Learning Object-Independent Modes of Variation with Feature Flow Fields

We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that object. We develop the framework in the context of a well-known example: analyzing the modes of spatial deformations of a scene under camera movement. Our method learns a close approximation to the standard affine deformations that are expected from the geometry of the situation, and does so in a completely unsupervised (i.e. ignorant of the geometry of the situation) fashion. We stress that it is learning a "parameterization", not just the parameter values, of the data. We then demonstrate how we have used the same framework to derive a novel data-driven model of joint color change in images due to common lighting variations. The model is superior to previous models of color change in describing non-linear color changes due to lighting

Light Field Morphable Models

Statistical shape and texture appearance models are powerful image representations, but previously had been restricted to 2D or simple 3D shapes. In this paper we present a novel 3D morphable model based on image-based rendering techniques, which can represent complex lighting conditions, structures, and surfaces. We describe how to construct a manifold of the multi-view appearance of an object class using light fields and show how to match a 2D image of an object to a point on this manifold. In turn we use the reconstructed light field to render novel views of the object. Our technique overcomes the limitations of polygon based appearance models and uses light fields that are acquired in real-time

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ

Graphical Image Rendering: Modeling, Animation of Facial or Wild Images

In this comparative study, we intend to analyse different methodologies to perform 3-Dimensional modeling and printing, by using raw images as input without any supervision by a human. Since the input consists of only raw images, the foundation of the methods is finding symmetry in images. But the images that seem symmetric are not symmetric due to the perspective effect and utterance of other factors. The method uses factors like depth, albedo, point of view, and lighting from the input image to formulate 3D shapes. A 3D template model with feature points is created, and by deforming the 3D template model, a 3D model of the subject is then reconstructed from orthogonal photos. The number and locations of the proper amount of feature points are derived. Procrustes Analysis and Radial Basis Functions (RBFs) are used for the deformation. Images are then mapped onto the mesh following the deformations for realistic visualization. Characterization of the input image shows an asymmetric cause of shading, lighting, and albedo rendering the symmetry of images. The experiments show that using these methods can give exact 3D shapes of objects like human faces, cars, and cats

Automatic facial landmark labeling with minimal supervision.

Abstract Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, an

Object Detection using Dimensionality Reduction on Image Descriptors

The aim of object detection is to recognize objects in a visual scene. Performing reliable object detection is becoming increasingly important in the fields of computer vision and robotics. Various applications of object detection include video surveillance, traffic monitoring, digital libraries, navigation, human computer interaction, etc. The challenges involved with detecting real world objects include the multitude of colors, textures, sizes, and cluttered or complex backgrounds making objects difficult to detect. This thesis contributes to the exploration of various dimensionality reduction techniques on descriptors for establishing an object detection system that achieves the best trade-offs between performance and speed. Histogram of Oriented Gradients (HOG) and other histogram-based descriptors were used as an input to a Support Vector Machine (SVM) classifier to achieve good classification performance. Binary descriptors were considered as a computationally efficient alternative to HOG. It was determined that single local binary descriptors in combination with Support Vector Machine (SVM) classifier don\u27t work as well as histograms of features for object detection. Thus, histogram of binary descriptors features were explored as a viable alternative and the results were found to be comparable to those of the popular Histogram of Oriented Gradients descriptor. Histogram-based descriptors can be high dimensional and working with large amounts of data can be computationally expensive and slow. Thus, various dimensionality reduction techniques were considered, such as principal component analysis (PCA), which is the most widely used technique, random projections, which is data independent and fast to compute, unsupervised locality preserving projections (LPP), and supervised locality preserving projections (SLPP), which incorporate non-linear reduction techniques. The classification system was tested on eye detection as well as different object classes. The eye database was created using BioID and FERET databases. Additionally, the CalTech-101 data set, which has 101 object categories, was used to evaluate the system. The results showed that the reduced-dimensionality descriptors based on SLPP gave improved classification performance with fewer computations

Spectral control of viscous alignment for deformation invariant image matching

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 55-57).We present a new approach to deformation invariant image matching. Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to "turbulence." The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising.by Christopher Minzer Yang.M.Eng

Criação de modelos faciais 3D através de fotografias

Nos dias de hoje, a qualidade da semelhança entre as personagens em videojogos e o seu correspondente em carne e osso está cada vez mais elevada. A necessidade de uma modelação rápida, eficaz e económica é então cada vez mais requisitada. A solução que melhor conjuga essas 3 qualidades é sem dúvida a modelação facial através de fotografias. O primeiro artigo publicado nesta área foi em 1999, e têm sido feitas muitas melhorias aos sistemas desenvolvidos, no entanto, ainda existem muitas falhas. Falhas quer em termos de qualidade, semelhança e tempo. Com este projeto de dissertação pretendia-se então combater essas lacunas desenvolvendo uma ferramenta capaz de criar modelos faciais através de fotografias e posterior alteração de diversos parâmetros faciais. Os objetivos foram maioritariamente cumpridos, apresentando-se então uma biblioteca capaz de criar modelos 3D a partir de uma fotografia com um rosto, e alterar expressões faciais no mesmo, bem como manipular visualmente o modelo e gravá-lo para um ficheiro. Os utilizadores que testaram a aplicação gráfica apreciaram a sua rapidez na criação no modelo, no entanto não se mostraram tão satisfeitos relativamente à quantidade de ferramentas disponíveis para a sua edição.Nowadays, the similarity quality between video game characters and their real-life counterpart is getting higher and higher. The demand for fast, efficient and cheap 3D modeling is being highly required. The solution that best combines these aspects is undoubtedly the modeling through the use of face pictures. The first article published in this area was in 1999, and many improvements have been made ever since, but there were still many flaws to cover in those systems. The flaws include quality issues, low similarity, and high execution time. Therefore, with this work, it’s intended to combat those flaws, by creating a framework capable of creating such 3D models and also adjust various facial parameters. The majority of the objectives drawn were accomplished, presenting a framework capable of creating 3D models by using a facial picture, and changing their facial expressions, as well has visually manipulating the 3D model and writing it to a file. The users that tested the graphical application appreciated the fast creation of the model, but were not so convinced by the number of editing tools at their disposal