Nonparametric Bayesian Texture Learning and Synthesis

We present a nonparametric Bayesian method for texture learning and synthesis. A texture image is represented by a 2D Hidden Markov Model (2DHMM) where the hidden states correspond to the cluster labeling of textons and the transition matrix encodes their spatial layout (the compatibility between adjacent textons). The 2DHMM is coupled with the Hierarchical Dirichlet process (HDP) which allows the number of textons and the complexity of transition matrix grow as the input texture becomes irregular. The HDP makes use of Dirichlet process prior which favors regular textures by penalizing the model complexity. This framework (HDP-2DHMM) learns the texton vocabulary and their spatial layout jointly and automatically. The HDP-2DHMM results in a compact representation of textures which allows fast texture synthesis with comparable rendering quality over the state-of-the-art patch-based rendering methods. We also show that the HDP- 2DHMM can be applied to perform image segmentation and synthesis. The preliminary results suggest that HDP-2DHMM is generally useful for further applications in low-level vision problems.United States. National Geospatial-Intelligence Agency (NEGI-1582-04- 0004)United States. Office of Naval Research (MURI Grant N00014-06-1-0734)United States. Advanced Research Projects Agency-Energy (VACE-II)Microsoft ResearchGoogle (Firm

Scale detection via keypoint density maps in regular or near-regular textures

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture ‘‘scale’’ as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A ‘‘mode’’ vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging

Periodic Pattern Detection for Real-Time Application

Abstract. Digital video stabilization approaches typically degrade their performances in presence of periodic patterns. Any kind of matching between consecutive frames is not usually able to work in presence of these kind of signals: the motion estimation engine is deceived and its performances degrade abruptly. In this paper we propose a fast fuzzy classifier able to recognize periodic and aperiodic pattern in the images that takes into account the peculiarities of digital video stabilization. Finally, the proposed classifier can be used as a filtering module in a block based video stabilization approach. Key words: Video Stabilization, periodic pattern, fuzzy classifier

Discovering Regularity in Point Clouds of Urban Scenes

Despite the apparent chaos of the urban environment, cities are actually replete with regularity. From the grid of streets laid out over the earth, to the lattice of windows thrown up into the sky, periodic regularity abounds in the urban scene. Just as salient, though less uniform, are the self-similar branching patterns of trees and vegetation that line streets and fill parks. We propose novel methods for discovering these regularities in 3D range scans acquired by a time-of-flight laser sensor. The applications of this regularity information are broad, and we present two original algorithms. The first exploits the efficiency of the Fourier transform for the real-time detection of periodicity in building facades. Periodic regularity is discovered online by doing a plane sweep across the scene and analyzing the frequency space of each column in the sweep. The simplicity and online nature of this algorithm allow it to be embedded in scanner hardware, making periodicity detection a built-in feature of future 3D cameras. We demonstrate the usefulness of periodicity in view registration, compression, segmentation, and facade reconstruction. The second algorithm leverages the hierarchical decomposition and locality in space of the wavelet transform to find stochastic parameters for procedural models that succinctly describe vegetation. These procedural models facilitate the generation of virtual worlds for architecture, gaming, and augmented reality. The self-similarity of vegetation can be inferred using multi-resolution analysis to discover the underlying branching patterns. We present a unified framework of these tools, enabling the modeling, transmission, and compression of high-resolution, accurate, and immersive 3D images

Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging

Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of these functions are extremely concise, but their implementation in real code is tedious, especially when optimized for real-time performance on modern GPUs in interactive applications. In this work, we propose a new language, Opt (available under http://optlang.org), for writing these objective functions over image- or graph-structured unknowns concisely and at a high level. Our compiler automatically transforms these specifications into state-of-the-art GPU solvers based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate different variations of the solver, so users can easily explore tradeoffs in numerical precision, matrix-free methods, and solver approaches. In our results, we implement a variety of real-world graphics and vision applications. Their energy functions are expressible in tens of lines of code, and produce highly-optimized GPU solver implementations. These solver have performance competitive with the best published hand-tuned, application-specific GPU solvers, and orders of magnitude beyond a general-purpose auto-generated solver

Unsupervised detection and localization of structural textures using projection profiles

Cataloged from PDF version of article.The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.(C)2010 Elsevier Ltd. All rights reserved