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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

Fraction of identified (red), not-identified (green) and unclear (blue) segments for the complete data set (20 subjects, 247 segments each) plotted against their reliability. Fat dots represent averages across reliability intervals [0, 10];[10, 20];···;[150, 160] plotted above their interval centers, lines are the corresponding regression lines. The ability to identify a segment increases with reliability. Grand averages (red: 0.64, green: 0.30, blue: 0.06) for all data are shown, too.
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Figure 4: Fraction of identified (red), not-identified (green) and unclear (blue) segments for the complete data set (20 subjects, 247 segments each) plotted against their reliability. Fat dots represent averages across reliability intervals [0, 10];[10, 20];···;[150, 160] plotted above their interval centers, lines are the corresponding regression lines. The ability to identify a segment increases with reliability. Grand averages (red: 0.64, green: 0.30, blue: 0.06) for all data are shown, too.

Mentions: Subjects many times used different names (e.g., “face” or “head”) to identify a segment, which are equally valid as both describe a valid conceptional entity (a part). Several segments could not always be identified, however. Averaging across all data shows that 64% of the segments could be identified, 30% not, and there were 6% potentially cases for further subdivision. Are these 30% counter-examples against our conjecture or are due to machine vision errors? Thus, we additionally considered the reliability of the individual segments (see Section 2.1). The Kinect sensor produces a discretization error (Smisek et al., 2011) as can be seen by the stripy patterns in Figure 2C (see also yellow box). Due to this, data at larger distances become quadratically more unreliable (see Section 2.1). As a result, for example, two objects will be combined into one segment just due to the fact that the separating concavity cannot be resolved anymore. When considering reliability we find that subjects could more often identify reliable segments (Figure 4, red) and unrecognized cases dropped accordingly (green). Comparing this result again to the segmented example scene (Figure 2D) we find that, indeed, for less reliable segments (red lettering) identification is low or ambivalent as compared to reliable ones.


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

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

Fraction of identified (red), not-identified (green) and unclear (blue) segments for the complete data set (20 subjects, 247 segments each) plotted against their reliability. Fat dots represent averages across reliability intervals [0, 10];[10, 20];···;[150, 160] plotted above their interval centers, lines are the corresponding regression lines. The ability to identify a segment increases with reliability. Grand averages (red: 0.64, green: 0.30, blue: 0.06) for all data are shown, too.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: Fraction of identified (red), not-identified (green) and unclear (blue) segments for the complete data set (20 subjects, 247 segments each) plotted against their reliability. Fat dots represent averages across reliability intervals [0, 10];[10, 20];···;[150, 160] plotted above their interval centers, lines are the corresponding regression lines. The ability to identify a segment increases with reliability. Grand averages (red: 0.64, green: 0.30, blue: 0.06) for all data are shown, too.
Mentions: Subjects many times used different names (e.g., “face” or “head”) to identify a segment, which are equally valid as both describe a valid conceptional entity (a part). Several segments could not always be identified, however. Averaging across all data shows that 64% of the segments could be identified, 30% not, and there were 6% potentially cases for further subdivision. Are these 30% counter-examples against our conjecture or are due to machine vision errors? Thus, we additionally considered the reliability of the individual segments (see Section 2.1). The Kinect sensor produces a discretization error (Smisek et al., 2011) as can be seen by the stripy patterns in Figure 2C (see also yellow box). Due to this, data at larger distances become quadratically more unreliable (see Section 2.1). As a result, for example, two objects will be combined into one segment just due to the fact that the separating concavity cannot be resolved anymore. When considering reliability we find that subjects could more often identify reliable segments (Figure 4, red) and unrecognized cases dropped accordingly (green). Comparing this result again to the segmented example scene (Figure 2D) we find that, indeed, for less reliable segments (red lettering) identification is low or ambivalent as compared to reliable ones.

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