Multi-view 3D data acquisition using a single uncoded light pattern

This work is part of the project ’3D-Head’ funded by the Malta Council for Science and Technology under Research Grant No. RTDI-2004-034.This research concerns the acquisition of 3-dimensional data from images for the purpose of modeling a person's head. This paper proposes an approach for acquiring the 3-dimensional reconstruction using a multiple stereo camera vision platform and a combination of passive and active lighting techniques. The proposed one-shot active lighting method projects a single, binary dot pattern, hence ensuring the suitability of the method to reconstruct dynamic scenes. Contrary to the conventional spatial neighborhood coding techniques, this approach matches corresponding spots between image pairs by exploiting solely the redundant data available in the multiple camera images. This produces an initial, sparse reconstruction, which is then used to guide a passive lighting technique to obtain a dense 3-dimensional representation of the object of interest. The results obtained reveal the robustness of the projected pattern and the spot matching algorithm, and a decrease in the number of false matches in the 3-dimensional dense reconstructions, particularly in smooth and textureless regions on the human face.peer-reviewe

Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences

This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into Hilbert spaces through two types of Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding, but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification and texture categorization

Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms

Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and/or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem. Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves

A fast and robust hand-driven 3D mouse

The development of new interaction paradigms requires a natural interaction. This means that people should be able to interact with technology with the same models used to interact with everyday real life, that is through gestures, expressions, voice. Following this idea, in this paper we propose a non intrusive vision based tracking system able to capture hand motion and simple hand gestures. The proposed device allows to use the hand as a "natural" 3D mouse, where the forefinger tip or the palm centre are used to identify a 3D marker and the hand gesture can be used to simulate the mouse buttons. The approach is based on a monoscopic tracking algorithm which is computationally fast and robust against noise and cluttered backgrounds. Two image streams are processed in parallel exploiting multi-core architectures, and their results are combined to obtain a constrained stereoscopic problem. The system has been implemented and thoroughly tested in an experimental environment where the 3D hand mouse has been used to interact with objects in a virtual reality application. We also provide results about the performances of the tracker, which demonstrate precision and robustness of the proposed syste

Exploiting Cross Domain Relationships for Target Recognition

Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems. First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view is the key point. In this work, reconstructable paths between different views are built to mirror labeled actions from one source view into one another target view for learning an adaptable classifier. The path learning takes advantage of the joint dictionary learning techniques with exploiting hidden information in the seemingly useless samples, making the recognition performance robust and effective. Second, the problem of person re-identification is studied, which tries to match pedestrian images in non-overlapping camera views based on appearance features. In this work, we propose to learn a random kernel forest to discriminatively assign a specific distance metric to each pair of local patches from the two images in matching. The forest is composed by multiple decision trees, which are designed to partition the overall space of local patch-pairs into substantial subspaces, where a simple but effective local metric kernel can be defined to minimize the distance of true matches. Third, the problem of multi-event detection and recognition in smart grid is studied. The signal of multi-event might not be a straightforward combination of some single-event signals because of the correlation among devices. In this work, a concept of ``root-pattern\u27\u27 is proposed that can be extracted from a collection of single-event signals, but also transferable to analyse the constituent components of multi-cascading-event signals based on an over-complete dictionary, which is designed according to the ``root-patterns\u27\u27 with temporal information subtly embedded. The correctness and effectiveness of the proposed approaches have been evaluated by extensive experiments

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Computational Aesthetics for Fashion

The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately

A Sparse Multi-Scale Algorithm for Dense Optimal Transport

Discrete optimal transport solvers do not scale well on dense large problems since they do not explicitly exploit the geometric structure of the cost function. In analogy to continuous optimal transport we provide a framework to verify global optimality of a discrete transport plan locally. This allows construction of an algorithm to solve large dense problems by considering a sequence of sparse problems instead. The algorithm lends itself to being combined with a hierarchical multi-scale scheme. Any existing discrete solver can be used as internal black-box.Several cost functions, including the noisy squared Euclidean distance, are explicitly detailed. We observe a significant reduction of run-time and memory requirements.Comment: Published "online first" in Journal of Mathematical Imaging and Vision, see DO