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This paper aims to study the appearance of drills from one brand by using currently available design tools. It aims to find and discuss the relationship between appearance innovation and maintaining key design features.

The innovation process is studied on drills of a Czech power tool maker and a previously created concept of a new drill. First, the authors explore the similarities between the designed concept and previous models of the brand by calculating the degree of similarity of given shape features. Second, they capture the drills simple shape grammar and strive to generate a sketch of the concept.

Results show the use of several similar shape features from previous models in the innovated design. Shape grammar can create a principally similar concept, but some innovations cannot be achieved this way. A description of appearance innovation within brand identity in terms of shape grammar is given.

The research is limited mainly to a small group of previous products that can be analyzed. It is done only for one particular brand identity. When used with the shape grammars, design generation is limited.

Better understanding of the innovative process aids designers in working with designs for brand identity and may serve to shape grammar enhancement.

The paper describes what happens during the innovation of product appearance and implicates enhancement and meaning of design analysis done by shape grammars and exploring similarities.

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.

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.

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.

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.

The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning.

To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations.

The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers.

The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations.

AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

Capturing consumers’ notice by differentiating a product from competing brands in attaching an affixed label featuring product claims, as an alternative front-of-package (FOP) cue, has been widely used in fast-moving consumer goods retailing. This paper aims to apply perceived product newness as the basis for examining how affixed labeling, manipulated in terms of design features and message claims, can impact consumer evaluation.

Four between-subjects experiments examined the persuasive impact of the use of affixed labels. In particular, how product evaluation, in response to affixed labeling, varied as a function of its shape (Study 1a), location (Study 1b), the combination of shape and location cues (Study 1c) and the strength of message claims conveyed by such labels (Study 2). Perceived product newness is assessed as a mediator for all studies.

The results show the power of affixed labels in persuasion. Specifically, consumers tend to perceive the item as newer, achieving persuasion, when the affixed label has a distinctive shape or location. Yet, incorporating several unusual design components fails to trigger an elevated result if a singular visual stimulus serves as a cue for an item’s newness. Further, the strength of claims highlighted in an affixed label correlates to positive impact on evaluations.

This study offers an empirically based examination of consumers’ responses to affixed labeling and identifies perceived product newness as a mediator of the observed effect.

A salient, affixed label enables a credible cue for product newness, therefore, driving evaluation.

This paper contributes to understanding the influence on the persuasion of FOP labeling, with salience to retail promotional and sales messaging tactics.

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.

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.

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.

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.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination.

The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined.

A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency.

The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

The purpose of this paper is to discuss the relationship between bra's visual impression and bra parts, and then to explore the application of visual impression in bra design.

Firstly, 82 female undergraduates are asked to answered this questionnaire online, which is about the importance of parts in bra design. In the part of data analysis, the method of principal part analysis (PCA) are used to get the relationship between bra's parts, and reduce dimension of factors that influence bra design. After that, those group of features are further discussed from the perspective of visual design. Finally, design an application based on conclusion.

To get the influence features of bra appearance and improve the visual design effect, this paper matches the bra parts with visual features (color, texture, shape and space) and presents four main features of bra design: “color,” “visual texture,” “design shape” and “spatial expression” together with corresponding bra parts and technique of expression. Moreover, user interface in bra cloud customization is designed.

The conclusion, which shows the corresponding relationship between bra visual effect and its basic parts, has an important role in bra visual design. First, it can be useful for design idea with different technique of expression, which may supply a theoretical basis for design. Secondly, the combination of bra parts and visual features can be used to evaluate the appearance.

Discussing the bra visual impression based on bra's basic parts and visual features provides a theoretical method for bra design and its appearance evaluation.

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.

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.

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.

The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.

Forensic dentistry is the application of dentistry in legal proceedings that arise from any facts relating to teeth. The ultimate goal of forensic odontology is to identify the individual when there are no other means of identification such as fingerprint, Deoxyribonucleic acid (DNA), iris, hand print and leg print. The purpose of selecting dental record is for the teeth to be able to withstand decomposition, heat degradation up to 1600 ^°C. Dental patterns are unique for every individual. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic dental images for person identification.

To achieve an accurate identification of individuals, the missing tooth in the radiograph has to be identified before matching of ante-mortem (AM) and post-mortem (PM) radiographs. To identify whether the missing tooth is a molar or premolar, each tooth in the given radiograph has to be classified using a k-nearest neighbor (k-NN) classifier; then, it is matched with the universal tooth numbering system. In order to make exact person identification, this research work is mainly concentrate on contour shape extraction and texture feature extraction for person identification. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic images for individual identification. Then, shape matching of AM and PM images is performed by similarity and distance metric for accurate person identification.

The experimental results are analyzed for shape and feature extraction of both radiographic and photographic dental images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model, and it is well suited for forensic odontologists to identify a person in mass disaster situations.

Forensic odontology is a branch of human identification that uses dental evidence to identify the victims. In mass disaster circumstances, contours and dental patterns are very useful to extract the shape in individual identification.

The experimental results are analyzed both the contour shape extraction and texture feature extraction of both radiographic and photographic images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model and it is well suited for forensic odontologists to identify a person in mass disaster situations. The findings provide theoretical and practical implications for individual identification of both radiographic and photographic images with a view to accurate identification of the person.