Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Example-based Image Recoloring in Indoor Environment

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery

Automated Tracking of Hand Hygiene Stages

The European Centre for Disease Prevention and Control (ECDC) estimates that 2.5 millioncases of Hospital Acquired Infections (HAIs) occur each year in the European Union. Handhygiene is regarded as one of the most important preventive measures for HAIs. If it is implemented properly, hand hygiene can reduce the risk of cross-transmission of an infection in the healthcare environment. Good hand hygiene is not only important for healthcare settings. Therecent ongoing coronavirus pandemic has highlighted the importance of hand hygiene practices in our daily lives, with governments and health authorities around the world promoting goodhand hygiene practices. The WHO has published guidelines of hand hygiene stages to promotegood hand washing practices. A significant amount of existing research has focused on theproblem of tracking hands to enable hand gesture recognition. In this work, gesture trackingdevices and image processing are explored in the context of the hand washing environment.Hand washing videos of professional healthcare workers were carefully observed and analyzedin order to recognize hand features associated with hand hygiene stages that could be extractedautomatically. Selected hand features such as palm shape (flat or curved); palm orientation(palms facing or not); hand trajectory (linear or circular movement) were then extracted andtracked with the help of a 3D gesture tracking device - the Leap Motion Controller. These fea-tures were further coupled together to detect the execution of a required WHO - hand hygienestage,Rub hands palm to palm, with the help of the Leap sensor in real time. In certain conditions, the Leap Motion Controller enables a clear distinction to be made between the left andright hands. However, whenever the two hands came into contact with each other, sensor data from the Leap, such as palm position and palm orientation was lost for one of the two hands.Hand occlusion was found to be a major drawback with the application of the device to this usecase. Therefore, RGB digital cameras were selected for further processing and tracking of the hands. An image processing technique, using a skin detection algorithm, was applied to extractinstantaneous hand positions for further processing, to enable various hand hygiene poses to be detected. Contour and centroid detection algorithms were further applied to track the handtrajectory in hand hygiene video recordings. In addition, feature detection algorithms wereapplied to a hand hygiene pose to extract the useful hand features. The video recordings did not suffer from occlusion as is the case for the Leap sensor, but the segmentation of one handfrom another was identified as a major challenge with images because the contour detectionresulted in a continuous mass when the two hands were in contact. For future work, the datafrom gesture trackers, such as the Leap Motion Controller and cameras (with image processing)could be combined to make a robust hand hygiene gesture classification system

Music-STAR: a Style Translation system for Audio-based Rearrangement

Music style translation has recently gained attention among music processing studies. It aims to generate variations of existing music pieces by altering the style-variant characteristics of the original music piece, while content such as the melody remains unchanged. These alterations could involve timbre translation, reharmonization, or music rearrangement. In this thesis, we plan to address music rearrangement, focusing on instrumentation, by processing waveforms of two-instrument pieces. Previous studies have achieved promising results utilizing time-frequency and symbolic music representations. Music translation on raw audio has also been investigated using single-instrument pieces. Although processing raw audio is more challenging, it embodies more detailed information about the performance, timbre, and dynamics of a music piece. To this end, we introduce Music-STAR, the first audio-based model that can transform the instruments of a multi-track piece into another set of instruments, resulting in a rearranged piece

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Instance Segmentation With Contrastive Self-supervised Learning

Object detection or localization gradually progresses from coarse to fine digital image inference. It provides the classes of the image objects and the location of the image objects that have been categorized. The location is provided in the shape of bounding boxes or centroids. Semantic segmentation provides fine inference by indicating labels for every pixel in the input image. Each pixel is labelled according to the object class within which it is surrounded. Moreover, instance segmentation gives different sets of pixels of interest for separate instances of objects. Thus, instance segmentation is defined as solving the problem of object detection and semantic segmentation simultaneously. This thesis is introducing a new self-supervised framework for instance segmentation and background removal. We make use of a state-of-art self-supervised method called bootstrap your own latent for our pretraining section and then by transfer learning, we transfer the representation learnt in this pretraining to downstream task which is instance segmentation using Mask R-CNN. Two other existing state-of-art methods of instance segmentation, namely, instance segmentation scheme using Mask R-CNN along with random initial weights and that with ImageNet initial weights, respectively are implemented and compared to our proposed framework. Comparing our method with those known methods in instance segmentation and background removal, we find out that self-supervised learning outperforms both methods despite using no labelled data in pretraining. Our experimental results indicate that the proposed framework outperforms the instance segmentation and background removal using ImageNet initial weights and random initial weights by 0.866% and 14.06% ,respectively, in average precision (AP). Moreover, the proposed self-supervised framework has been designed to minimize the computations and the need for annotated datasets. This design demonstrates that the proposed system can perform high-quality processing at a meagre computation cost for many applications. Hence, instance segmentation using self-supervised techniques is a practical approach to lowering the barrier of computation resource requirements and labelled datasets to make them more implementable and applicable to the general public

COLOR MAPPING FOR CAMERA-BASED COLOR CALIBRATION AND COLOR TRANSFER

Ph.DDOCTOR OF PHILOSOPH

The riddle of the mirror

The context of design is changing at a rapid pace. The impact of information technologies and digital creative tools continuously improving have revolutionized design practice. There has been a transition within the scope of a designer’s role - from simply giving form to a material to designing digital services. These developments have distanced design from tactile materiality. The designer, whose practice began as a material-based and rooted in the arts and crafts, must now rethink and reposition their creative processes and role within the current context of design. In this master thesis, I investigated this problem space by trying to answer the following research question: how does hands-on interaction with material influence the expressive and creative aspects of design practice? My method for investigating this research question is to engage in a practice-led research approach in which I explore the artistic potential of silver glass colors in glassblowing. I chose to research this topic in the field of glassblowing because of the intense physical interaction required between the artist and the material. In glassmaking, silver glass colors are used to create specific aesthetics, ranging from iridescent to silver mirrored surfaces. However, silver glass colors have been very challenging for many practitioners due to their unpredictable nature. I have considered this unpredictable material nature as a research opportunity to explore a reliable method of achieving silver mirrored results and to have artistic control of the wide metallic and iridescent palette. I have then applied these insights from material research to my artistic process and, with the help of relevant theory, I have reflected on both of these processes to investigate their intersection as a whole. My aim has been to determine the influence of hands-on material exploration on my design practice. The main findings are summarized in three points: 1) Hands-on interaction with the material has primarily influenced my early artistic vision and enhanced its expressive and creative aspects throughout my artistic production.2) Cyclical hands-on dialog with a material can support personal growth and help to develop an individual voice and creative expression; thus offering great potential for educational purposes. 3) The empirical data shows that silver glass colors provide a wide range of visual palette and can be preferred for local applications in particular, yet they require a long learning process to have control of them. This work provides information for readers who wish to know more about the role of hands-on studio practice and developing an individual voice through a creative process. Moreover, it presents helpful insights and data for people who are particularly interested in conducting material-based research in glassblowing and utilizing silver glass colors as an artistic technique