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1 – 10 of over 53000Rajasekar Velswamy, Sorna Chandra Devadass, Karunakaran Velswamy and Jeyakrishnan Venugopal
The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number…
Abstract
Purpose
The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes.
Design/methodology/approach
For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object.
Findings
This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.
Originality/value
Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.
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Keywords
Hung-Cheng Tsai and Yuan-Chin Hsu
It is desirable that shape appeal in craft design takes people's cultural and emotional influencers into consideration. Five Royal Lords worship, prevalent in the southwest…
Abstract
Purpose
It is desirable that shape appeal in craft design takes people's cultural and emotional influencers into consideration. Five Royal Lords worship, prevalent in the southwest coastal part of Taiwan and a source of religious and spiritual support to the local residents, offers such a shape appeal. This study takes the design of Taiwan's cultural handicrafts as the main point of discussion and uses Kansei engineering with semantic technique to promote the linkage between shape and the mental image of the Five Royal Lords' headwear. There are only two types of traditional headwear for the Five Royal Lords: the Imperial Crown and Lord Crown, despite the different personal characteristics of the five deities. This study aims to design a crown for each that matches their individuality.
Design/methodology/approach
In the first stage of the research process, the Kawakita Jiro method was used to arrive at appropriate descriptions representative of the deities' individuality. Fuzzy set theory was then applied to convert the relationship between the representative descriptions and headwear shape features into a quantitative one, after which the headwear could be redesigned and validated.
Findings
The study results show that: (1) analysis of the relationship between shape features and representative deity descriptions offered guidance to the redesign. (2) A method combining fuzzy theory and description terms could generate quantitative data that helped to provide design suggestions and result validation, supporting both scientific rationality and designers' sensibility. (3) The validation revealed that the redesigned headwear was better than the original headwear.
Originality/value
The study successfully established a design and development process featuring collaboration by folklore experts, designers, craftspeople and worshippers, and helping to promote new cultural product development. The success of the research process can serve as a reference to the development of other different products with shape features.
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As an extended work of the previous paper (Sul, 2010), this paper provides a guideline information for an anonymous garment pattern in sewing process. The purpose of this paper is…
Abstract
Purpose
As an extended work of the previous paper (Sul, 2010), this paper provides a guideline information for an anonymous garment pattern in sewing process. The purpose of this paper is to first, provide garment pattern database. By simply taking pictures of garment patterns, the shape database is constructed. Once the shape database is prepared, data retrieval can be done by image indexing, i.e., simply inserting garment pattern boundary shape again to the database. Using shock graph methodology, the pattern sets used for database preparation can be exactly retrieved. Second, to find the nearest shape of a given input pattern shape in the database. If the input garment pattern shape does not exist in the database, the shape matching algorithm provides the next similar pattern data. The user, who is assumed to be non-expert in garment sewing process, can easily predict the position and combination information of various patterns.
Design/methodology/approach
Image processing is used to construct the garment pattern shape database. The boundary shapes are extracted from the photographs of garment patterns and their shape recognition information, especially shock graph, is also recorded for later pattern data retrieval.
Findings
Using the image processing technique, garment patterns can be converted to electronic format easily. Also the prepared pattern database can be used for finding the nearest shape of an additional given input garment pattern. Patterns with irregular shapes were retrieved easily, while those with a simple shape, such as rectangle, showed a little erroneous result.
Originality/value
Shape recognition has been adopted in various industrial areas, except for garment sewing process. Using the provided methodology, garment pattern shapes can be easily saved and retrieved only by taking pictures of them.
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Chen Guodong, Zeyang Xia, Rongchuan Sun, Zhenhua Wang and Lining Sun
Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to…
Abstract
Purpose
Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features.
Design/methodology/approach
The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales.
Findings
The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework.
Originality/value
The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.
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Keywords
Shenlong Wang, Kaixin Han and Jiafeng Jin
In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of…
Abstract
Purpose
In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.
Design/methodology/approach
First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.
Findings
The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.
Originality/value
A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.
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Keywords
Tushar Jain, Meenu Gupta and H.K. Sardana
The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of…
Abstract
Purpose
The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.
Design/methodology/approach
The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.
Findings
Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.
Originality/value
After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.
Details
Keywords
The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are…
Abstract
Purpose
The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.
Design/methodology/approach
Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. In this work, recognition of objects manufactured in mechanical industry is considered. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such mechanical part. Red, green and blue RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.
Findings
One important finding is that there is not any considerable change in the network performances after 500 iterations. It has been found that for data smaller network structure, smaller learning rate and momentum are required. The relative sample size also has a considerable effect on the performance of the classifier. Further studies suggest that classification accuracy is achieved with the confusion matrix of the data used. Hence, with these results the proposed system can be used efficiently for more objects. Depending upon the manufacturing product and process used, the dimension verification and surface roughness may be integrated with proposed technique to develop a comprehensive vision system. The proposed technique is also highly suitable for web inspections, which do not require dimension and roughness measurement and where desired accuracy is to be achieved at a given speed. In general, most recognition problems provide identity of object with pose estimation. Therefore, the proposed recognition (pose estimation) approach may be integrated with inspection stage.
Originality/value
This paper considers the problem of recognizing and classifying the objects of such mechanical part. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. ANN is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects as well as the effect of learning rate and momentum.
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Keywords
Lynda C. Taylor and Robert W. Scapens
The purpose of this paper is to analyse the implementation of a new accounting system in the accounting department of a large retail company. The paper seeks to understand and…
Abstract
Purpose
The purpose of this paper is to analyse the implementation of a new accounting system in the accounting department of a large retail company. The paper seeks to understand and explain how management accounting change can be shaped by the identity and image of particular groups in an organisation.
Design/methodology/approach
This paper reports the findings of a longitudinal explanatory case study. An institutional framework was initially used to inform the research, but was subsequently extended using the concepts of identity and image.
Findings
By changing existing accounting systems, the accountants “inside” the accounting department sought to challenge their current “negative” identity and image. However, the case shows that the new accounting system was not well received by accountants “outside” the accounting department. The case illustrates that the differing identity and image of the two groups of accountants were crucial factors underlying the different perceptions of the accounting change.
Originality/value
The conceptual framework developed in this paper highlights the role which identity and image can play in shaping processes of change, and it enriches the understanding of the reasons for change, stability and resistance to change.
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Keywords
Jayakrishnan Jayapal, Senthilkumaran Kumaraguru and Sudhir Varadarajan
This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The…
Abstract
Purpose
This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.
Design/methodology/approach
The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.
Findings
The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.
Research limitations/implications
The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.
Originality/value
This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.
Details
Keywords
Han Shen, Chengyi Song, Mimi Li and Qian Jiang
SNS, namely social networking sites, has become one of the most effective and fast channels of information diffusion and dissemination. As an influential way of online marketing…
Abstract
SNS, namely social networking sites, has become one of the most effective and fast channels of information diffusion and dissemination. As an influential way of online marketing, SNS has been increasingly used by tourism organizations and enterprises to shape their destination image. On the basis of previews literature of destination image and SNS, this paper used the text analysis software ROST Content Mining (ROST CM) System to do a case study of the SNS destination marketing of Singapore on Chinese market. The authors analyze the text related to Singapore tourism on the major SNS in mainland China: Renren, Sina Weibo, and Douban, through word frequency analysis and the social semantic network, to summarize the destination image of Singapore on SNS. The paper also focuses on the difference of image building by official and individual SNS. Results found by this paper can be used by the relevant tourism organizations and enterprises to improve their destination marketing and image building on SNS channels.
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