<div> <div> <div> <p>In this thesis, two aspects of video coding improvement are discussed, namely error resilience and coding efficiency. </p> <p>With the increasing amount of videos being created and consumed, better video compression tools are needed to provide reliable and fast transmission. Many popular video coding standards such as VPx, H.26x achieve video compression by using spa- tial and temporal dependencies in the source video signal. This makes the encoded bitstream vulnerable to errors during transmission. In this thesis, we investigate an error resilient video coding for the VP9 bitstreams using error resilience packets. An error resilient packet consists of encoded keyframe contents and the prediction sig- nals for each non-keyframe. Experimental results exhibit that our proposed method is effective under typical packet loss conditions. </p> <p>In the second part of the thesis, we first present an automatic stillness feature detection method for group of pictures. The encoder adaptively chooses the coding structure for each group of pictures based on its stillness feature to optimize the coding efficiency. </p> <p>Secondly, a content-based video coding method is proposed. Modern video codecs including the newly developed AOM/AV1 utilize hybrid coding techniques to remove spatial and temporal redundancy. However, the efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result. One interesting approach is to only encode visually relevant information and use a different coding method for “perceptually insignificant” regions </p> </div> </div> <div> <div> <p>xiv </p> </div> </div> </div> <div> <div> <div> <p>in the frame. In this thesis, we introduce a texture analyzer before encoding the input sequences to identify detail irrelevant texture regions in the frame using convolutional neural networks. The texture region is then reconstructed based on one set of motion parameters. We show that for many standard test sets, the proposed method achieved significant data rate reductions. </p> </div> </div> </div>

<div>Touch event detection investigates the interaction between two people from video recordings. We are interested in a particular type of interaction which occurs between a caregiver and an infant, as touch is a key social and emotional signal used by caregivers when interacting with their children. We propose an automatic touch event detection and recognition method to determine the potential timing when the caregiver touches the infant, and classify the event into six touch types based on which body part of the infant has been touched. We leverage deep learning based human pose estimation and person segmentation to analyze the spatial relationship between the caregivers’ hands and the infant. We demonstrate promising performance on touch event detection and classification, showing great potential for reducing human effort when generating groundtruth annotation.</div><div><br></div><div>Recently, artificial intelligence powered techniques have shown great potential to increase the efficiency of video compression. In this thesis, we describe a texture analysis pre-processing method that leverages deep learning based scene understanding to extract semantic areas for the improvement of subsequent video coder. Our proposed method generates a pixel-level texture mask by combining the semantic segmentation with simple post-processing strategy. Our approach is integrated into a switchable texture-based video coding method. We demonstrate that for many standard and user generated test sequences, the proposed method achieves significant data rate reduction without noticeable visual artifacts.</div>

It has been hypothesized that dietary differences in bone consumption exist between the extinct Pleistocene dire wolf (Canis dirus) and the modern gray wolf (C. lupus). Here dental microwear texture analysis of the m2 is used to test the hypothesized dietary behavior of C. dirus. If the m2 does distinguish dietary tendencies and dire wolves were heavy bone consumers, then the microwear signals of C. dirus should be similar to extant duraphageous canids. Microwear texture analysis of C. dirus was compared with that of C. lupus, coyote (C. latrans), and African wild dog (Lycaon pictus) to assess the degrees of bone consumption. An overall lack in statistically significant variables suggests little difference between the dietary tendencies between C. dirus and C. lupus. The dire wolf did not closely align with the duraphageous L. pictus, which calls into question the hypothesis of heavy carcass utilization during the Pleistocene.

Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

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Yes / This comprehensive review paper examines bone fracture detection techniques based on time-domain low-frequency and microwave radiofrequency (RF). Early and accurate diagnosis of bone fractures remains critical in healthcare, as it can significantly improve patient outcomes. This review focuses on the potential of low-frequency and microwave RF methods, particularly their combination and application of time-domain analysis for enhanced fracture detection. We begin by providing an overview of the fundamental concepts of RF techniques and then by examining biological tissues' dielectric properties. We then compare the advantages and limitations of various bone fracture detection techniques, such as low-frequency RF methods, microwave RF methods, ultrasonography, X-ray, and CT scans. The discussion then shifts to hybrid approaches that combine low-frequency and microwave techniques, emphasising the advantages of such combinations in fracture detection. Machine learning techniques, their applications in bone fracture detection, and the role of time-domain analysis in hybrid approaches are also investigated. Finally, we examine the accuracy and reliability of simulated models for bone fracture detection. We discuss recent advancements and future directions, such as novel sensor technologies, improved signal processing techniques, integration with medical imaging modalities, and personalised fracture detection approaches. This review aims to comprehensively understand the landscape and future potential of time-domain analysis in low-frequency and microwave RF techniques for bone fracture detection. / EU Horizon Europe H2020-MSCA-RISE-2022-2027 (ID: 101086492) and H2020-MSCA-RISE-2019-2024 (ID 872878), Marie Skłodowska-Curie, Research and Innovation Staff Exchange (RISE), and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/ X039366/1.

As technology advances in all areas of society and industry, the technology used to produce one of life's essentials - food - is also improving. The majority of agriculture production in developed countries has gone from family farms to industrial operations. With the advent of large-scale farming, the automation of basic farming operations has increasingly made practical and economic sense. Broccoli, which is still harvested by hand, is one of the most expensive crops to produce. Investing in sensing technology that can provide detailed information about the location, maturity and viability of broccoli heads has the potential to produce great commercial benefits. This technology is also a prerequisite for developing an autonomous harvester that could select and harvest mature heads of broccoli. This thesis details the work done to develop a computer vision algorithm that has the ability to locate the broccoli head within an image of an entire broccoli plant and to distinguish between mature and immature broccoli heads. Locating the head involves the use of a Hough transform to find the leaf stems and, once the stems are found, the location and extent of the broccoli head can be ascertained with the use of contrast texture analysis at the intersection of the stems. A co-occurrence matrix is then produced of the head and statistical texture analysis is performed to determine the maturity of the broccoli head. The conceptual design of a selective autonomous broccoli harvester, as well as suggestions for further research, is also presented. / Master of Science

A biodiversidade das espécies existentes no riquíssimo reino vegetal, tornam os modelos tradicionais de taxonomia uma tarefa muito complexa e morosa, na qual o processo de classificação é tradicionalmente realizado manualmente. As dificuldades presentes nesse processo implicam na existência de poucas pesquisas de classificação vegetal utilizando métodos matemáticos e computacionais. Desta forma, visando contribuir com as técnicas de taxonomia já desenvolvidas, este estudo objetiva desenvolver e testar uma metodologia computacional de identificação de espécies vegetais por meio da análise da textura foliar. Motivado pelo projeto TreeVis, este trabalho realiza uma revisão dos métodos utilizados para análise de textura em imagens digitais (foco concentrado em extração de características e classificação), investigando a aplicabilidade de métodos tradicionais como matrizes de coocorrência, técnicas estado da arte como Gabor wavelets e também de novos e promissoras técnicas de análise de textura, como a dimensão fractal volumétrica. No contexto de classificação investiga-se métodos para reconhecimento de padrões lineares com base em análise de dados multivariados, não lineares com base na teoria das Redes Neurais Artificiais e métodos simples para combinação de diferentes classificadores (comitê de máquinas). Apesar da alta similaridade entre classes e similaridade intraclasses não adequada, os resultados alcançados mostraram-se excelentes. A melhor estratégia de classificação, utilizando comitê de máquinas com descritores de Gabor wavelets/cor e dimensão fractal volumétrica/cor, obteve uma probabilidade de acerto global de 96:32% nas 40 classes estudadas. Esse resultado demonstra como os métodos computacionais de análise de imagens, em especial análise de textura, podem contribuir facilitando e agilizando a tarefa de identificação de espécies vegetais / Biodiversity of species existing in the plant kingdom make the use of traditional models of taxonomy, a process of classification traditionally performed manually, a very complex and time-consuming task. Most of difficulties in that process result from the existence of few researches on plant classification using mathematical and computational methods. In this way, to contribute with the taxonomy techniques already developed, this study aims to develop and test a computational method for identifying plant species by leaf texture analysis. Motivated by the TreeVis project, this work is a comprehensive revision of texture analysis methods used in digital images (focus concentrated in features extraction and classification). This study investigates the applicability of traditional methods such as co-occurrence matrix, state of the art techniques as Gabor wavelets, and new and promising texture analysis methods, such as volumetric fractal dimension. In classification context is investigated methods of pattern recognition based on multivariate data analysis, artificial neural networks and committee machines. Although leaf classes present high similarity between classes and not appropriate similarity intraclasses, the results obtained are excellent. The best strategy for classification, using committee machines with descriptors of Gabor wavelets/color and volumetric fractal dimension/color, yielded a high probability of success, 96:32% in 40 classes studied. This result demonstrates how computational methods of images analysis, in particular texture analysis, can contribute and make more easier and faster the task of identifying plant species

Submitted by PPG Odontologia (odontologia-pg@pucrs.br) on 2018-10-11T20:02:40Z No. of bitstreams: 1 MAUR?CIO_DO_NASCIMENTO_GERHARDT_DIS.pdf: 5795636 bytes, checksum: ed03225082187f892f9f67a93253df94 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-10-17T13:37:17Z (GMT) No. of bitstreams: 1 MAUR?CIO_DO_NASCIMENTO_GERHARDT_DIS.pdf: 5795636 bytes, checksum: ed03225082187f892f9f67a93253df94 (MD5) / Made available in DSpace on 2018-10-17T14:10:50Z (GMT). No. of bitstreams: 1 MAUR?CIO_DO_NASCIMENTO_GERHARDT_DIS.pdf: 5795636 bytes, checksum: ed03225082187f892f9f67a93253df94 (MD5) Previous issue date: 2018-07-31 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / The literature has shown that the bone tissue may undergo remodeling under mechanical stimuli. Therefore, it is necessary to assess if the prosthetic loading of dental implants can change the peri-implant bone characteristics. The aim of this study was to evaluate the bone changes in distal implants of implant supported fixed complete dentures (PTFIs) by means of gray levels and texture analysis in periapical radiographs. The sample consisted of 63 distal implants of PTFIs. The radiographs were taken after the prostheses installation (T0), 1 year (T1) and 3 years (T3) in function. The images of each implant at different times were superimposed using the software GIMP? and then exported to the software ImageJ?, where the Regions of Interest (ROIs) were selected for the analyses of the following gray levels variables: mean gray levels, standard deviation and coefficient of variation; and the texture variables: correlation, contrast, entropy and angular second moment. Data were analyzed by suing mixed regression models at the significance level of 0.05. Statistically significant effects were found for time in one year (P < 0.05) and in 3 years (P < 0.01) and for maximum bite force (P < 0.01) on mean gray levels increase. The interaction between time in one year and bruxism was significant (P < 0.01) for reduction of the coefficient of variation. No significant effect was found for texture variables (P > 0.05). The results suggest an increase of bone density close to distal implants of PTFIs in a period of time up to 3 years as measured by the increase in mean gray levels and reduction of coefficient of variation in a period of 1 year. / A literatura tem mostrado que o tecido ?sseo possui a capacidade de se modificar sob a a??o de est?mulos mec?nicos. Portanto, h? necessidade de avaliar se o carregamento prot?tico por implantes osseointegradospode alterar as caracter?sticas ?sseas. O objetivo deste estudo foi avaliar a altera??o ?ssea em implantes distais de pr?teses totais fixas implantossuportadas (PTFIs) por meio de an?lise de n?veis de cinza e de par?metros de textura em radiografias periapicais. A amostra foi constitu?da por 63 implantes distais de PTFIs. As radiografias foram obtidas ap?s a instala??o das pr?teses (T0), 1 ano (T1) e 3 anos (T3) das mesmas em fun??o. As imagens de um mesmo implante em diferentes tempos foram sobrepostas utilizando o softwareonde foram delimitadas as Regi?es de Interesse (ROIs) e analisadas as vari?veis de n?veis de cinza: m?dia de n?veis de cinza, desvio padr?o e coeficiente de varia??o; e os par?metros de textura: correla??o, contraste, entropia e segundo momento angular. Os dados foram analisados atrav?s de modelos de regress?o mistos ao n?vel de signific?ncia de 0,05. Houve efeito estatisticamente significativo do tempo de 1 ano (P< 0,05) e 3 anos (P < 0,01) e da for?a m?xima de mordida (P < 0,01) para aumento da m?dia dos n?veis de cinza. A intera??o entre tempo de 1 ano e bruxismo foi significativa (P < 0,01) para a redu??o do coeficiente de varia??o. Os par?metros de textura n?o tiveram efeito significativo (P > 0,05). Os resultados sugerem um adensamento do osso perimplantar de implantes distais de PTFIs em um per?odo de at? 3 anos atrav?s do aumento da m?dia dos n?veis de cinza e redu??o do coeficiente de varia??o no per?odo de 1 ano.