An Improved Gray Image Representation Method Based on Binary-Bit Plane Decomposition

2008 ◽  
Author(s):  
Yunping Zheng ◽  
Chuanbo Chen ◽  
Mudar Sarem
Keyword(s):  
Image Representation ◽  
Gray Image ◽  
Bit Plane ◽  
Representation Method

An Improved RNAMC Image Representation

2011 ◽  
Vol 143-144 ◽  
pp. 746-749
Author(s):  
Yun Ping Zheng ◽  
Zu Jia Li ◽  
Mudar Sarem ◽  
Qing Hong Yang ◽  
Xiu Xiu Liao
Keyword(s):  
Image Quality ◽  
Image Representation ◽  
Experimental Results ◽  
Reconstructed Image ◽  
Bit Rate ◽  
Gray Image ◽  
Packing Model ◽  
Representation Method ◽  
Improved Algorithm

In this paper, by controlling the ratio of the length and the width of a homogenous block, we proposed an improved algorithm for the gray image representation by using the Rectangular Non-symmetry and Anti-packing Model Coding (RNAMC) and extended shading approach, which is called the IRNAMC image representation method. Also, we present an IRNAMC representation algorithm of gray images. By comparing our proposed IRNAMC method with the conventional S-Tree Coding (STC) method, the experimental results presented in this paper show that the former can significantly reduce the lower bit rate and the number of homogenous blocks than the latter whereas remaining the satisfactory image quality. Also, the experimental results show that by controlling the ratio of the length and the width, we can improve the reconstructed image quality of the RNAMC method.

A Novel Gray Image Representation Method Based on NAM Using Nonoverlapping Square Subpatterns

2011 ◽  
Vol 143-144 ◽  
pp. 760-764
Author(s):  
Jie He ◽  
Yun Ping Zheng ◽  
Hui Guo
Keyword(s):  
Data Storage ◽  
Time Complexity ◽  
Image Representation ◽  
Gray Image ◽  
Total Data ◽  
Representation Method ◽  
Storage Structures ◽  
Image Pattern ◽  
Test Objects ◽  
Typical Test

In this paper, we propose a novel gray image representation method based on the non-symmetry and anti-packing model (NAM) by using the nonoverlapping square subpatterns, which is called the square NAM for gray images (SNAMG) representation method. Also, a SNAMG representation algorithm is put forward and the storage structures, the total data amount, and the time complexity of the proposed algorithm are analyzed in detail. By taking some standard gray images, such as ‘F16’ and ‘Peppers’, as the typical test objects, and comparing the proposed algorithm with those of the triangle NAM for gray images (TNAMG) and the classic linear quadtree (LQT), the theoretical and experimental results show that the former is obviously superior to the latter with respect to the numbers of subpatterns (nodes) and the data storage, and therefore it is a better method to represent the gray image pattern.

scholarly journals Image-making and Architecture: A Digital Medium for Qualitative Design Representative

2020 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Dewa Ayu Ketut Septika
Keyword(s):  
Architectural Design ◽  
Rapid Development ◽  
Image Representation ◽  
Object Oriented ◽  
Digital Medium ◽  
Design Representation ◽  
Architectural Representation ◽  
Qualitative Representation ◽  
Representation Method

Architecture is inseparable from visual aspects in the form of representation as a way to establish communication. Due to digitalization and the rapid development of technology, there has been a shift in the paradigm of image representation. Manual images become digital images, from sketching and drawing to image-making and rendering. Digital rendering is considered to be object-oriented and quantitative. It has the characteristics of being precise, fast, reflecting form and materiality, with a photorealistic image as a result. There is no visible involvement of subjects such as architects, image-maker, and observers in the process because the image represents the final outcome. Here, the role of representation to evoke imagination and deeper interpretation is lost, because it does not leave room for intervention and contemplation of the design. That is why another kind of digital method emerged in the world of representation, instead of “rendering” the design as an image, it is relevant to the act of “making” an image as design representation. Its characteristic as a representation method makes the produced images have the ability to be evocative, in order to make its viewers contemplate and interpret the design, which makes it a qualitative representation. The aim of this paper is to understand the act of image-making through digital collage as a medium for qualitative representation in architectural design. By comparing digital rendering and collage images obtained through literature studies, this paper wants to offer the author’s viewpoint on the qualities and experiences brought by architectural representation.

scholarly journals Research on Vocabulary Sizes and Codebook Universality

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Wei-Xue Liu ◽  
Jian Hou ◽  
Hamid Reza Karimi
Keyword(s):  
Image Representation ◽  
Classification Performance ◽  
Image Feature ◽  
Vocabulary Size ◽  
Image Descriptors ◽  
Knn Classifier ◽  
Single Dataset ◽  
Representation Method ◽  
Almost All ◽  
Local Image

Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works on codebooks, the building of the visual vocabulary follows a basic routine, that is, extracting local image descriptors and clustering with a user-designated number of clusters. The problem with this routine lies in that building a codebook for each single dataset is not efficient. In order to deal with this problem, we investigate the influence of vocabulary sizes on classification performance and vocabulary universality with the kNN classifier. Experimental results indicate that, under the condition that the vocabulary size is large enough, the vocabularies built from different datasets are exchangeable and universal.

A robust image representation method against illumination and occlusion variations

2021 ◽  
pp. 104212
Author(s):  
Jin Tan ◽  
Taiping Zhang ◽  
Linchang Zhao ◽  
Xiaoliu Luo ◽  
Yuan Yan Tang
Keyword(s):  
Image Representation ◽  
Robust Image ◽  
Representation Method
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