Time filter

Source Type

Zhu S.-C.,University of California at Los Angeles | Zhu S.-C.,Lotus Hill Research Institute | Shi K.,University of California at Los Angeles | Si Z.,University of California at Los Angeles
Pattern Recognition Letters

Natural images have a vast amount of visual patterns distributed in a wide spectrum of subspaces of varying complexities and dimensions. Understanding the characteristics of these subspaces and their compositional structures is of fundamental importance for pattern modeling, learning and recognition. In this paper, we start with small image patches and define two types of atomic subspaces: explicit manifolds of low dimensions for structural primitives and implicit manifolds of high dimensions for stochastic textures. Then we present an information theoretical learning framework that derives common models for these manifolds through information projection, and study a manifold pursuit algorithm that clusters image patches into those atomic subspaces and ranks them according to their information gains. We further show how those atomic subspaces change over an image scaling process and how they are composed to form larger and more complex image patterns. Finally, we integrate the implicit and explicit manifolds to form a primal sketch model as a generic representation in early vision and to generate a hybrid image template representation for object category recognition in high level vision. The study of the mathematical structures in the image space sheds lights on some basic questions in human vision, such as atomic elements in visual perception, the perceptual metrics in various manifolds, and the perceptual transitions over image scales. This paper is based on the J.K. Aggarwal Prize lecture by the first author at the International Conference on Pattern Recognition, Tempa, FL. 2008. © 2009 Elsevier B.V. All rights reserved. Source

Lin L.,Sun Yat Sen University | Lin L.,Lotus Hill Research Institute | Wang X.,Sun Yat Sen University | Yang W.,Sun Yat Sen University | Lai J.,Sun Yat Sen University
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the switch variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus define the model with three layers: the leaf-nodes for detecting local contour fragments, the or-nodes specifying selection of leaf-nodes, and the root-node encoding the holistic distortion. In the training stage, for optimization of the And-Or tree learning, we extend the concave-convex procedure (CCCP) by embedding the structural clustering during the iterative learning steps. The inference of shape detection is consistent with the model optimization, which integrates the local testings via the leaf-nodes and or-nodes with the global verification via the root-node. The advantages of our approach are validated on the challenging shape databases (i.e., ETHZ and INRIA Horse) and summarized as follows. (1) The proposed method is able to accurately localize shape contours against unreliable edge detection and edge tracing. (2) The And-Or tree model enables us to well capture the intraclass variance. © 2012 IEEE. Source

Lin L.,Lotus Hill Research Institute | Lin L.,Sun Yat Sen University | Zeng K.,Lotus Hill Research Institute | Lv H.,Lotus Hill Research Institute | And 4 more authors.
NPAR Symposium on Non-Photorealistic Animation and Rendering

We present an interactive system that stylizes an input video into a painterly animation. The system consists of two phases. The first is an Video Parsing phase that extracts and labels semantic objects with different material properties (skin, hair, cloth, and so on) in the video, and then establishes robust correspondence between frames for discriminative image features inside each object. The second Painterly Rendering phase performs the stylization based on the video semantics and feature correspondence. Compared to the previous work, the proposed method advances painterly animation in three aspects: Firstly, we render artistic painterly styles using a rich set of example-based brush strokes. These strokes, placed in multiple layers and passes, are automatically selected according to the video semantics. Secondly, we warp brush strokes according to global object deformations, so that the strokes appear to be tightly attached to the object surfaces. Thirdly, we propose a series of novel teniques to reduce the scintillation effects. Results applying our system to several video clips show that it produces expressive oil painting animations. © 2010 ACM. Source

Xie Y.,Beijing Institute of Technology | Xie Y.,Lotus Hill Research Institute | Lin L.,Lotus Hill Research Institute | Lin L.,Sun Yat Sen University | Jia Y.,Beijing Institute of Technology
Proceedings - International Conference on Pattern Recognition

Compared to the traditional tracking with fixed cameras, the PTZ-camera-based tracking is more challenging due to (i) lacking of reliable background modeling and subtraction; (ii) the appearance and scale of target changing suddenly and drastically. Tackling these problems, this paper proposes a novel tracking algorithm using patch-based object models and demonstrates its advantages with the PTZ-camera in the application of visual surveillance. In our method, the target model is learned and represented by a set of feature patches whose discriminative power is higher than others. The target model is matched and evaluated by both appearance and motion consistency measurements. The homography between frames is also calculated for scale adaptation. The experiment on several surveillance videos shows that our method outperforms the state-of-arts approaches. © 2010 IEEE. Source

Lin L.,Sun Yat Sen University | Lin L.,Huazhong University of Science and Technology | Liu X.,Huazhong University of Science and Technology | Liu X.,Lotus Hill Research Institute | And 4 more authors.
Pattern Recognition

In this paper, we present a framework for object categorization via sketch graphs that incorporate shape and structure information. In this framework, we integrate the learnable And-Or graph model, a hierarchical structure that combines the reconfigurability of a stochastic context free grammar (SCFG) with the constraints of a Markov random field (MRF). Considering the computation efficiency, we generalize instances from the And-Or graph models and perform a set of sequential tests for cascaded object categorization, rather than directly inferring with the And-Or graph models. We study 33 categories, each consisting of a small data set of 30 instances, and 30 additional templates with varied appearance are generalized from the learned And-Or graph model. These samples better span the appearance space and form an augmented training set ΩT of 1980 (60×33) training templates. To perform recognition on a testing image, we use a set of sequential tests to project ΩT into different representation spaces to narrow the number of candidate matches in ΩT. We use graphlets (structural elements), as our local features and model ΩT at each stage using histograms of graphlets over categories, histograms of graphlets over object instances, histograms of pairs of graphlets over objects, and shape context. Each test is increasingly computationally expensive, and by the end of the cascade we have a small candidate set remaining to use with our most powerful test, a top-down graph matching algorithm. We apply the proposed approach on the challenging public dataset including 33 object categories, and achieve state-of-the-art performance. © 2012 Elsevier Ltd. Source

Discover hidden collaborations