry such a strong semantical link to our everyday language as there are no names for them.

No MeSH data available.


Related in: MedlinePlus

Humans can with high reliability identify image segments that result from splitting images along concave-convex surface transitions. (A) One example scene used for analysis. (B) Color-based segmentation of the scene. (C) Point cloud image of parts of the scene (rotated 3D view) with RGB data overlayed. (D) 3D-segmented scene and segment names used by our subjects to identify objects. Missing percentages are the non-named cases. E.g., the pink segment top-left was named “cupboard” by 60% of the subjects and remained unidentified by the remaining 40%. Red lettering indicates segments with reliability less than 50.
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Figure 2: Humans can with high reliability identify image segments that result from splitting images along concave-convex surface transitions. (A) One example scene used for analysis. (B) Color-based segmentation of the scene. (C) Point cloud image of parts of the scene (rotated 3D view) with RGB data overlayed. (D) 3D-segmented scene and segment names used by our subjects to identify objects. Missing percentages are the non-named cases. E.g., the pink segment top-left was named “cupboard” by 60% of the subjects and remained unidentified by the remaining 40%. Red lettering indicates segments with reliability less than 50.

Mentions: One example scene is shown in Figure 2A recorded with an RGB-D sensor (“Kinect”), which produces 3D-point cloud data. All other scenes are of equal complexity (Figure 3). Using an advanced, model-free color-based segmentation method (Ben Salah et al., 2011) one can see that the resulting image segments rarely correspond to objects in the scene (Figure 2B) and this is also extremely dependent on illumination (see Figure 3, middle). Unwanted merging or splitting of objects will, regardless of the chosen segmentation parameters, generically happen (e.g., “throat+face,” “fridge-fragments,” etc. Figure 2B).


Perceptual influence of elementary three-dimensional geometry: (2) fundamental object parts.

Tamosiunaite M, Sutterlütti RM, Stein SC, Wörgötter F - Front Psychol (2015)

Humans can with high reliability identify image segments that result from splitting images along concave-convex surface transitions. (A) One example scene used for analysis. (B) Color-based segmentation of the scene. (C) Point cloud image of parts of the scene (rotated 3D view) with RGB data overlayed. (D) 3D-segmented scene and segment names used by our subjects to identify objects. Missing percentages are the non-named cases. E.g., the pink segment top-left was named “cupboard” by 60% of the subjects and remained unidentified by the remaining 40%. Red lettering indicates segments with reliability less than 50.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585234&req=5

Figure 2: Humans can with high reliability identify image segments that result from splitting images along concave-convex surface transitions. (A) One example scene used for analysis. (B) Color-based segmentation of the scene. (C) Point cloud image of parts of the scene (rotated 3D view) with RGB data overlayed. (D) 3D-segmented scene and segment names used by our subjects to identify objects. Missing percentages are the non-named cases. E.g., the pink segment top-left was named “cupboard” by 60% of the subjects and remained unidentified by the remaining 40%. Red lettering indicates segments with reliability less than 50.
Mentions: One example scene is shown in Figure 2A recorded with an RGB-D sensor (“Kinect”), which produces 3D-point cloud data. All other scenes are of equal complexity (Figure 3). Using an advanced, model-free color-based segmentation method (Ben Salah et al., 2011) one can see that the resulting image segments rarely correspond to objects in the scene (Figure 2B) and this is also extremely dependent on illumination (see Figure 3, middle). Unwanted merging or splitting of objects will, regardless of the chosen segmentation parameters, generically happen (e.g., “throat+face,” “fridge-fragments,” etc. Figure 2B).

Bottom Line: Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability.In addition, we observed that using other image-segmentation methods will not yield nameable entities.This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Physics - Biophysics and Bernstein Center for Computational Neuroscience, University of Göttingen Göttingen, Germany ; Department of Informatics, Vytautas Magnus University Kaunas, Lithuania.

ABSTRACT
Objects usually consist of parts and the question arises whether there are perceptual features which allow breaking down an object into its fundamental parts without any additional (e.g., functional) information. As in the first paper of this sequence, we focus on the division of our world along convex to concave surface transitions. Here we are using machine vision to produce convex segments from 3D-scenes. We assume that a fundamental part is one, which we can easily name while at the same time there is no natural subdivision possible into smaller parts. Hence in this experiment we presented the computer vision generated segments to our participants and asked whether they can identify and name them. Additionally we control against segmentation reliability and we find a clear trend that reliable convex segments have a high degree of name-ability. In addition, we observed that using other image-segmentation methods will not yield nameable entities. This indicates that convex-concave surface transition may indeed form the basis for dividing objects into meaningful entities. It appears that other or further subdivisions do not carry such a strong semantical link to our everyday language as there are no names for them.

No MeSH data available.


Related in: MedlinePlus