N-colour separation methods for accurate reproduction of spot colours

In packaging, spot colours are used to print key information like brand logos and elements for which the colour accuracy is critical. The present study investigates methods to aid the accurate reproduction of these spot colours with the n-colour printing process. Typical n-colour printing systems consist of supplementary inks in addition to the usual CMYK inks. Adding these inks to the traditional CMYK set increases the attainable colour gamut, but the added complexity creates several challenges in generating suitable colour separations for rendering colour images. In this project, the n-colour separation is achieved by the use of additional sectors for intermediate inks. Each sector contains four inks with the achromatic ink (black) common to all sectors. This allows the extension of the principles of the CMYK printing process to these additional sectors. The methods developed in this study can be generalised to any number of inks. The project explores various aspects of the n-colour printing process including the forward characterisation methods, gamut prediction of the n-colour process and the inverse characterisation to calculate the n-colour separation for target spot colours. The scope of the study covers different printing technologies including lithographic offset, flexographic, thermal sublimation and inkjet printing. A new method is proposed to characterise the printing devices. This method, the spot colour overprint (SCOP) model, was evaluated for the n-colour printing process with different printing technologies. In addition, a set of real-world spot colours were converted to n-colour separations and printed with the 7-colour printing process to evaluate against the original spot colours. The results show that the proposed methods can be effectively used to replace the spot coloured inks with the n-colour printing process. This can save significant material, time and costs in the packaging industry

Accurate Colour Reproduction of Human Face using 3D Printing Technology

The colour of the face is one of the most significant factors in appearance and perception of an individual. With the rapid development of colour 3D printing technology and 3D imaging acquisition techniques, it is possible to achieve skin colour reproduction with the application of colour management. However, due to the complicated skin structure with uneven and non-uniform surface, it is challenging to obtain accurate skin colour appearance and reproduce it faithfully using 3D colour printers. The aim of this study was to improve the colour reproduction accuracy of the human face using 3D printing technology. A workflow of 3D colour image reproduction was developed, including 3D colour image acquisition, 3D model manipulation, colour management, colour 3D printing, postprocessing and colour reproduction evaluation. Most importantly, the colour characterisation methods for the 3D imaging system and the colour 3D printer were comprehensively investigated for achieving higher accuracy

The Smart Phone as a Mouse

With the development of hardware, mobile phone has become a feature-rich handheld device. Built-in camera and Bluetooth technology are supported in most current mobile phones. A real-time image processing experiment was conducted with a SonyEricsson P910i smartphone and a desktop computer. This thesis describes the design and implementation of a system which uses a mobile phone as a PC mouse. The movement of the mobile phone can be detected by analyzing the images captured by the onboard camera and the mouse cursor in the PC can be controlled by the movement of the phone

Comparison of Electromagnetic Data With Irregular or Discontinuous Surfaces Using the Feature Selective Validation Method

In the field of Computational Electromagnetics, validation is a formal process to ensure the expected behaviour of the model. The Feature Selective Validation (FSV) method was originally developed to aid the validation of computational electromagnetics, and particularly electromagnetic compatibility (EMC). Since then, it has been adopted by the IEEE Standard for Validation of Computational Electromagnetics Computer Modelling and Simulations 1597.1 and used in a variety of other applications. The FSV method quantifies the difference between two sets of original data using a reliability function based on the decomposition of the data into a number of component parts that are then combined using a weighted scheme, giving a number of presentations of the comparison data. From the literature review, it is identified that the FSV method has been applied for various structured data like the S-parameter, radiation pattern, efficiency, and gain of an antenna. Since the original development of the FSV in the field of computational electromagnetics, more complex data like surface current, electric, and magnetic field outputs from Ultra High Frequency (UHF) devices are represented in 2- (or higher) dimensional image formats with irregular shapes, which are non-rectangular structures including features such as spaces or gaps within the device structure. However, performing FSV on such image data is challenging, particularly to avoid non-contributing spaces or voids in the image structure dominating the comparison results. This thesis discusses in detail a methodology developed to perform 2-Dimensional FSV on 2-Dimensional regular (rectangular and square) shaped images by segmenting the image into multiple blocks. In each block, 2-Dimensional FSV is performed separately, and then the outputs from the segmented blocks are concatenated to form the original 2-Dimensional image. Finally, the FSV outputs from the segmented approach are compared with the FSV outputs obtained from the original (full) structure, and the results are analysed using a non-parametric statistical test, which shows that the approach leads to results that support the proposed solution of comparison by segmentation and recombination. The proposed method is demonstrated on regular and irregular images to allow a detailed analysis

Traffic and road sign recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers' tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification.Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera's algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day.Approximately 97% successful segmentation rate was achieved using this algorithm. Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim's shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment's orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but v-SVM gives better results in some case

Defining Acceptable Colour Tolerances for Identity Branding in Natural Viewing Conditions

Graphic arts provide the channel for the reproduction of most brand communications. The reproduction tolerances in the graphic arts industry are based on standards that aim to produce visually acceptable outcomes. To communicate with their target audience brands, use a set of visual cues that may include the definition of a single or combinations of them to represent themselves. The outcomes are often defined entirely by their colour specification without an associating it to target parameters or suitable colour thresholds. This paper researches into the feasibility of defining colour tolerances for brand graphical representations. The National Health Service branding was used as a test case borne out of a need to resolve differences between contracted suppliers of brand graphics. Psychophysical evaluation of colour coded navigation used to facilitate wayfinding in hospitals under the varying illuminances across the estate was found to have a maximum acceptable colour difference threshold of 5ΔE00. The simulation of defined illumination levels in hospitals, between 25-3000 lux, resulted in an acceptable colour tolerance estimation for colour coded navigation of 3.6ΔE00. Using ICC media relative correction an experiment was designed to test the extent to which substrate white points could be corrected for colour differences between brand proofs and reproductions. Branded stationery and publications substrate corrections to achieve visual matches had acceptable colour difference thresholds of 9.5ΔE*ab for solid colours but only 2.5ΔE*ab. Substrate white point corrections on displays were found to be approximately 12ΔE*ab for solids and 5ΔE*ab for tints. Where display media were concerned the use of non-medical grade to view medical images and branded content was determined to be inefficient, unless suitable greyscale functions were employed. A STRESS test was carried out, for TC 1-93 Greyscale Calculation for Self-Luminous Devices, to compare DICOM GSDF with Whittle’s log brightness. Whittle’s function was found to outperform DICOM GSDF. The colour difference formulas used in this research were tested, using near neutral samples 2 judged by observers using estimated magnitude differences. The CIEDE2000 formula was found to outperform CIELAB despite unexpected outcomes when tested using displays. CIELAB was outperformed in ΔL* by CIEDE2000 for displays. Overall it was found that identity branding colour reproduction was mostly suited to graphic arts tolerances however, to address specific communications, approved tolerances reflecting viewing environments would be the most efficient approach. The findings in this research highlights the need for brand visualisation to consider the adoption of a strategy that includes graphic arts approaches. This is the first time that the subject of defining how brands achieve tolerances for their targeted visual communications has been researched

An investigation into Quadtree fractal image and video compression

Digital imaging is the representation of drawings, photographs and pictures in a format that can be displayed and manipulated using a conventional computer. Digital imaging has enjoyed increasing popularity over recent years, with the explosion of digital photography, the Internet and graphics-intensive applications and games. Digitised images, like other digital media, require a relatively large amount of storage space. These storage requirements can become problematic as demands for higher resolution images increases and the resolution capabilities of digital cameras improve. It is not uncommon for a personal computer user to have a collection of thousands of digital images, mainly photographs, whilst the Internet’s Web pages present a practically infinite source. These two factors 一 image size and abundance 一 inevitably lead to a storage problem. As with other large files, data compression can help reduce these storage requirements. Data compression aims to reduce the overall storage requirements for a file by minimising redundancy. The most popular image compression method, JPEG, can reduce the storage requirements for a photographic image by a factor of ten whilst maintaining the appearance of the original image 一 or can deliver much greater levels of compression with a slight loss of quality as a trade-off. Whilst JPEG's efficiency has made it the definitive image compression algorithm, there is always a demand for even greater levels of compression and as a result new image compression techniques are constantly being explored. One such technique utilises the unique properties of Fractals. Fractals are relatively small mathematical formulae that can be used to generate abstract and often colourful images with infinite levels of detail. This property is of interest in the area of image compression because a detailed, high-resolution image can be represented by a few thousand bytes of formulae and coefficients rather than the more typical multi-megabyte filesizes. The real challenge associated with Fractal image compression is to determine the correct set of formulae and coefficients to represent the image a user is trying to compress; it is trivial to produce an image from a given formula but it is much, much harder to produce a formula from a given image. เท theory, Fractal compression can outperform JPEG for a given image and quality level, if the appropiate formulae can be determined. Fractal image compression can also be applied to digital video sequences, which are typically represented by a long series of digital images 一 or 'frames'