Chopin’s Etudes are difficult musical compositions for advanced piano students. Helmut Brauss, a professional pianist and educator, has created a number of videos to teach students motion patterns that will help them perfect the Etudes. The subtleties of motion shown in the videos are not apparently obvious to students, and in our research, we have developed four markerless based approaches to visually augment the videos: Predictive Optical Flow, Historical Optical Flow, Predictive Hand Tracking and Historical Hand Tracking. A survey of students learning the Etudes was conducted, and it was determined that the participants found the Historical techniques to be the most useful. No difference could be found between the usefulness of the Optical Flow and Hand Tracking augmentations. / Graduate

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2006Bitstream added on 2014-06-13T19:40:16Z : No. of bitstreams: 1 barbosa_rl_dr_prud.pdf: 1353454 bytes, checksum: 109a85af0d056d18c721a2c6af70ce93 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Em certas condições, os sensores de orientação e posicionamento (INS e GPS) de um Sistema Móvel de Mapeamento Terrestre (SMMT) ficam indisponíveis por algum intervalo de tempo casionando a perda da orientação e do posicionamento das imagens capturadas neste intervalo. Neste trabalho, é proposta uma solução baseada apenas nas imagens sem a utilização de sensores ou informações externas às mesmas, através do fluxo óptico. Um sistema móvel com um par de vídeo câmaras, denominado Unidade Móvel de Mapeamento Digital (UMMD), foi utilizado para testar a metodologia proposta em uma via plana. As câmaras são fixadas em uma base com um afastamento entre as câmaras de 0,94m e paralelas ao eixo de deslocamento (Y). A velocidade do veículo é estimada, inicialmente, com base no fluxo óptico denso. Em seguida, a estimação da velocidade é melhorada após uma filtragem, que consiste em: utilizar os vetores que apresentam comportamento radial na metade inferior das imagens e que foram detectados pelo algoritmo de Canny, acrescida uma segunda etapa na estimação da velocidade com eliminação de erros grosseiros. Com a velocidade estimada e sabendo-se o tempo de amostragem do vídeo, o deslocamento de cada imagem é determinado e esta informação é utilizada como aproximação inicial para o posicionamento das câmaras. Os resultados mostraram que a velocidade estimada ficou próxima da velocidade verdadeira e a qualidade do ajustamento se mostrou razoável, considerando-se a não utilização de sensores externos e de pontos de apoio. / Under certain conditions the positioning and orientation sensors such as INS and GPS of a land-based mobile mapping system may fail for a certain time interval. The consequence is that the images captured during this time interval may be misoriented or even may have no orientation. This thesis proposes a solution to orient the images based only on image processing and a photogrammetric technique without any external sensors in order to overcome the lack of external orientation. A land-based mobile mapping system with a pair of video cameras and a GPS receiver was used to test the proposed methodology on an urban flat road. The video cameras were mounted on the roof of the vehicle with both optical axes parallel to the main road axis (Y). The methodology is based on the velocity estimation of the vehicle, which is done in two steps. Initially, the dense optical flow is computed then the velocity estimation is obtained through a filtering strategy that consists of using radial vectors in the low parts of the images. These radial vectors are detected by the Canny algorithm. The vehicle velocity is re-estimated after eliminating the optical flow outliers. With the reestimated velocity and with the video sampling time the spatial displacement of each image (with respect to the previous one of the sequence) is determined. The results show that the estimated velocity is pretty close to the true one and the quality of the least square adjustment is quite acceptable, considering that no external sensors were used.

abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Multimodal biometric systems have been subject of study in recent decades, theirunique characteristic of Anti spoofing and liveness detection plus ability to deal withaudio noise made them technology candidates for improving current systems such asvoice recognition, verification and identification systems.In this work we studied feasibility of incorporating audio-visual voice recognitionsystem for dealing with audio noise in the truck cab environment. Speech recognitionsystems suffer from excessive noise from the engine and road traffic and cars stereosystem. To deal with this noise different techniques including active and passive noisecancelling have been studied.Our results showed that although audio-only systems are performing better in noisefree environment their performance drops significantly by increase in the level of noisein truck cabins, which by contrast does not affect the performance of visual features.Final fused system comprising both visual and audio cues, proved to be superior toboth audio-only and video-only systems.

The most common navigation aid visually-impaired people employ is a white cane, but, recently, technology has given rise to a varied set of sophisticated navigation aids. While these new aids can provide more assistance to a visually-impaired person than a white cane, they tend to be expensive due to a small market segment, which in turn can reduce their accessibility. In an effort to produce a technologically-advanced yet accessible navigation aid, an Android application is proposed that detects and notifies users about obstacles within their path through the use of a smartphone's camera. While the smartphone is mounted on a harness worn by the user, the Walking Assistant application operates by capturing images as the user walks, finding features of objects within each frame, and determining how the features have moved from image to image. If it is discovered that an object is moving towards the user, the Walking Assistant will activate the smartphone's vibration mode to alert the user to the object's presence. Additionally, the user can control the Walking Assistant through the use of either touch or voice commands. By conducting real-world tests, it was determined that the Walking Assistant can correctly identify obstacles 42.1% of the time, while generating false positive obstacle identifications only 15.0% of the time. The accuracy of the Walking Assistant can be further improved by implementing additional features, such as a fuzzy-decision-based thresholding system or image stabilization.

Radiation therapy has been successful in treating lung cancer patients, but its efficacy is limited by the inability to account for the respiratory motion during treatment planning and radiation dose delivery. Physics-based lung deformation models facilitate the motion computation of both tumor and local lung tissue during radiation therapy. In this dissertation, a novel method is discussed to accurately register 3D lungs across the respiratory phases from 4D-CT datasets, which facilitates the estimation of the volumetric lung deformation models. This method uses multi-level and multi-resolution optical flow registration coupled with thin plate splines (TPS), to address registration issue of inconsistent intensity across respiratory phases. It achieves higher accuracy as compared to multi-resolution optical flow registration and other commonly used registration methods. Results of validation show that the lung registration is computed with 3 mm Target Registration Error (TRE) and approximately 3 mm Inverse Consistency Error (ICE). This registration method is further implemented in GPU based real time dose delivery simulation to assist radiation therapy planning.

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Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation. / Master of Science

This thesis investigates the use of monocular computer vision for odometry, specificallyemploying optical flow techniques. The goal is to develop and evaluate avisual odometry system that accurately estimates the trajectory and rotation of acamera in real-time. The system utilizes a camera and the Lucas-Kanade methodto capture high-frame-rate images, detect and track features within these images,and calculate the camera’s motion based on the movement of these features.Odometry is essential for estimating the position and orientation of moving objects,such as robots or vehicles, over time. Traditional methods rely on wheel encodersand Inertial Measurement Units (IMU), but visual odometry leverages visual datato enhance accuracy and robustness, without the risk of slippage and change inwheel diameter from loads. Furthermore a visual odometry system, like the oneused in this project, is not affected by occlusion.In this project, a camera was set up and calibrated, followed by the implementationof feature detection using the Shi-Tomasi corner detection algorithm. TheLucas-Kanade method was then applied to estimate optical flow, and an affinetransformation was used to compute the translation and rotation of the camera.The system’s performance was evaluated based on accuracy, computational efficiency,and robustness to noise.The results demonstrate that the visual odometry system can effectively track thecamera’s motion with a high degree of accuracy. But with limitations in speed. Thediscussion highlights potential applications in autonomous navigation and areas forfuture improvement, such as integrating additional sensors and enhancing featuredetection algorithm. / Denna rapport undersöker användningen av monokulärt datorseende förodometri, med särskilt fokus på optisk flödesteknik. Målet är att utveckla ochutvärdera ett visuellt odometrisystem som noggrant uppskattar en kameras positionoch rotation i realtid. Systemet använder en kamera och Lucas-Kanademetodenför att fånga bilder i hög frekvens, upptäcka och spåra pixelpunkter inomdessa bilder samt beräkna kamerans rörelse baserat på dessa punkters förflyttning.Odometri är viktigt för att uppskatta position och orientering av rörliga objekt,såsom robotar eller fordon, över tid. Traditionella metoder förlitar sig på hjulenkodersoch Inetrial Measurement Units (IMU), men visuell odometri utnyttjarvisuella data för att förbättra noggrannheten och robustheten, utan risken förslirning och förändringar i hjuldiametern på grund av belastningar. Dessutompåverkas inte ett visuellt odometrisystem, som det som används i detta projekt,av ocklusion.I detta projekt installerades och kalibrerades en kamera, följt av implementeringav funktionsdetektion med hjälp av Shi-Tomasi hörndetektionsalgoritm.Därefter tillämpades Lucas-Kanade-metoden för att uppskatta optiskt flöde, ochen affin transformation användes för att beräkna kamerans förflyttning och rotation.Systemets prestanda utvärderades utifrån noggrannhet, beräkningseffektivitetoch robusthet mot brus.Resultaten visar att det visuella odometrisystemet effektivt kan spåra kameransrörelse med hög noggrannhet. Med begränsning beroende på hastighet. Diskussionenbelyser potentiella tillämpningar inom autonom navigering och områden förframtida förbättringar, såsom integrering av ytterligare sensorer och förbättringav feature detection algoritmen.