Shape Matching for Foliage Database Retrieval

2011 ◽  
pp. 100-129 ◽  
Author(s):  
Haibin Ling ◽  
David W. Jacobs
Keyword(s):  
Shape Matching ◽  
Shape Context ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Texture Information ◽  
Speed Up ◽  
Guide System ◽  
Plant Species Identification ◽  
Reliable Solution ◽  
Electronic Field

Computer-aided foliage image retrieval systems have the potential to dramatically speed up the process of plant species identification. Despite previous research, this problem remains challenging due to the large intra-class variability and inter-class similarity of leaves. This is particularly true when a large number of species are involved. In this chapter, the authors present a shape-based approach, the inner-distance shape context, as a robust and reliable solution. The authors show that this approach naturally captures part structures and is appropriate to the shape of leaves. Furthermore, they show that this approach can be easily extended to include texture information arising from the veins of leaves. They also describe a real electronic field guide system that uses our approach. The effectiveness of the proposed method is demonstrated in experiments on two leaf databases involving more than 100 species and 1,000 leaves.

Non-Subsampled Contourlet Texture Retrieval Using Four Estimators

2012 ◽  
Vol 263-266 ◽  
pp. 167-170 ◽  
Author(s):  
Xin Wu Chen ◽  
Jing Ge ◽  
Jin Gen Liu
Keyword(s):  
Wavelet Transform ◽  
Standard Deviation ◽  
Digital Images ◽  
Contourlet Transform ◽  
Retrieval Algorithm ◽  
Retrieval Rate ◽  
Retrieval Systems ◽  
Texture Information ◽  
Shift Invariance ◽  
Texture Retrieval

Contourlet transform is superior to wavelet transform in representing texture information and sparser in describing geometric structures in digital images, but lack of robust character of shift invariance. Non-subsampled contourlet transform (NSCT) alleviates this shortcoming hence more suitable for texture and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rates of existing contourlet transforms retrieval systems, a new texture retrieval algorithm was proposed. In the algorithm, texture information was represented by four statistical estimators, namely, L2-energy, kurtosis, standard deviation and L1-energy of each sub-band coefficients in NSCT domain. Experimental results show that the new algorithm can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today.

Development of Automatic Storage Retrieval System for Variable Loads

2021 ◽  
Vol 18 (1) ◽  
pp. 99
Author(s):  
Afzeri Tamsir
Keyword(s):  
Data Storage ◽  
Integrated System ◽  
Numerical Control ◽  
Storage And Retrieval ◽  
Retrieval Systems ◽  
Speed Up ◽  
Automated Storage ◽  
Warehousing Systems ◽  
And Storage ◽  
Carrier Element

 Automated Storage and Retrieval Systems (ASRS) have been widely used in warehousing systems to speed up load movements and save storage space. ASRS is an integrated system that is equipped with a controller and arm for the collection and storage of goods. This paper discusses the results of developing a system for taking and storing goods for various loads. The prototype element consists of a mechanism for retrieving, placing and application for data collection into the database. In this research, the design and development of ASRS was carried out to be applied in the storage of products of various sizes which is suitable for small size industries. The development process includes investigating features that have been developed in the ASRS, operating procedures, hardware selection and software development in accordance with the mechanism designed. Numerical control which moves the carrier element with high resolution is applied to be able to place the load in a changing position. Development and testing is carried out to ensure the performance of the tool runs well and the data storage that includes the identification and size of the load can be recorded properly.

Kansei Database and AR*-Tree for Speeding up the Retrieval

2010 ◽  
pp. 111-125 ◽  
Author(s):  
Yaokai Feng
Keyword(s):  
Information Retrieval ◽  
Key Words ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Multidimensional Indexing ◽  
General Flow ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Speed Up ◽  
Text Information

Along with Kansei information being successfully introduced to information retrieval systems, particularly multimedia retrieval systems, many Kansei retrieval systems have been implemented in the past two decades. And, it has become clear that the traditional multimedia retrieval systems using key-words or/and other text information are not enough in many applications, because that they can not deal with sensitive words reflecting user’s subjectivity. In this chapter, Kansei retrieval systems efficiently taking user’s subjectivity into account will be discussed in detail. Like many traditional retrieval systems, Kansei retrieval systems are also based on databases system, which are called Kansei databases. After roughly introducing some existing Kansei retrieval systems is a general flow for designing Kansei retrieval systems. Also, we will discuss how to speed up the Kansei retrieval systems by using multidimensional indexing technologies and you will learn that our proposed multidimensional index structure, Adaptive R*-tree (AR*-tree for short), is more suitable to Kansei retrieval systems than the traditional multidimensional indexing technologies.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

Shape Matching in Pose Reconstruction Using Shape Context

2006 ◽  
Author(s):  
Chungang Hao ◽  
Xianjie Qiu ◽  
Zhaoqi Wang ◽  
Shengjian Chen
Keyword(s):  
Shape Matching ◽  
Shape Context

scholarly journals A morphing approach for continuous generalization of linear map features

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243328
Author(s):  
Aji Gao ◽  
Jingzhong Li ◽  
Kai Chen
Keyword(s):  
Shape Matching ◽  
Map Generalization ◽  
Shape Context ◽  
Chi Square ◽  
Linear Map ◽  
Continuous Map ◽  
Shape Structure ◽  
Limited Scale ◽  
Map Data ◽  
Shape Characteristics

With the development of web maps, people are no longer satisfied with fixed and limited scale map services but want to obtain personalized and arbitrary scale map data. Continuous map generalization technology can be used to generate arbitrary scale map data. This paper proposes a morphing method for continuously generalizing linear map features using shape context matching and hierarchical interpolation (SCM-HI). More specifically, shape characteristics are quantitatively described by shape context on which shape similarity is measured based on a chi-square method; then, two levels of interpolation, skeleton and detail interpolations, are employed to generate the geometry of intermediate curves. The main contributions of our approach include (1) exploiting both the geometry and spatial structure of a vector curve in shape matching by using shape context, and (2) preserving both the main shape structure as-rigid-as-possible and local geometric details as gradual and smooth as possible for intermediate curves by hierarchical interpolation. Experiments show that our method generates plausible morphing effects and can thus serve as a robust approach for continuous generalization of linear map features.

A Content-Based Approach to Medical Image Database Retrieval

2009 ◽  
pp. 1062-1083
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 258-292 ◽  
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

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