The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for this landscape. However, the pixel-based approaches also resulted in accurate results. Furthermore, the study shows that in the period 1990–2010, 42% of the forest cover disappeared and was converted into bare soil and savannahs. Next to the change in forest, stable forest regions were fragmented. This has negative effects on the amount of suitable habitats for Malagasy fauna. Finally, the scaling structure in landscape patches was investigated. The study shows that the patch size distribution has long-tail properties and that these properties do not change in periods of deforestation.

I den här studien undersöks huruvida den tekniskt obsoleta konstformen pixelgrafik, eller ”pixel art” fortfarande är ett relevant medium och vilka kvaliteter som gör det till ett självständigt praktiskt hantverk. I en kvalitativ studie intervjuas fyra erfarna pixel konstnärer om deras definitioner av ”pixel art” samt deras egen praktik. Dessa analyseras sedan med hjälp av befintliga teorier inom design och praktisk kunskap. Studien finner att en anledning till att pixelgrafik fortsatt att vara relevant beror på begräsningarna som har sin bakgrund i tekniska restriktioner i gammal hårdvara ger upphov till kreativitet. Det simpla fyrkantsmönstret som pixelgrafik bygger på är lätt för nybörjare att ta till sig och stimulerar kreativ problemlösning. Begränsningarna tvingar konstnären att kompromissa vad gäller val av färg och form. Pixel art har mer gemensamt med gamla tekniker som korsstygn eller mosaikläggning än andra typer av samtida digital konst. Själva utförandet beskrivs av respondenterna som meditativt och jämförbart med att lägga ett pussel eller att spela Tetris. Trots att det är ett medium som ofta är förknippat med nostalgi och en ”retro-estetik” så är det fortfarande en konstform som utvecklas och som fortsatt att vara populär. / This study seeks to answer what makes the technically obsolete medium of pixel art still relevant and what qualities make it into an independent craft. In a qualitative study four highly experienced pixel artists are interviewed about their own definitions of pixel art and their methodology. The answers are then analyzed through the lenses of existing theories on design and practical knowledge. The study concludes that a reason why pixel art has remained relevant is that the limitations that had their origins in the technical restrictions of early computer hardware gives way to creativity. The simple pixel-grid is easy for beginners to get a grip on and stimulates creative problem-solving, forcing the artist to compromise in regard to the choice of colors and shapes. In that regard it begs comparisons to cross-stitching and mosaics rather than most contemporary digital art. The immediacy of managing these restrictions was described as meditative and can be compared to solving a puzzle or a game of Tetris. Despite its reputation for being a backwards-gazing medium linked with retro videogames the artists insist the artform is still being evolved and elevated today.

In this project report a novel pixel-based approach to digital pathology is proposed. The algorithm directly decides the class of single pixels in an image without needing the larger context of neighbouring pixels. This allows researchers to circumvent complications that might arise from using classical cell segmentation methods based around counting cells - which then relies on the cell segmentation being close to perfect. Such issues are avoided by pixel-based approaches by instead directly measuring total area. The algorithm is tested on the BOMI2 Redox dataset consisting of 79 samples of multi-spectral images from lung cancer patients. The results of the algorithm are compared against ground truth data in the form of RNA sequencing data from the same patient cores as the images are taken. The algorithm achieves Spearman correlations in the range of R = [0.4,0.6], thereby serving as an initial testament to the validity of pixel-based methods. Furthermore an automatic method for deciding biomarker threshold values is proposed, based around finding the knee point of the biomarker histogram. The threshold values found by the algorithm on the BOMI2 Redox data set are reasonable. The method opens up for a standardised way of deciding thresholds in digital pathology, allowing easier comparison between research results from different researchers.

The characterization of modern jet engines or stationary gas turbines running with lean combustion by means of swirl-stabilized ames necessitates seedingless optical field measurements of the ame transfer function, i.e. the ratio of the uctuating heat release rate inside the ame volume, the instationary ow velocity at the combustor outlet and the time average of both quantities. For this reason, a high-speed camera-based laser interferometric vibrometer is proposed for spatio-temporally resolved measurements of the ame transfer function inside a swirl-stabilized technically premixed ame. Each pixel provides line-of-sight measurements of the heat release rate due to the linear coupling to uctuations of the refractive index along the laser beam, which are based on density uctuations inside the ame volume. Additionally, field measurements of the instationary ow velocity are possible due to correlation of simultaneously measured pixel signals and the known distance between the measurement positions. Thus, the new system enables the spatially resolved detection of the ame transfer function and instationary ow behavior with a single measurement for the first time. The presented setup offers single pixel resolution with measurement rates up to 40 kHz at an maximum image resolution of 256 px x 128 px. Based on a comparison with reference measurements using a standard pointwise laser interferometric vibrometer, the new system is validated and a discussion of the measurement uncertainty is presented. Finally, the measurement of refractive index uctuations inside a ame volume is demonstrated.

This master's thesis deals with acceleration of pixel interpolation methods using the GPU and NVIDIA (R) CUDA TM architecture. Graphic output is represented by a demonstrational application for geometrical image transforms using chosen interpolation method. Time critical parts of the code are moved on the GPU and executed in parallel. There are used highly optimized routines from the OpenCV library, made by the Intel company for an image and video processing.

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database. / Libyan Centre for Remote Sensing and Space Science / Appendix A was submitted with extra data files which are not available online.

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[pt] A segmentação semântica das estruturas anatômicas em imagens de mamografia desempenha um papel significativo no apoio da análise médica. Esta tarefa pode ser abordada com o uso de um modelo de aprendizado de máquina, que deve ser capaz de identificar e delinear corretamente as estruturas de interesse tais como papila, tecido fibroglandular, músculo peitoral e tecido gorduroso. No entanto, a segmentação de estruturas pequenas como papila e peitoral é frequentemente um desafio. Sendo o maior desafio o reconhecimento ou deteção do músculo peitoral na vista craniocaudal (CC), devido ao seu tamanho variável, possíveis ausências e sobreposição de tecido fibroglandular. Para enfrentar esse desafio, este trabalho propõe uma abordagem centrada em dados para melhorar o desempenho do modelo de segmentação na papila mamária e no músculo peitoral. Especificamente, aprimorando os dados de treinamento e as anotações em duas etapas. A primeira etapa é baseada em modificações nas anotações. Foram desenvolvidos algoritmos para buscar automaticamente anotações fora do comum dependendo da sua forma. Com estas anotações encontradas, foi feita uma revisão e correção manual. A segunda etapa envolve um downsampling do conjunto de dados, reduzindo as amostras de imagens do conjunto de treinamento. Foram analisados os casos de falsos positivos e falsos negativos, identificando as imagens que fornecem informações confusas, para posteriormente removê-las do conjunto. Em seguida, foram treinados modelos usando os dados de cada etapa e foram obtidas as métricas de classificação para o músculo peitoral em vista CC e o IoU para cada estrutura nas vistas CC e MLO (Mediolateral Oblíqua). Os resultados do treinamento mostram uma melhora progressiva na identificação e segmentação do músculo peitoral em vista CC e uma melhora na papila em vista MLO, mantendo as métricas para as demais estruturas. / [en] The semantic segmentation of anatomical structures in mammography images plays a significant role in supporting medical analysis. This task can be approached using a machine learning model, which must be capable of identifying and accurately delineating the structures. However, segmentation of small structures such as nipple and pectoral is often challenging. Especially in there cognition or detection of the pectoral muscle in the craniocaudal (CC) view,due to its variable size, possible absences and overlapping of fibroglandular tissue.To tackle this challenge, this work proposes a data-centric approach to improvethe segmentation model s performance on the mammary papilla and pectoral muscle. Specifically, enhancing the training data and annotations in two stages.The first stage is based on modifications to the annotations. Algorithms were developed to automatically search for uncommon annotations dependingon their shape. Once these annotations were found, a manual review and correction were performed.The second stage involves downsampling the dataset, reducing the image samples in the training set. Cases of false positives and false negatives were analyzed, identifying images that provide confusing information, which were subsequently removed from the set. Next, models were trained using the data from each stage, and classification metrics were obtained for the pectoral muscle in the CC view and IoU for each structure in CC and MLO (mediolateral oblique) views. The training results show a progressive improvement in the identification and segmentation of the pectoral muscle in the CC view and an enhancement in the mammary papilla in the MLO view, while maintaining segmentation metricsfor the other structures.