Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page: http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm

Contributions on Automatic Recognition of Faces using Local Texture Features

Uno de los temas más destacados del área de visión artifical se deriva del análisis facial automático. En particular, la detección precisa de caras humanas y el análisis biométrico de las mismas son problemas que han generado especial interés debido a la gran cantidad de aplicaciones que actualmente hacen uso de estos mecnismos. En esta Tesis Doctoral se analizan por separado los problemas relacionados con detección precisa de caras basada en la localización de los ojos y el reconomcimiento facial a partir de la extracción de características locales de textura. Los algoritmos desarrollados abordan el problema de la extracción de la identidad a partir de una imagen de cara ( en vista frontal o semi-frontal), para escenarios parcialmente controlados. El objetivo es desarrollar algoritmos robustos y que puedan incorpararse fácilmente a aplicaciones reales, tales como seguridad avanzada en banca o la definición de estrategias comerciales aplicadas al sector de retail. Respecto a la extracción de texturas locales, se ha realizado un análisis exhaustivo de los descriptores más extendidos; se ha puesto especial énfasis en el estudio de los Histogramas de Grandientes Orientados (HOG features). En representaciones normalizadas de la cara, estos descriptores ofrecen información discriminativa de los elementos faciales (ojos, boca, etc.), siendo robustas a variaciones en la iluminación y pequeños desplazamientos. Se han elegido diferentes algoritmos de clasificación para realizar la detección y el reconocimiento de caras, todos basados en una estrategia de sistemas supervisados. En particular, para la localización de ojos se ha utilizado clasificadores boosting y Máquinas de Soporte Vectorial (SVM) sobre descriptores HOG. En el caso de reconocimiento de caras, se ha desarrollado un nuevo algoritmo, HOG-EBGM (HOG sobre Elastic Bunch Graph Matching). Dada la imagen de una cara, el esquema seguido por este algoritmo se puede resumir en pocos pasos: en una primera etapa se extMonzó Ferrer, D. (2012). Contributions on Automatic Recognition of Faces using Local Texture Features [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16698Palanci

A Watch-List Based Classification System

Watch-list-based classification and verification is advantageous in a variety of surveillance applications. In this thesis, we present an approach for verifying if a query image lies in a predefined set of target samples (the watch-list) or not. This approach is particularly useful at identifying a small set of target subjects and therefore can render high levels of accuracy. Further, this approach can also be extended to identify the query image exactly out of the target samples. The three- stages approach proposed here consists of using a combination of color and texture features to represent the image and further using, Kernel Partial Least Squares for dimensionality reduction followed by a classifier. This approach provides improved accuracy as shown by experiments on two datasets

Semantically Informed Multiview Surface Refinement

We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated images. Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D. In the semantic segmentation step, the labels on the mesh are updated with MRF inference, such that they are compatible with the semantic segmentations in the input images. Also, this step includes prior assumptions about the surface shape of different semantic classes. The priors induce a tight coupling, where semantic information influences the shape update and vice versa. Specifically, we introduce priors that favor (i) adaptive smoothing, depending on the class label; (ii) straightness of class boundaries; and (iii) semantic labels that are consistent with the surface orientation. The novel mesh-based reconstruction is evaluated in a series of experiments with real and synthetic data. We compare both to state-of-the-art, voxel-based semantic 3D reconstruction, and to purely geometric mesh refinement, and demonstrate that the proposed scheme yields improved 3D geometry as well as an improved semantic segmentation