Analysis of Dot Micro-Morphology for Image Replication Quality Evaluation

2015 ◽  
Vol 731 ◽  
pp. 135-140
Author(s):  
Qi Wang ◽  
Qian Wang ◽  
Xiao Lin Qiu ◽  
Tian Ma Wang
Keyword(s):  
Three Dimensional ◽  
Test System ◽  
Two Dimensional ◽  
Value Differences ◽  
Printing Quality ◽  
Replication Quality ◽  
Dimensional Shape ◽  
Micro Morphology ◽  
Three Dimensional Shape

Dot is the smallest structural unit of image printing. Micro-characteristic of each printing dot such as spreading area, dot shape and three-dimensional shape which are formed on the surface of substrates affect the image replication quality directly. It is significant to accurately extract and analyze dot microstructure in the process of image transfer for evaluating the quality of image replication .Different shapes of printing dots from the standard offset proof are extracted through microscopic test system. The optimal threshold segmentation algorithm was determined by experiment which gets accurate microscopic quantitative value of the dot, the obtained two-dimensional and three-dimensional shape of printing dot became the basis to evaluate the quality of image replication. The between-cluster variance method is used for dot image segmentation. The research was carried on to track image edge after image threshold and to extract the characteristic parameters to accurately characterize the two-dimensional shape, thus characterizing the image replication quality of dot plane form accordingly; on the basis of collected dot two-dimensional data, the experiment was conducted to combine with gray value differences of single dot caused from ink accumulation within its shape areas. Besides, dot density contour and three-dimensional data were quantized by programming in order to restore three-dimensional shape of printing dot on the surface of substrates quantitatively and intuitively. It shows that quality of dot micro-morphology is the fundamental guarantee of image replication quality. The changes of dot shape, area as well as three-dimensional shape in the process of image replication affect the printing quality. Extraction and analysis dot microstructure can detect dot replication quality intuitively which becomes new method to evaluate image replication quality.

Design of a Two-Piece Brassiere Cup From its Data Points Toward its Automation

2020 ◽  
Author(s):  
Kotaro Yoshida ◽  
Hidefumi Wakamatsu ◽  
Eiji Morinaga ◽  
Takahiro Kubo
Keyword(s):  
Three Dimensional ◽  
Developable Surface ◽  
Two Dimensional ◽  
Trial And Error ◽  
Design Efficiency ◽  
Developable Surfaces ◽  
Data Points ◽  
Dimensional Shape ◽  
The Given ◽  
Three Dimensional Shape

Abstract A method to design the two-dimensional shapes of patterns of two piece brassiere cup is proposed when its target three-dimensional shape is given as a cloud of its data points. A brassiere cup consists of several patterns and their shapes are designed by repeatedly making a paper cup model and checking its three-dimensional shape. For improvement of design efficiency of brassieres, such trial and error must be reduced. As a cup model for check is made of paper not cloth, it is assumed that the surface of the model is composed of several developable surfaces. When two lines that consist in the developable surface are given, the surface can be determined. Then, the two-piece brassiere cup can be designed by minimizing the error between the surface and given data points. It was mathematically verified that the developable surface calculated by our propose method can reproduce the given data points which is developable surface.

Are Mechanisms for Perception of Biological Motion Different from Mechanisms for Perception of Nonbiological Motion?

2002 ◽  
Vol 95 (3_suppl) ◽  
pp. 1301-1310 ◽  
Author(s):  
Leo Poom ◽  
Henrik Olsson
Keyword(s):  
Biological Motion ◽  
Three Dimensional ◽  
Constant Time ◽  
Coherent Motion ◽  
Two Dimensional ◽  
Dimensional Shape ◽  
Perception Of Biological Motion ◽  
Three Dimensional Shape ◽  
Over Time ◽  
Unidirectional Motion

We compared the integration of information over space and time for perceiving different configurations of moving dots: a walking person (biological motion), rigid three-dimensional shapes, and unidirectional coherent motion of all dots (translation). No performance differences in judging walking direction and coherent translation direction were obtained in conditions with constant presentation times and varying number of target dots (integration over space). Depending on the speed of the two-dimensional configurations judgments were either worse or better than the judgments of walking direction. The results for conditions with different presentation times (integration over time) show that information about biological motion is integrated over time that increases with increasing gait period, while two-dimensional unidirectional motion is integrated over constant time independent of speed. The effect is not due to the oscillatory nature of the biological motion since information about a rigid three-dimensional shape is summed over a constant time independent of the period of the motion cycle. This could be interpreted as different neural mechanisms mediating the temporal summation for walking direction compared to detecting the orientation of rigid structure, or the direction of two-dimensional unidirectional motion. Since biological motion is characterized by nonrigidity, it is possible that the form itself is integrated over time and not the motion pattern.

scholarly journals Two-Dimensional Kinetic Shape Dynamics: Verification and Application

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Ismet Handzic ◽  
Haris Muratagic ◽  
Kyle B. Reed
Keyword(s):  
Equations Of Motion ◽  
Three Dimensional ◽  
Smooth Curve ◽  
Two Dimensional ◽  
Physical Experiments ◽  
Kinematic Behavior ◽  
Force Reaction ◽  
Reaction Forces ◽  
Dimensional Shape ◽  
Three Dimensional Shape

A kinetic shape (KS) is a smooth two- or three-dimensional shape that is defined by its predicted ground reaction forces as it is pressed onto a flat surface. A KS can be applied in any mechanical situation where position-dependent force redirection is required. Although previous work on KSs can predict static force reaction behavior, it does not describe the kinematic behavior of these shapes. In this article, we derive the equations of motion for a rolling two-dimensional KS (or any other smooth curve) and validate the model with physical experiments. The results of the physical experiments showed good agreement with the predicted dynamic KS model. In addition, we have modified these equations of motion to develop and verify the theory of a novel transportation device, the kinetic board, that is powered by an individual shifting their weight on top of a set of KSs.

scholarly journals The perception of shape from shading in a new light

2013 ◽  
Author(s):  
Michael J Proulx
Keyword(s):  
Bayesian Models ◽  
Three Dimensional ◽  
Internal Representation ◽  
Shape From Shading ◽  
Two Dimensional ◽  
Persuasive Power ◽  
Dimensional Shape ◽  
External Light Source ◽  
First Time ◽  
Three Dimensional Shape

How do humans see three-dimensional shape based on two-dimensional shading? Much research has assumed that a ‘light from above’ bias solves the ambiguity of shape from shading. Counter to the ‘light from above’ bias, studies of Bayesian priors have found that such a bias can be swayed by other light cues. Despite the persuasive power of the Bayesian models, many new studies and books cite the original ‘light from above’ findings. Here I present a version of the Bayesian result that can be experienced. The perception of shape-from-shading based was found here to be influenced by an external light source, even when the light was obstructed and did not directly illuminate a two-dimensional stimulus. The results imply that this effect is robust and not low-level in nature. The perception of shape from shading is not necessarily based on a hard-wired internal representation of lighting direction, but rather assesses the direction of lighting in the scene adaptively. Here, for the first time, is an experiential opportunity to see what the Bayesian models have supported all along.

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