New Generative Image Model for Variational Autoencoders Based on Counts Partition

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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Explanation of Color Lines Based on a Simple Color Image Model

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.16.color-296 ◽

2018 ◽

Vol 2018 (16) ◽

pp. 296-1-296-5

Author(s):

Megan M. Fuller ◽

Jae S. Lim

Keyword(s):

Color Image ◽

Image Model

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A Social Media Mining and Ensemble Learning Model: Application to Luxury and Fast Fashion Brands

Information ◽

10.3390/info12040149 ◽

2021 ◽

Vol 12 (4) ◽

pp. 149

Author(s):

Yulin Chen

Keyword(s):

Public Participation ◽

Brand Positioning ◽

Image Model ◽

Web Crawler ◽

Fast Fashion ◽

Social Media Mining ◽

Fashion Brand ◽

Community Information ◽

Brand Information ◽

Media Mining

This research proposes a framework for the fashion brand community to explore public participation behaviors triggered by brand information and to understand the importance of key image cues and brand positioning. In addition, it reviews different participation responses (likes, comments, and shares) to build systematic image and theme modules that detail planning requirements for community information. The sample includes luxury fashion brands (Chanel, Hermès, and Louis Vuitton) and fast fashion brands (Adidas, Nike, and Zara). Using a web crawler, a total of 21,670 posts made from 2011 to 2019 are obtained. A fashion brand image model is constructed to determine key image cues in posts by each brand. Drawing on the findings of the ensemble analysis, this research divides cues used by the six major fashion brands into two modules, image cue module and image and theme cue module, to understand participation responses in the form of likes, comments, and shares. The results of the systematic image and theme module serve as a critical reference for admins exploring the characteristics of public participation for each brand and the main factors motivating public participation.

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Computer Investigation of a Latent-Image Model*

Journal of the Optical Society of America ◽

10.1364/josa.55.000439 ◽

1965 ◽

Vol 55 (4) ◽

pp. 439 ◽

Cited By ~ 35

Author(s):

B. E. Bayer ◽

J. F. Hamilton

Keyword(s):

Image Model ◽

Latent Image ◽

Computer Investigation

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A generalized Gaussian image model for edge-preserving MAP estimation

IEEE Transactions on Image Processing ◽

10.1109/83.236536 ◽

1993 ◽

Vol 2 (3) ◽

pp. 296-310 ◽

Cited By ~ 574

Author(s):

C. Bouman ◽

K. Sauer

Keyword(s):

Map Estimation ◽

Image Model ◽

Edge Preserving ◽

Gaussian Image

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Automatic verification method of progressive image model matching information based on machine learning

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215741 ◽

2021 ◽

pp. 1-10

Author(s):

Jie Yuan ◽

Yuan Ji ◽

Zhou Zhu ◽

Liya Huang ◽

Junfeng Qian ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Design Method ◽

Large Error ◽

Model Matching ◽

Target Image ◽

Image Model ◽

Verification Method ◽

Low Performance

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.

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Some Experiments on Artificially Roughened Lucy Ashton Geosims

Journal of Ship Research ◽

10.5957/jsr.1980.24.3.170 ◽

1980 ◽

Vol 24 (03) ◽

pp. 170-180

Author(s):

A.J. Smits ◽

N. Matheson ◽

P.N. Joubert

Keyword(s):

Pipe Flow ◽

Wave Drag ◽

Mirror Image ◽

Turbulent Pipe Flow ◽

Viscous Resistance ◽

Local Parameter ◽

Image Model ◽

Outer Flow ◽

Total Drag ◽

Towing Tank

A mirror-image model of the Lucy Ashton was covered with two different "mesh-type" roughnesses and tested in a wind tunnel. Total drag and velocity profile measurements are presented. Total drag results are also given for a normal Lucy Ashton model with the same roughness mesh tested in a towing tank. The influence of roughness on the wave drag component was found to be small, implying that the "interdependence" of wavemaking and viscous resistance is small. The importance of considering roughness as a local parameter is demonstrated, and the roughness function results agree well with the fully developed turbulent pipe flow results of Perry and Abell, even though the ship boundary-layer outer flow did not follow any recognized wake function.

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