The Perception of Surface Folding in Static and Animated Displays

Perception ◽  
1997 ◽  
Vol 26 (2) ◽  
pp. 153-170 ◽  
Author(s):  
Manfredo Massironi ◽  
Nicola Bruno

How do we interpret outline drawings of surfaces? Although pictorial depictions are projectively ambiguous, observers demonstrate definite preferences of interpretation. Additionally, they commit typical errors. A study is reported of one specific arrangement of surfaces as it is represented in outline drawings, namely the arrangement that results when two arbitrary surfaces are joined at a common edge to form an angle in 3-D (‘phenomenic folding’). With some of these arrangements, observers report that the angle formed by the two surfaces is zero (complete folding). With others, they report that the angles are greater than zero (incomplete folding). Both interpretations are actually valid. Several investigators have proposed that observer preferences such as these are due to a tendency to prefer a 3-D interpretation that will make the depicted 3-D shape regular. Three experiments were performed to test this regularisation hypothesis. In the first, observers were shown pairs of four-sided polygons joined at one equal side. Their task was to imagine how the smaller polygon could be folded completely towards the larger, and, subsequently, to report on its position after the folding (‘mental folding’). Reported positions were consistent with 3-D interpretations that caused figural regularisations. In the second and third experiments, observers were shown drawings of diamonds and parallelograms folded along a number of differently positioned and oriented segments (‘folding edge’). Their task was to estimate verbally the extent of the dihedral angle formed by the two surfaces. Results indicated that the perception of incomplete folding is determined by 3-D interpretation of the orientation of the drawing with respect to the picture plane. In a fourth experiment, observers were asked whether projective equivalences might be disambiguated by animating two kinds of displays that yield the ‘incomplete folding’ effect but that should be distinguishable on the basis of the trajectories of the vertexes of the folding parts. Results demonstrated that even in these conditions observers are unable to interpret the foldings correctly. These results might be taken to indicate that projective, static information leading to a simpler and more regular interpretation of the display can prevail over explicit motion information that should force the system to achieve a nonregular solution.


2013 ◽  
Vol 69 (11) ◽  
pp. o1632-o1632
Author(s):  
Hakima Chicha ◽  
El Mostapha Rakib ◽  
Latifa Bouissane ◽  
Mohamed Saadi ◽  
Lahcen El Ammari

In the title compound, C14H12ClN3O3S, the fused five- and six-membered rings are folded slightly along the common edge, forming a dihedral angle of 3.2 (1)°. The mean plane through the indazole system makes a dihedral angle of 30.75 (7)° with the distant benzene ring. In the crystal, N—H...O hydrogen bonds link the molecules, forming a two-dimensional network parallel to (001).



1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)



2009 ◽  
Vol 129 (5) ◽  
pp. 977-984
Author(s):  
Atsutoshi Shimeno ◽  
Seiichi Uchida ◽  
Ryo Kurazume ◽  
Rin-ichiro Taniguchi ◽  
Tsutomu Hasegawa


2004 ◽  
Vol 95 (1) ◽  
pp. 3-7 ◽  
Author(s):  
P. Chhillar ◽  
S. Sangal ◽  
A. Upadhyaya


2009 ◽  
Vol 109 (1) ◽  
pp. 35-42 ◽  
Author(s):  
A. V. Kol’tsov ◽  
A. V. Serov


2015 ◽  
Vol 71 (12) ◽  
pp. o917-o918 ◽  
Author(s):  
Shaaban K. Mohamed ◽  
Joel T. Mague ◽  
Mehmet Akkurt ◽  
Eman A. Ahmed ◽  
Mustafa R. Albayati

The title compound, C19H17NO7, crystallized in a ratio of about 6:4 of the two possible keto–enol forms. This was observed as disorder over the central C3H2O2unit. The dihedral angle between the rings is 8.2 (2)°.The molecules pack by C—H...O interactions in a layered fashion parallel to (-104).



Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.





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