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Along with the ever growingWeb is the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering undesirable web content. In this paper, we investigate this problem through WebGuard, our automatic machine learning based pornographic website classification and filtering system. Facing the Internet more and more visual and multimedia as exemplified by pornographic websites, we focus here our attention on the use of skin color related visual content based analysis along with textual and structural content based analysis for improving pornographic website filtering. While the most commercial filtering products on the marketplace are mainly based on textual content‐based analysis such as indicative keywords detection or manually collected black list checking, the originality of our work resides on the addition of structural and visual content‐based analysis to the classical textual content‐based analysis along with several major‐data mining techniques for learning and classifying. Experimented on a testbed of 400 websites including 200 adult sites and 200 non pornographic ones, WebGuard, our Web filtering engine scored a 96.1% classification accuracy rate when only textual and structural content based analysis are used, and 97.4% classification accuracy rate when skin color related visual content based analysis is driven in addition. Further experiments on a black list of 12 311 adult websites manually collected and classified by the French Ministry of Education showed that WebGuard scored 87.82% classification accuracy rate when using only textual and structural content‐based analysis, and 95.62% classification accuracy rate when the visual content‐based analysis is driven in addition. The basic framework of WebGuard can apply to other categorization problems of websites which combine, as most of them do today, textual and visual content.

The purpose of this study is to achieve multi-variety and small-batch assembly through direct cooperation between equipment and people and to improve assembly efficiency as well as flexibility.

Firstly, the concept of the human–computer interaction is designed. Secondly, the machine vision technology is studied theoretically. Skin color filter based on hue, saturation and value color model is put forward to screen out images that meet the skin color characteristics of the worker, and a multi-Gaussian weighted model is built to separate moving objects from its background. Both of them are combined to obtain the final images of the target objects. Then, the key technology is applied to the smart assembly workbench. Finally, experiments are conducted to evaluate the role of the human–computer interaction features in improving productivity for the smart assembly workbench.

The result shows that multi-variety and small-batch considerable increases assembly time and the developed human–computer interaction features, including prompting and introduction, effectively decrease assembly time.

This study proves that the machine vision technology studied in this paper can effectively eliminate the interferences of the environment to obtain the target image. By adopting the human–computer interaction features, including prompting and introduction, the efficiency of manual operation is improved greatly, especially for multi-variety and small-batch assembly.

Virtual medical instrumentation plays a vital role in a telemedicine system that obtains data from the medical instrument, required by doctors at remote location to diagnose a patient. In recent years, the analysis of skin quality by telemedicine system has become an emerging trend. To allow the skin to complement the beauty products and achieve better improvement results, the purpose of this study is to provide advice on a system that can objectively evaluate the condition of the skin of the face and to match appropriate beauty and cosmetic products.

A novel customer-oriented medical system is proposed for the applications of telemedicine in this study, whose aim is to improve information transfer quality and rate to further enhance the communication between medical staffs and patients in the telemedicine. More specifically, facial skin will be recorded with digital images, and skin detection will be performed using image processing technology to facilitate doctors to provide medical treatment for the patients at far end.

The roughness, freckles and acne indicators were evaluated after obtaining skin images. These three indicators were used as input to the system, and skin scores were then calculated to evaluate skin conditions to further provide more matching skin care.

This can improve the health problems that have occurred and can also record the skin condition for each test. Experimental results suggest that it is suitable for the applications of telemedicine.

This paper holds a big advantage to enable to recognize faces, regardless of time and place. Also this provides an independent performance of smart phone, because of its process by a computer of third party not by that of the mobile device. In addition, it is desirable to minimize the expensive operations in mobile device with constraint computational power (i.e. battery consumption). Thus, the authors exclude the process of transmission failed from the input device. The paper aims to discuss these issues.

In this paper, the authors have proposed a new face detection and verification algorithm, based on skin color detection to enable extracting the face region from color images of the mobile phone. And then extracted the facial feature as eigenface, verified whether or not the identity of users is right, applied support vector machine to the region of detected face.

The experimental results for two datasets show that the proposed method achieves slightly higher efficiencies at the detection and verification of user identity, compared with other method, where varying lighting conditions with complex backgrounds, according to be fast and accurate than any other previous methods.

The proposed algorithm enables to implement fast and accurate search using triangle-square transformation for detection of human faces in a digital still color images, obtained by the mobile device camera under unconstraint environments, using advanced skin color model and characteristic points in a detected face.

The study aims to examine female sexuality in marketing communications and how it shapes the millennial buying decisions in the fashion industry. The focus of the study is to connect fashion industry and marketing communication to understand how female sexuality influence buying behaviours and decisions of the millennial.

The study was underpinned by the dimensions of skin colour, brand image and market share in sexual appealing marketing communication, and the millennial in the study comprises youths between the age of 21and 40 years and demographically defined by Wells and Guber (1966) as bachelors, Full nests 1 and 2. Multistage stage sampling was used with a structured questionnaire.

Findings show that youths, 2019 buying decisions and behaviours are strategically influenced by different manifestations of female sexuality in the context of the study and equally affect market share and patronage.

The study shows what shapes the marketing communication strategies of the rising fashion industry but is limited to the millennial buying decisions and not the larger fashion industry consumers.

The needs for fashion industry to understand the influence of increasing use of female sexuality in marketing communication on male and female consumers and the effects on their respective buying behaviours is strategic to the industry as shown in the study.

Female sexuality in marketing communication is strategic to fashion industry in today's market among youths.

The millennial constitutes a larger percentage of the developing economy market with rising income thus the need to understand their buying behaviours in the fashion industry

Significant research has been carried out in terms of development of new bidirectional reflectance distribution function (BRDF) instruments; however, there is still little research available regarding spectral BRDF measurements of human skin. This study aims to investigate the variation in human skin reflectance using a new fibre optic-based spectral-BRDF measurement device.

Design of this system mainly involves use of multiple fibre optics to illuminate and detect light reflected from a sample, whereas a hemispherical dome was 3D printed to mount the fibres at various slant/tilt angles. To investigate the spectral differences in BRDF of human skin, 3 narrowband filters in the visible spectrum were used, whereas measurements were taken from the back of the hand for Caucasian and Asian skin types.

The experiments demonstrate that the BRDF of human skin varies with wavelengths in the visible spectrum and it is also different for Caucasian and Asian skin types. Both skin types exhibit off-specular reflection with increase in angle of incidence and show less variation with respect to viewing angles when the angle of incidence is normal to the surface.

A database of spectral BRDF measurements of human skin will help not only in creating realistic skin renderings but also in development of novel skin reflectance models for biomedical and machine vision applications. The measurements would also provide means to validate the predictions from existing light transport/spectral simulation models for human skin and will ultimately help in the accurate diagnosis and simulation of various skin disorders.

The proposed system provides fast scatter measurements by utilising multiple fibres to detect light simultaneously at different angles while also allowing easy switching between incident light directions. Due to its flexible design and contact-based measurements, the device is independent of errors due to sample movements and does not require any image registration. Also, measurements taken from the device show that the BRDF of skin varies significantly in the visible spectrum and it is different for Caucasian and Asian skin types.

Tongue diagnosis is a standard expert technique of traditional Chinese medicine (TCM). Computerized tongue diagnosis promises to automate the process of tongue diagnosis yet the tongue images segmentation upon which it depends is made difficult by the fact that the tongue is non‐rigid and varies greatly in size, shape, color, and texture. This paper presents a novel medical sensor system for TCM tongue diagnosis, which makes use of hyperspectral imaging technology.

The tongue image capturing sensor device for Chinese medical is based on the theory of the pushbroom hyperspectral imager. The paper illustrates its advantages by detecting the tongue contour in the hyperspectral images.

The experiments from 1,522 clinical tongue images show the validity of the system.

In this paper, the authors propose to use hyperspectral technology for tongue diagnosis for the first time in the literature and obtain promising results.

The novel sensor for tongue image capture gives a new method for tongue imformation collection. This system gives a new approach for tongue information collection.

From a distinctiveness theory and elaboration likelihood model (ELM) perspective, this study aims to investigate the determinants, the direct and indirect effects of ethnic identification on brand attachment, brand love and behavioral outcomes. The authors examine two types of products (high- vs low-involvement).

In a survey, participants were randomly assigned to four conditions consisting of two high-involvement (i.e. a smart watch and a car) and two low-involvement (i.e. a soda/soft drink and a shampoo) stimuli. A total of 192 respondents participated in this study; 39% were male, 61% were female and 90% were 18-22 years old.

In line with ELM, the authors offer evidence that ethnic advertising is more effective for low-involvement products targeted at African American consumers. Results also showed that brand attachment partially mediates the effect of ethnic identification with the ad (EthIdenAD) and brand love. In summary, the structural equation modeling (SEM) results support the efficacy of ethnic-targeted ads for developing brand attachment, brand love and behavioral outcomes.

Using ethnically targeted ads to target minority groups may not be sufficient to increase the efficacy of ads. According to distinctiveness theory, African Americans with different levels of ethnic identification will respond differently to ad attitudes, brand attachment, brand love, identification with the model in the ad and varying degrees of belief that the ad was targeted toward them.

This study examines the effect of ethnically targeted advertising on brand love, and behavioral outcomes with the mediation effect of brand attachment and moderation effect of product involvement. The results of this study suggest several theoretical and practical implications for marketing and brand management, including directions for advertising strategy and consumer–brand relationships.

The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller.

Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject's face.

The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects.

Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.

The purpose of this paper is to investigate an idea of producing an assistive and augmentative communication (AAC) tool that uses natural human computer interfacing to accommodate the disabilities of children with cerebral palsy (CP) and assist them in their daily communication.

The authors developed a prototype that recognizes the real time detected emotions display on the face and send alerts to the caretakers through Short Messaging System (SMS) or loud speaker.

The evaluation result shows that the proposed prototype recognizes real time facial expressions from the children with CP with an average of 79.4 per cent, and a maximum of 88.3 per cent (standard deviation of 7.4 per cent) on ten children with CP. Evaluations were also conducted to investigate the effectiveness of the prototype to deliver critical expression messages to their caretakers. The result showed that 98.5 per cent of SMS was sent successfully to the caretakers (pre‐defined mobile phone number) with an average waiting time of 8.3 seconds.

The paper demonstrates the potential of the proposed prototype to assist children with CP to communicate with their caretakers in real time.