scholarly journals A Perceptually Motivated Three-Component Image Model

Video Coding ◽  
1996 ◽  
pp. 337-382
Author(s):  
Nariman Farvardin ◽  
Xiaonong Ran
Keyword(s):  
Image Model ◽  
Component Image

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

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.

Explanation of Color Lines Based on a Simple Color Image Model

2018 ◽  
Vol 2018 (16) ◽  
pp. 296-1-296-5
Author(s):  
Megan M. Fuller ◽  
Jae S. Lim
Keyword(s):  
Color Image ◽  
Image Model

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

Information ◽  
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.

Computer Investigation of a Latent-Image Model*

1965 ◽  
Vol 55 (4) ◽  
pp. 439 ◽  
Author(s):  
B. E. Bayer ◽  
J. F. Hamilton
Keyword(s):  
Image Model ◽  
Latent Image ◽  
Computer Investigation
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