Real-time face swapping as a tool for understanding infant self-recognition

International audienceTo study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods

Algorithm/Architecture Co-Exploration of Visual Computing: Overview and Future Perspectives

Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms with emerging platforms that have multiple cores and reconfigurable architecture. As the algorithms in forthcoming visual systems become increasingly complex, many applications must have different profiles with different levels of performance. Hence, with expectations that the visual experience in the future will become continuously better, it is critical that advanced platforms provide higher performance, better flexibility, and lower power consumption. To achieve these goals, algorithm and architecture co-design is significant for characterizing the algorithmic complexity used to optimize targeted architecture. This paper shows that seamless weaving of the development of previously autonomous visual computing algorithms and multicore or reconfigurable architectures will unavoidably become the leading trend in the future of video technology

Algorithms and architectures for MCMC acceleration in FPGAs

Markov Chain Monte Carlo (MCMC) is a family of stochastic algorithms which are used to draw random samples from arbitrary probability distributions. This task is necessary to solve a variety of problems in Bayesian modelling, e.g. prediction and model comparison, making MCMC a fundamental tool in modern statistics. Nevertheless, due to the increasing complexity of Bayesian models, the explosion in the amount of data they need to handle and the computational intensity of many MCMC algorithms, performing MCMC-based inference is often impractical in real applications. This thesis tackles this computational problem by proposing Field Programmable Gate Array (FPGA) architectures for accelerating MCMC and by designing novel MCMC algorithms and optimization methodologies which are tailored for FPGA implementation. The contributions of this work include: 1) An FPGA architecture for the Population-based MCMC algorithm, along with two modified versions of the algorithm which use custom arithmetic precision in large parts of the implementation without introducing error in the output. Mapping the two modified versions to an FPGA allows for more parallel modules to be instantiated in the same chip area. 2) An FPGA architecture for the Particle MCMC algorithm, along with a novel algorithm which combines Particle MCMC and Population-based MCMC to tackle multi-modal distributions. A proposed FPGA architecture for the new algorithm achieves higher datapath utilization than the Particle MCMC architecture. 3) A generic method to optimize the arithmetic precision of any MCMC algorithm that is implemented on FPGAs. The method selects the minimum precision among a given set of precisions, while guaranteeing a user-defined bound on the output error. By applying the above techniques to large-scale Bayesian problems, it is shown that significant speedups (one or two orders of magnitude) are possible compared to state-of-the-art MCMC algorithms implemented on CPUs and GPUs, opening the way for handling complex statistical analyses in the era of ubiquitous, ever-increasing data.Open Acces

Computer vision based navigation for spacecraft proximity operations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 219-226).The use of computer vision for spacecraft relative navigation and proximity operations within an unknown environment is an enabling technology for a number of future commercial and scientific space missions. This thesis presents three first steps towards a larger research initiative to develop and mature these technologies. The first step that is presented is the design and development of a " flight-traceable" upgrade to the Synchronize Position Hold Engage Reorient Experimental Satellites, known as the SPHERES Goggles. This upgrade enables experimental research and maturation of computer vision based navigation technologies on the SPHERES satellites. The second step that is presented is the development of an algorithm for vision based relative spacecraft navigation that uses a fiducial marker with the minimum number of known point correspondences. An experimental evaluation of this algorithm is presented that determines an upper bound on the accuracy and precision of this system. The third step towards vision based relative navigation in an unknown environment is a preliminary investigation into the computational issues associated with high performance embedded computing. The computational characteristics of vision based relative navigation algorithms are discussed along with the requirements that they impose on computational hardware. A trade study is performed which compares a number of dierent commercially available hardware architectures to determine which would provide the best computational performance per unit of electrical power.by Brent Edward Tweddle.S.M

Pedestrian detection in far infrared images

Detection of people in images is a relatively new field of research, but has been widely accepted. The applications are multiple, such as self-labeling of large databases, security systems and pedestrian detection in intelligent transportation systems. Within the latter, the purpose of a pedestrian detector from a moving vehicle is to detect the presence of people in the path of the vehicle. The ultimate goal is to avoid a collision between the two. This thesis is framed with the advanced driver assistance systems, passive safety systems that warn the driver of conditions that may be adverse. An advanced driving assistance system module, aimed to warn the driver about the presence of pedestrians, using computer vision in thermal images, is presented in this thesis. Such sensors are particularly useful under conditions of low illumination.The document is divided following the usual parts of a pedestrian detection system: development of descriptors that define the appearance of people in these kind of images, the application of these descriptors to full-sized images and temporal tracking of pedestrians found. As part of the work developed in this thesis, database of pedestrians in the far infrared spectrum is presented. This database has been used in developing an evaluation of pedestrian detection systems as well as for the development of new descriptors. These descriptors use techniques for the systematic description of the shape of the pedestrian as well as methods to achieve invariance to contrast, illumination or ambient temperature. The descriptors are analyzed and modified to improve their performance in a detection problem, where potential candidates are searched for in full size images. Finally, a method for tracking the detected pedestrians is proposed to reduce the number of miss-detections that occurred at earlier stages of the algorithm. --La detección de personas en imágenes es un campo de investigación relativamente nuevo, pero que ha tenido una amplia acogida. Las aplicaciones son múltiples, tales como auto-etiquetado de grandes bases de datos, sistemas de seguridad y detección de peatones en sistemas inteligentes de transporte. Dentro de este último, la detección de peatones desde un vehículo móvil tiene como objetivo detectar la presencia de personas en la trayectoria del vehículo. EL fin último es evitar una colisión entre ambos. Esta tesis se enmarca en los sistemas avanzados de ayuda a la conducción; sistemas de seguridad pasivos, que advierten al conductor de condiciones que pueden ser adversas. En esta tesis se presenta un módulo de ayuda a la conducción destinado a advertir de la presencia de peatones, mediante el uso de visión por computador en imágenes térmicas. Este tipo de sensores resultan especialmente útiles en condiciones de baja iluminación. El documento se divide siguiendo las partes habituales de una sistema de detección de peatones: desarrollo de descriptores que defina la apariencia de las personas en este tipo de imágenes, la aplicación de estos en imágenes de tamano completo y el seguimiento temporal de los peatones encontrados. Como parte del trabajo desarrollado en esta tesis se presenta una base de datos de peatones en el espectro infrarrojo lejano. Esta base de datos ha sido utilizada para desarrollar una evaluación de sistemas de detección de peatones, así como para el desarrollo de nuevos descriptores. Estos integran técnicas para la descripción sistemática de la forma del peatón, así como métodos para la invariancia al contraste, la iluminación o la temperatura externa. Los descriptores son analizados y modificados para mejorar su rendimiento en un problema de detección, donde se buscan posibles candidatos en una imagen de tamano completo. Finalmente, se propone una método de seguimiento de los peatones detectados para reducir el número de fallos que se hayan producido etapas anteriores del algoritmo