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Macroscopic anatomy has traditionally been taught using cadaveric material, lectures and a variable amount of additional resources such as online modules. Anatomical models have also been used to assist in teaching. Of these, traditional plastic models have been shown to be effective educational tools, yet have significant drawbacks such as a lack of anatomical detail and texturisation. Three-dimensional (3D) printed models stand to solve these problems and widen access to high-quality anatomical teaching. This paper outlines the use of 3D multi-planar imaging (CT and MRI) as a framework to develop an accurate model of the retroperitoneum. CT and MRI scans were used to construct a virtual 3D model of the retroperitoneum. This was printed locally as a full-size colour model for use in medical education. We give a complete account of the processes and software used. This study is amongst the first of a series in which we will document the newly formed Oxford Library of Anatomy. This series will provide the methodology for the production of models from CT and MRI scans, and the Oxford Library of Anatomy will provide a complete series of some of the most complex anatomical areas and ones which degrade quickly when a real cadaver is being used. In our own internal experience, the models are highly accurate, reproducible and durable, as compared to prosected specimens. We hope they will form an important adjunct in the teaching of the subject.

A challenge emerging from the literature on measuring and modelling segregation at multiple scales is how to map the results. This Featured Graphic uses a method based on the CMY colour model, allowing each of the C, M and Y values to represent one of the micro-, meso- and macro-levels of analysis separately, then combining them in an overall map. It presents the method using a new index, a multilevel index of diversity, with a case study looking at the ethnic diversity of state-school pupils living in London’s neighbourhoods.

In human-machine communication situations, perceptual and conceptual deviations can appear. The challenge of categorising colours is tackled in this paper. Colour perception is very subjective. Colours may be perceived differently depending on a person’s eye anatomy and a person’s sense of sight which adapts to the surroundings and perceives different brightness of hues depending on the context. Distinguishing more/less quantity of hues depends also on the level of expertise but also on the cultural and social environment. Colours naming involves conceptual alignment with human cognition, meaning and human understanding for referring to an object and even for discriminating among objects. Studies in cross-cultural linguistics say that humans determined prototypical colours as the centre of colour categories. Hence, a cognitive colour model should distinguish/indicate when a colour coordinate is close/far to the centre of its category. And these centres of categories should be adaptable and customisable depending on the society. A fuzzy colour model based on HSL colour space and radial basis functions is presented in this paper. Logics have been defined to combine this fuzzy-colour model with a Probabilistic Reference And GRounding mechanism (PRAGR) in order to obtain the most discriminative colour descriptor for an object depending on the context. Two case studies related with human cognition are presented. Then further tests are carried out on a dataset where the first and second most discriminative colour is computed for each object in each scene. Finally, a survey is conducted to find out the cognitive adequacy of the obtained discriminative colour names.

Detection and prognosis of Dental fluorosis is foremost for the doctors, the researchers as well as for the sufferers. Dental fluorosis is the presence of vague white streaks or patches on the tooth area that only happen when there is utilisation of fluoride from any source, over extended periods when teeth are getting mature. The existing research works and techniques mainly depends on the visual ability of the dentist to detect or to separate the areas of fluorosis. So to overcome the issue, this paper proposed a solution for getting the severity level of dental fluorosis without human intervention using enhanced K-Means unsupervised clustering algorithm and fuzzy C-means on HSV (hue, saturation and value) colour model. Using the clustering algorithm helped us out to successfully distinguish the fluorosis affected area. The area percentage was calculated and accordingly images were categorized with different level of severity level. The level was compared with that given by expert on each image. The results obtained are far better than the conventional approach [1].

Continuous automation of conventional industrial operations with smart technology have drawn significant attention. Firstly, the study investigates on optimizing the proportion of industrial biscuit processing waste powder, (B) substituted into BG-11 as a source of cultivation medium for the growth of C. vulgaris. Various percentages of industrial biscuit processing waste powder, (B) were substituted in the inorganic medium to analyse the algal growth and biochemical composition. The use of 40B combination was found to yield highest biomass concentration (4.11 g/L), lipid (260.44 mg/g), protein (263.93 mg/g), and carbohydrate (418.99 mg/g) content compared with all the other culture ratio combination. Secondly, the exploitation of colour acquisition was performed onto C. vulgaris growth phases, and a novel photo-to-biomass concentration estimation was conducted via image processing for three different colour model pixels. Based on linear regression analysis the red, green, blue (RGB) colour model can interpret its colour variance precisely.

A novel proof of concept was developed targeted at the detection of Materials Other than Grain (MOGs) in soybean harvesting. Front lit and back lit images were acquired, and image processing algorithms were applied to detect various forms of MOG, also known as dockage fractions, such as split beans, contaminated beans, defect beans, and stem/pods. The HSI (hue, saturation and intensity) colour model was used to segment the image background and subsequently, dockage fractions were detected using median blurring, morphological operators, watershed transformation, and component labelling based on projected area and circularity. The algorithms successfully identified the dockage fractions with an accuracy of 96% for split beans, 75% for contaminated beans, and 98% for both defect beans and stem/pods.

Introduction Typhae pollen (TP) has been used as an anticoagulant in traditional Chinese medicine and throughout Asia. Typhae Pollen Carbonisata (TPC), a processed product of TP, has hemostatic properties. TPC is produced by frying TP, and the degree of processing can be judged by the colour change from yellow to brown. Objective To establish a novel method for quality assessment of TPC and discriminate TPC from underdone products and overdone products. Methodology The Commission Internationale de l'Eclairage (CIE) L* a* b* colour space values of TP and TPC were measured to establish the colour model of TPC. Ultra-performance liquid chromatography was developed for fingerprinting. Thrombin activity promoting rates, clotting time, and bleeding time illustrated the difference in the hemostatic effect of the processed products. Chemometric approaches were performed to reveal the correlation between components and colour values or thrombin activity. Results Reference ranges of colour values and mathematical functions of TPC were established. The developed method was found to be fast, economic, sensitive, and stable. Fingerprints and thrombin activity in conjunction with partial least squares (PLS) demonstrated that peaks 2, 4, 7, 30, and 36 (isorhamnetin) were the main contributors for colour values and hemostatic activity of TPC. Conclusions TPC and its unqualified products can be effectively distinguished based on chromaticity analysis, which provides a powerful tool for the comprehensive evaluation of the quality of Chinese herbal medicines.

A self-contained smartphone-based colorimetric sensing platform adaptable for multiple analytes is demonstrated. Concurrent variation of multiple optical sensing parameters is addressed using multiple nonlinear regression analysis.

Classifying and evaluating the structure of tropical forest plant biomes based on up-to-date remote sensing data, remains a great challenge due to the species diversity, changes in the stages of seasonal growth. Remote sensing has long been a useful tool for classification and assessment of vegetation. For data from unmanned aerial vehicles (UAVs), the ability to receive high-resolution data also partly solves this problem. The development of vegetation based on natural conditions includes: topographic, geomorphology, geologic conditions, climate, human impacts. Based on the red-green-blue colour model (RGB) obtained from UAV, analysis of VARI (visible atmospherically resistant Index) and TGI (triangulation greenness index), combined with the development characteristics of each group and type of biomes on different topography and geologic types, the researchers were able to give some characteristics of some tropical forest vegetation in Ngoc Linh Nature Reserve, Kon Tum province, Vietnam.

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

Face recognition systems have been obtaining substantial importance in modern world. Security systems are major application of face recognition system. However, the potential of the face recognition system to withstand the attack of an unauthorized person is an important concern. Face recognition systems are vulnerable to photographs and video spoof attacks. In these scenarios, anti-spoofing systems comes in handy to evade these attacks. Robust solutions are required for face recognition system to be immune against spoofing attacks. In this paper, the detected face is denoised and then converted to YCbCr and CIELUV colour model and then passed through VGG-Face architecture for extraction of face embeddings of each colour space. Then the extracted face embeddings are concatenated and then passed through SVC (Support Vector Classifier) which then classifies real and spoof faces. The proposed method has obtained a test accuracy of 99.6% with specificity of 99.5% for spoof detection.

The studies of meat and fat colour and marbling in Longissimus dorsi of different cattle — beef-producing (Aberdeen-Angus,Herefordbreeds) and dual-purpose (Simmental, Black-and-white breeds) — allowed to define groups by the colour values according to Lab international colour model. Measurements were performed 24 hours post-mortem between 12th and 13th ribs. It was found that different ranges of meat colour differed primarily in L* (lightness) and a* (redness) values, while b* (yellowness) values did not significantly differ. The highest differentiation between ranges of fat colour was noted in b* values, whereas L* and a* slightly differed. Moreover, visual assessment of beef marbling by four grades (small, moderate, good, and rich) and instrumental (microstructural) analysis using a computer image analysis system were carried out. The morphometric study of marbling was conducted in accordance with the principles of system quantitative analysis. To perform quantitative measurements, object analysis parameters (area) were specified. Both automatic and manual measurements of specified parameters were used. The study of Longissimus dorsi marbling established high agreement between visual and instrumental evaluations of marbling.

Object tracking as a branch of computer vision plays a key role in the intelligent video surveillance. In recent years, the mean shift algorithm due to its simplicity and robustness has attracted great attention for tracking the object by using a colour model, while only using the colour causes the error in some cases of tracking such as illumination variations and so on. Consequently, the authors proposed an enhanced mean shift tracking algorithm. First, they presented a new texture-based target representation by a modified version of the interlaced derivative pattern, which considers spatial dependencies between pixels. Second, an improvement for the mean shift tracking algorithm based on this representation is suggested. In addition, a parameter to resize the window around the object is considered, adaptively. Experimental results on some benchmark video in comparison with other state-of-art methods show the efficiency and utility of the proposed algorithm in many complex conditions.

The Woodburytype is a 19th century photomechanical technique capable of producing high-quality continuous-tone prints. It uses pigment dispersed in gelatin to produce a 2.5D print, in which the effect of varying tone is produced by a variation in the print height. We propose a method of constructing full colour prints in this manner, using a CMY colour model. This involves the layering of multiple translucent pigmented gelatin films and tracking how the perceived colour of these stacks changes with varying height. A set of CMY inks is constructed, taking into account the optical properties of both the pigment and gelatin, and a method of translating images into these prints is detailed.

Multivariate analyses, in particular correspondence analysis (CA), have become a standard exploratory tool for analysing and interpreting variance in archaeological assemblages. While they have greatly helped analysts, they unfortunately remain abstract to the viewer, all the more so if the viewer has little or no experience with multivariate statistics. A second issue with these analyses can arise from the detachment of archaeological material from its geo-referenced location and typically considered only in terms of arbitrary classifications (e.g. North Europe, Central Europe, South Europe) instead of the full range of local conditions (e.g. proximity to other assemblages, relationships with other spatial phenomena). This article addresses these issues by presenting a novel method for spatially visualising CA so that these analyses can be interpreted intuitively. The method works by transforming the resultant bi-plots of the CA into colour maps using the HSV colour model, in which the similarity and difference between assemblages directly corresponds to the similarity and difference of the colours used to display them. Utilising two datasets – ceramics from the excavations of the Roman fortress of Vetera I, and terra sigillata forms collected as part of 'The Samian Project' – the article demonstrates how the method is applied and how it can be used to draw out spatial and temporal trends.

High-resolution satellite images usually contain a significant amount of shadow cast by elevated objects. Image information is affected by such shadows. Shadows obscure image information partially and/or totally, which leads to interpretation errors. This article aims to develop a complete shadow detection approach using all available bands of a multispectral image. Therefore, the near-infrared band is employed in addition to visible bands while generating a C1C2C3 colour model to achieve a modified C3 index. A comparative study is then applied to the original C1C2C3, hue, saturation, and value colour model and the modified model, to validate the developed index. The results show the superiority of the proposed approach in shadow detection, with overall accuracy around 94%.