Curvature in image and shape processing

2013 ◽  
Vol 3 (1) ◽  
pp. 131-139
Author(s):  
Yonathan Aflalo ◽  
Anastasia Dubrovina ◽  
Ron Kimmel ◽  
Aaron Wetzler
Keyword(s):  
Shape Processing

Temporal Dynamics of Shape Processing Differentiate Contributions of Dorsal and Ventral Visual Pathways

2019 ◽  
Vol 31 (6) ◽  
pp. 821-836 ◽  
Author(s):  
Elliot Collins ◽  
Erez Freud ◽  
Jana M. Kainerstorfer ◽  
Jiaming Cao ◽  
Marlene Behrmann
Keyword(s):  
Visual Information ◽  
Temporal Dynamics ◽  
Object Perception ◽  
Visual Pathway ◽  
Shape Sensitivity ◽  
Shape Information ◽  
Dorsal Pathway ◽  
Ventral Visual Pathway ◽  
Shape Processing ◽  
Ventral Pathway

Although shape perception is primarily considered a function of the ventral visual pathway, previous research has shown that both dorsal and ventral pathways represent shape information. Here, we examine whether the shape-selective electrophysiological signals observed in dorsal cortex are a product of the connectivity to ventral cortex or are independently computed. We conducted multiple EEG studies in which we manipulated the input parameters of the stimuli so as to bias processing to either the dorsal or ventral visual pathway. Participants viewed displays of common objects with shape information parametrically degraded across five levels. We measured shape sensitivity by regressing the amplitude of the evoked signal against the degree of stimulus scrambling. Experiment 1, which included grayscale versions of the stimuli, served as a benchmark establishing the temporal pattern of shape processing during typical object perception. These stimuli evoked broad and sustained patterns of shape sensitivity beginning as early as 50 msec after stimulus onset. In Experiments 2 and 3, we calibrated the stimuli such that visual information was delivered primarily through parvocellular inputs, which mainly project to the ventral pathway, or through koniocellular inputs, which mainly project to the dorsal pathway. In the second and third experiments, shape sensitivity was observed, but in distinct spatio-temporal configurations from each other and from that elicited by grayscale inputs. Of particular interest, in the koniocellular condition, shape selectivity emerged earlier than in the parvocellular condition. These findings support the conclusion of distinct dorsal pathway computations of object shape, independent from the ventral pathway.

scholarly journals Global shape processing: Which parts form the whole?

2010 ◽  
Vol 10 (6) ◽  
pp. 16-16 ◽  
Author(s):  
J. Bell ◽  
S. Hancock ◽  
F. A. A. Kingdom ◽  
J. W. Peirce
Keyword(s):  
Global Shape ◽  
Shape Processing

Algorithms for Nesting of Ship/Offshore Structural Plates

1991 ◽  
Author(s):  
B. T. Cheok ◽  
A. Y. C. Nee
Keyword(s):  
Feature Extraction ◽  
Search Time ◽  
Computer Search ◽  
Rectangle Packing ◽  
Manual Method ◽  
Input And Output ◽  
Material Usage ◽  
Shape Processing ◽  
Cad Cam ◽  
Cam Systems

Abstract This paper discusses the development of a set of algorithms for the automatic nesting of ship/offshore structural plates. The algorithms are developed to take advantage of the peculiarity of most ship/offshore structural plates with the aims of optimising material usage and minimising computer search time. The parts to be nested are first processed by a shape processing routine which employs a simple feature extraction approach to classify the plates according to predefined rules specially adopted for ship/offshore structural shapes. The most appropriate search path for each class of plates is used to obtain the best enclosing rectangle for similar shapes. The search paths are based on heuristics developed to simulate the manual method used by the human operator. Finally, all the plates are laid out on the stock sheet using a “rectangle packing” approach. A computer package, Patnest-Ship was developed to demonstrate the efficiency of the algorithms and very encouraging results are achieved. The input and output files to and from Patnest-Ship are described in DXF format so that it can be integrated with existing CAD/CAM systems. Pre- and post-processors for this package have been implemented on AutoCAD to permit the user to define the plates and interactively improve on the solutions provided by Patnest-Ship, if necessary.

Numerical Assessment on Edgewise Compressive Strength of Heavy Sandwich Fiberboard

2015 ◽  
Vol 724 ◽  
pp. 74-78
Author(s):  
Guang Jun Hua ◽  
Wei Min Fei ◽  
Ze Shun Liao ◽  
Yong Xie
Keyword(s):  
Compressive Strength ◽  
Sample Size ◽  
Simulation Method ◽  
Large Sample Size ◽  
Heavy Duty ◽  
Material Consumption ◽  
Shape Processing ◽  
Reasonable Choice ◽  
Correct Understanding ◽  
Corrugated Fiberboard

The application status of heavy duty corrugated paperboard and honeycomb fiberboard were reviewed. In order to contrast the edgewise compressive strength of the two typical sandwich fiberboards, the finite element models of honeycomb fiberboard and AAB flute corrugated fiberboard with large sample size were established. By numerical simulation method, the effect of structure on the edgewise compressive strength were decoupled from the factor such as the materials, material consumption, sample size and shape, processing technology and environmental conditions etc. Under the same material, material consumption and sample size, bulking analysis based on numerical method was carried out. The results show that the edgewise compressive strength of both sides of the honeycomb fiberboard is about 50% higher than that of AAB flute corrugated fiberboard, and honeycomb fiberboard is similar to bi-isotropic material. The conclusions obtained are valuable to reasonable choice of the honeycomb fiberboard and heavy duty corrugated fiberboard and correct understanding the mechanical properties of the two sandwich fiberboard.

scholarly journals Capsule Networks as Recurrent Models ofGrouping and Segmentation

2019 ◽  
Author(s):  
Adrien Doerig ◽  
Lynn Schmittwilken ◽  
Bilge Sayim ◽  
Mauro Manassi ◽  
Michael H. Herzog
Keyword(s):  
Neural Networks ◽  
Visual System ◽  
Convolutional Neural Networks ◽  
Human Vision ◽  
Global Shape ◽  
Grouping And Segmentation ◽  
Segmentation Process ◽  
Shape Processing ◽  
Visual Crowding ◽  
Recurrent Connections

AbstractClassically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we previously showed that no classic model of vision, including ffCNNs, can explain human global shape processing (1). Here, we show that Capsule Neural Networks (CapsNets; 2), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. We also show that ffCNNs and standard recurrent CNNs do not, suggesting that the grouping and segmentation capabilities of CapsNets are crucial. Furthermore, we provide psychophysical evidence that grouping and segmentation are implemented recurrently in humans, and show that CapsNets reproduce these results well. We discuss why recurrence seems needed to implement grouping and segmentation efficiently. Together, we provide mutually reinforcing psychophysical and computational evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.Author SummaryFeedforward Convolutional Neural Networks (ffCNNs) have revolutionized computer vision and are deeply transforming neuroscience. However, ffCNNs only roughly mimic human vision. There is a rapidly expanding body of literature investigating differences between humans and ffCNNs. Several findings suggest that, unlike humans, ffCNNs rely mostly on local visual features. Furthermore, ffCNNs lack recurrent connections, which abound in the brain. Here, we use visual crowding, a well-known psychophysical phenomenon, to investigate recurrent computations in global shape processing. Previously, we showed that no model based on the classic feedforward framework of vision can explain global effects in crowding. Here, we show that Capsule Neural Networks (CapsNets), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. ffCNNs and recurrent CNNs with lateral and top-down recurrent connections do not, suggesting that grouping and segmentation are crucial for human-like global computations. Based on these results, we hypothesize that one computational function of recurrence is to efficiently implement grouping and segmentation. We provide psychophysical evidence that, indeed, grouping and segmentation is based on time consuming recurrent processes in the human brain. CapsNets reproduce these results too. Together, we provide mutually reinforcing computational and psychophysical evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.

Studies on Softening Kinetics of Niobium Microalloyed Steel Using Stress Relaxation Technique

2012 ◽  
Vol 715-716 ◽  
pp. 794-799 ◽  
Author(s):  
Cheng Liang Miao ◽  
Cheng Jia Shang ◽  
Guo Dong Zhang ◽  
Guo Hui Zhu ◽  
Hatem S. Zurob ◽  
...  
Keyword(s):  
Stress Relaxation ◽  
Microalloyed Steels ◽  
Distinct Advantage ◽  
Low Carbon ◽  
Shape Processing ◽  
Physically Based ◽  
Near Net Shape ◽  
Niobium Microalloyed Steel ◽  
Relaxation Curves ◽  
Kinetics Of

Stress relaxation was studied in a series of low carbon, high Mn microalloyed steels containing 0.012, 0.06 and 0.1 wt% Nb. The stress-relaxation curves were modeled using a physically-based model that takes into account the time evolution of precipitation, recovery and recrystallization as well as their interactions. The results confirm that high Mn-high Nb design can offer distinct advantage over the low-Mn design for the application of near net shape processing.

Sign in / Sign up

Export Citation Format

Share Document