Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Compressed Sensing for Open-ended Waveguide Non-Destructive Testing and Evaluation

Ph. D. ThesisNon-destructive testing and evaluation (NDT&E) systems using open-ended waveguide (OEW) suffer from critical challenges. In the sensing stage, data acquisition is time-consuming by raster scan, which is difficult for on-line detection. Sensing stage also disregards demand for the latter feature extraction process, leading to an excessive amount of data and processing overhead for feature extraction. In the feature extraction stage, efficient and robust defect region segmentation in the obtained image is challenging for a complex image background. Compressed sensing (CS) demonstrates impressive data compression ability in various applications using sparse models. How to develop CS models in OEW NDT&E that jointly consider sensing & processing for fast data acquisition, data compression, efficient and robust feature extraction is remaining challenges. This thesis develops integrated sensing-processing CS models to address the drawbacks in OEW NDT systems and carries out their case studies in low-energy impact damage detection for carbon fibre reinforced plastics (CFRP) materials. The major contributions are: (1) For the challenge of fast data acquisition, an online CS model is developed to offer faster data acquisition and reduce data amount without any hardware modification. The images obtained with OEW are usually smooth which can be sparsely represented with discrete cosine transform (DCT) basis. Based on this information, a customised 0/1 Bernoulli matrix for CS measurement is designed for downsampling. The full data is reconstructed with orthogonal matching pursuit algorithm using the downsampling data, DCT basis, and the customised 0/1 Bernoulli matrix. It is hard to determine the sampling pixel numbers for sparse reconstruction when lacking training data, to address this issue, an accumulated sampling and recovery process is developed in this CS model. The defect region can be extracted with the proposed histogram threshold edge detection (HTED) algorithm after each recovery, which forms an online process. A case study in impact damage detection on CFRP materials is carried out for validation. The results show that the data acquisition time is reduced by one order of magnitude while maintaining equivalent image quality and defect region as raster scan. (2) For the challenge of efficient data compression that considers the later feature extraction, a feature-supervised CS data acquisition method is proposed and evaluated. It reserves interested features while reducing the data amount. The frequencies which reveal the feature only occupy a small part of the frequency band, this method finds these sparse frequency range firstly to supervise the later sampling process. Subsequently, based on joint sparsity of neighbour frame and the extracted frequency band, an aligned spatial-spectrum sampling scheme is proposed. The scheme only samples interested frequency range for required features by using a customised 0/1 Bernoulli measurement matrix. The interested spectral-spatial data are reconstructed jointly, which has much faster speed than frame-by-frame methods. The proposed feature-supervised CS data acquisition is implemented and compared with raster scan and the traditional CS reconstruction in impact damage detection on CFRP materials. The results show that the data amount is reduced greatly without compromising feature quality, and the gain in reconstruction speed is improved linearly with the number of measurements. (3) Based on the above CS-based data acquisition methods, CS models are developed to directly detect defect from CS data rather than using the reconstructed full spatial data. This method is robust to texture background and more time-efficient that HTED algorithm. Firstly, based on the histogram is invariant to down-sampling using the customised 0/1 Bernoulli measurement matrix, a qualitative method which only gives binary judgement of defect is developed. High probability of detection and accuracy is achieved compared to other methods. Secondly, a new greedy algorithm of sparse orthogonal matching pursuit (spOMP)-based defect region segmentation method is developed to quantitatively extract the defect region, because the conventional sparse reconstruction algorithms cannot properly use the sparse character of correlation between the measurement matrix and CS data. The proposed algorithms are faster and more robust to interference than other algorithms.China Scholarship Counci

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Contributions to region-based image and video analysis: feature aggregation, background subtraction and description constraining

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 22-01-2016Esta tesis tiene embargado el acceso al texto completo hasta el 22-07-2017The use of regions for image and video analysis has been traditionally motivated by their ability to diminish the number of processed units and hence, the number of required decisions. However, as we explore in this thesis, this is just one of the potential advantages that regions may provide. When dealing with regions, two description spaces may be differentiated: the decision space, on which regions are shaped—region segmentation—, and the feature space, on which regions are used for analysis—region-based applications—. These two spaces are highly related. The solutions taken on the decision space severely affect their performance in the feature space. Accordingly, in this thesis we propose contributions on both spaces. Regarding the contributions to region segmentation, these are two-fold. Firstly, we give a twist to a classical region segmentation technique, the Mean-Shift, by exploring new solutions to automatically set the spectral kernel bandwidth. Secondly, we propose a method to describe the micro-texture of a pixel neighbourhood by using an easily customisable filter-bank methodology—which is based on the discrete cosine transform (DCT)—. The rest of the thesis is devoted to describe region-based approaches to several highly topical issues in computer vision; two broad tasks are explored: background subtraction (BS) and local descriptors (LD). Concerning BS, regions are here used as complementary cues to refine pixel-based BS algorithms: by providing robust to illumination cues and by storing the background dynamics in a region-driven background modelling. Relating to LD, the region is here used to reshape the description area usually fixed for local descriptors. Region-masked versions of classical two-dimensional and three-dimensional local descriptions are designed. So-built descriptions are proposed for the task of object identification, under a novel neural-oriented strategy. Furthermore, a local description scheme based on a fuzzy use of the region membership is derived. This characterisation scheme has been geometrically adapted to account for projective deformations, providing a suitable tool for finding corresponding points in wide-baseline scenarios. Experiments have been conducted for every contribution, discussing the potential benefits and the limitations of the proposed schemes. In overall, obtained results suggest that the region—conditioned by successful aggregation processes—is a reliable and useful tool to extrapolate pixel-level results, diminish semantic noise, isolate significant object cues and constrain local descriptions. The methods and approaches described along this thesis present alternative or complementary solutions to pixel-based image processing.El uso de regiones para el análisis de imágenes y secuencias de video ha estado tradicionalmente motivado por su utilidad para disminuir el número de unidades de análisis y, por ende, el número de decisiones. En esta tesis evidenciamos que esta es sólo una de las muchas ventajas adheridas a la utilización de regiones. En el procesamiento por regiones deben distinguirse dos espacios de análisis: el espacio de decisión, en donde se construyen las regiones, y el espacio de características, donde se utilizan. Ambos espacios están altamente relacionados. Las soluciones diseñadas para la construcción de regiones en el espacio de decisión definen su utilidad en el espacio de análisis. Por este motivo, a lo largo de esta tesis estudiamos ambos espacios. En particular, proponemos dos contribuciones en la etapa de construcción de regiones. En la primera, revisitamos una técnica clásica, Mean-Shift, e introducimos un esquema para la selección automática del ancho de banda que permite estimar localmente la densidad de una determinada característica. En la segunda, utilizamos la transformada discreta del coseno para describir la variabilidad local en el entorno de un píxel. En el resto de la tesis exploramos soluciones en el espacio de características, en otras palabras, proponemos aplicaciones que se apoyan en la región para realizar el procesamiento. Dichas aplicaciones se centran en dos ramas candentes en el ámbito de la visión por computador: la segregación del frente por substracción del fondo y la descripción local de los puntos de una imagen. En la rama substracción de fondo, utilizamos las regiones como unidades de apoyo a los algoritmos basados exclusivamente en el análisis a nivel de píxel. En particular, mejoramos la robustez de estos algoritmos a los cambios locales de iluminación y al dinamismo del fondo. Para esta última técnica definimos un modelo de fondo completamente basado en regiones. Las contribuciones asociadas a la rama de descripción local están centradas en el uso de la región para definir, automáticamente, entornos de descripción alrededor de los puntos. En las aproximaciones existentes, estos entornos de descripción suelen ser de tamaño y forma fija. Como resultado de este procedimiento se establece el diseño de versiones enmascaradas de descriptores bidimensionales y tridimensionales. En el algoritmo desarrollado, organizamos los descriptores así diseñados en una estructura neuronal y los utilizamos para la identificación automática de objetos. Por otro lado, proponemos un esquema de descripción mediante asociación difusa de píxeles a regiones. Este entorno de descripción es transformado geométricamente para adaptarse a potenciales deformaciones proyectivas en entornos estéreo donde las cámaras están ampliamente separadas. Cada una de las aproximaciones desarrolladas se evalúa y discute, remarcando las ventajas e inconvenientes asociadas a su utilización. En general, los resultados obtenidos sugieren que la región, asumiendo que ha sido construida de manera exitosa, es una herramienta fiable y de utilidad para: extrapolar resultados a nivel de pixel, reducir el ruido semántico, aislar las características significativas de los objetos y restringir la descripción local de estas características. Los métodos y enfoques descritos a lo largo de esta tesis establecen soluciones alternativas o complementarias al análisis a nivel de píxelIt was partially supported by the Spanish Government trough its FPU grant program and the projects (TEC2007-65400 - SemanticVideo), (TEC2011-25995 Event Video) and (TEC2014-53176-R HAVideo); the European Commission (IST-FP6-027685 - Mesh); the Comunidad de Madrid (S-0505/TIC-0223 - ProMultiDis-CM) and the Spanish Administration Agency CENIT 2007-1007 (VISION)

Animating the evolution of software

The use and development of open source software has increased significantly in the last decade. The high frequency of changes and releases across a distributed environment requires good project management tools in order to control the process adequately. However, even with these tools in place, the nature of the development and the fact that developers will often work on many other projects simultaneously, means that the developers are unlikely to have a clear picture of the current state of the project at any time. Furthermore, the poor documentation associated with many projects has a detrimental effect when encouraging new developers to contribute to the software. A typical version control repository contains a mine of information that is not always obvious and not easy to comprehend in its raw form. However, presenting this historical data in a suitable format by using software visualisation techniques allows the evolution of the software over a number of releases to be shown. This allows the changes that have been made to the software to be identified clearly, thus ensuring that the effect of those changes will also be emphasised. This then enables both managers and developers to gain a more detailed view of the current state of the project. The visualisation of evolving software introduces a number of new issues. This thesis investigates some of these issues in detail, and recommends a number of solutions in order to alleviate the problems that may otherwise arise. The solutions are then demonstrated in the definition of two new visualisations. These use historical data contained within version control repositories to show the evolution of the software at a number of levels of granularity. Additionally, animation is used as an integral part of both visualisations - not only to show the evolution by representing the progression of time, but also to highlight the changes that have occurred. Previously, the use of animation within software visualisation has been primarily restricted to small-scale, hand generated visualisations. However, this thesis shows the viability of using animation within software visualisation with automated visualisations on a large scale. In addition, evaluation of the visualisations has shown that they are suitable for showing the changes that have occurred in the software over a period of time, and subsequently how the software has evolved. These visualisations are therefore suitable for use by developers and managers involved with open source software. In addition, they also provide a basis for future research in evolutionary visualisations, software evolution and open source development