Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

Human stereo matching is not restricted to epipolar lines

AbstractComputational approaches to stereo matching have often taken advantage of a geometric constraint which states that matching elements in the left and right eye images will always fall on “epipolar lines”. The use of this epipolar constraint reduces the search space from two dimensions to one, producing a tremendous saving in the computation time required to find the matching solution. Use of this constraint requires a precise knowledge of the relative horizontal, vertical and torsional positions of the two eyes, however, and this information may be unavailable in many situations. Experiments with dynamic random element stereograms reveal that human stereopsis can detect and identify the depth of matches over a range of both vertical and horizontal disparity. Observers were able to make accurate near/far depth discriminations when vertical disparity was as large as 45 arcmin, and were able to detect the presence of correlation over a slightly larger range. Thus, human binocular matching sensitivity is not strictly constrained to epipolar lines

Automatic Real-Time Pose Estimation of Machinery from Images

The automatic positioning of machines in a large number of application areas is an important aspect of automation. Today, this is often done using classic geodetic sensors such as Global Navigation Satellite Systems (GNSS) and robotic total stations. In this work, a stereo camera system was developed that localizes a machine at high frequency and serves as an alternative to the previously mentioned sensors. For this purpose, algorithms were developed that detect active markers on the machine in a stereo image pair, find stereo point correspondences, and estimate the pose of the machine from these. Theoretical influences and accuracies for different systems were estimated with a Monte Carlo simulation, on the basis of which the stereo camera system was designed. Field measurements were used to evaluate the actual achievable accuracies and the robustness of the prototype system. The comparison is present with reference measurements with a laser tracker. The estimated object pose achieved accuracies higher than [Formula: see text] with the translation components and accuracies higher than [Formula: see text] with the rotation components. As a result, 3D point accuracies higher than [Formula: see text] were achieved by the machine. For the first time, a prototype could be developed that represents an alternative, powerful image-based localization method for machines to the classical geodetic sensors

Detecção de eventos complexos em vídeos baseada em ritmos visuais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de eventos complexos em vídeos possui várias aplicações práticas relevantes, alavancadas pela grande disponibilidade de câmeras digitais instaladas em aeroportos, estações de ônibus e trens, centros de compras, estádios, hospitais, escolas, prédios, estradas, entre vários outros locais. Avanços na tecnologia digital têm aumentado as capacidades dos sistemas em reconhecer eventos em vídeos por meio do desenvolvimento de dispositivos com alta resolução, dimensões físicas pequenas e altas taxas de amostragem. Muitos trabalhos disponíveis na literatura têm explorado o tema a partir de diferentes pontos de vista. Este trabalho apresenta e avalia uma metodologia para extrair características dos ritmos visuais no contexto de detecção de eventos em vídeos. Um ritmo visual pode ser visto com a projeção de um vídeo em uma imagem, tal que a tarefa de análise de vídeos é reduzida a um problema de análise de imagens, beneficiando-se de seu baixo custo de processamento em termos de tempo e complexidade. Para demonstrar o potencial do ritmo visual na análise de vídeos complexos, três problemas da área de visão computacional são selecionados: detecção de eventos anômalos, classificação de ações humanas e reconhecimento de gestos. No primeiro problema, um modelo e? aprendido com situações de normalidade a partir dos rastros deixados pelas pessoas ao andar, enquanto padro?es representativos das ações são extraídos nos outros dois problemas. Nossa hipo?tese e? de que vídeos similares produzem padro?es semelhantes, tal que o problema de classificação de ações pode ser reduzido a uma tarefa de classificação de imagens. Experimentos realizados em bases públicas de dados demonstram que o método proposto produz resultados promissores com baixo custo de processamento, tornando-o possível aplicar em tempo real. Embora os padro?es dos ritmos visuais sejam extrai?dos como histograma de gradientes, algumas tentativas para adicionar características do fluxo o?tico são discutidas, além de estratégias para obter ritmos visuais alternativosAbstract: The recognition of complex events in videos has currently several important applications, particularly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, hospitals, schools, buildings, roads, among others. Moreover, advances in digital technology have enhanced the capabilities for detection of video events through the development of devices with high resolution, small physical size, and high sampling rates. Many works available in the literature have explored the subject from different perspectives. This work presents and evaluates a methodology for extracting a feature descriptor from visual rhythms of video sequences in order to address the video event detection problem. A visual rhythm can be seen as the projection of a video onto an image, such that the video analysis task can be reduced into an image analysis problem, benefiting from its low processing cost in terms of time and complexity. To demonstrate the potential of the visual rhythm in the analysis of complex videos, three computer vision problems are selected in this work: abnormal event detection, human action classification, and gesture recognition. The former problem learns a normalcy model from the traces that people leave when they walk, whereas the other two problems extract representative patterns from actions. Our hypothesis is that similar videos produce similar patterns, therefore, the action classification problem is reduced into an image classification task. Experiments conducted on well-known public datasets demonstrate that the method produces promising results at high processing rates, making it possible to work in real time. Even though the visual rhythm features are mainly extracted as histogram of gradients, some attempts for adding optical flow features are discussed, as well as strategies for obtaining alternative visual rhythmsMestradoCiência da ComputaçãoMestre em Ciência da Computação1570507, 1406910, 1374943CAPE

The Role of Vergence Micromovements on Depth Perception

A new approach in stereo vision is proposed which recovers 3D depth information using continuous vergence angle control with simultaneous local correspondence response. This technique relates elements with the same relative position in the left and right images for a continuous sequence of vergence angles. the approach considers the extremely fine vergence movements about a given fixation point within the depth of field boundaries. It allows the recovery of 3D depth information given the knowledge of the system\u27s geometry and a sequence of pairs [αi, Ci], where αi is the ith vergence angle and Ci is the ith matrix of correspondence responses. The approach has several advantages over the current ones. First, due to its local operation characteristics, the resulting algorithms can be implemented in a modular hardware scheme. Second, unlike currently used algorithms, there is no need to compute depth from disparity values; at the cost of the acquisition of a sequence of images during the micromovements. The approach also greatly reduces the errors in stereo due to the sensor quantization. Last, and most important of all, the approach is supported by experimental results from physiology and psychophysics. Physiological results show that the human eye performs fine movements during the process of fixation on a single point, which are collectively called physiological nystagmus. One such movement, called binocular flicks, happens in opposing directions and produces convergence/divergence of the eyes. These are the micromovements that we suppose are the basis for depth perception. Therefore, the approach proposes a functional correlation between these vergence micromovements, depth perception, stereo acuity and stereo fusion

Spherical Image Processing for Immersive Visualisation and View Generation

This research presents the study of processing panoramic spherical images for immersive visualisation of real environments and generation of in-between views based on two views acquired. For visualisation based on one spherical image, the surrounding environment is modelled by a unit sphere mapped with the spherical image and the user is then allowed to navigate within the modelled scene. For visualisation based on two spherical images, a view generation algorithm is developed for modelling an indoor manmade environment and new views can be generated at an arbitrary position with respect to the existing two. This allows the scene to be modelled using multiple spherical images and the user to move smoothly from one sphere mapped image to another one by going through in-between sphere mapped images generated

A Sterescopic System to Measure Water Waves in Laboratories

A new system for estimating the synthetic parameters of sea states during physical investigations has been implemented. The technique proposed herein is based on stereographic analysis of digital images acquired with optical sensors. A series of ad hoc floating markers has been made and properly moored to the bottom of a large wave tank to estimate the synthetic parameters of generated waves. The implemented acquisition system and the proposed algorithm provide automatic recognition of all markers by a pair of optical sensors that synchronously captures their instantaneous location and tracks their movements over time. After transformation from the image to the real-world coordinates, water surface elevation time series have been obtained. Several experimental tests have been carried out to assess the feasibility and reliability of the proposed approach. The estimated wave synthetic parameters have been then compared with those obtained by employing standard resistive probes. The deviation were found to be equal to ~6% for the significant wave height and 1% for peak, mean, and significant wave periods