Segmentation by Continuous Latent Semantic Analysis for Multi-structure Model Fitting

International Journal of Computer Vision - In this paper, we propose a novel continuous latent semantic analysis fitting method, to efficiently and effectively estimate the parameters of model...     

A consensus-based method for tracking: Modelling background scenario and foreground appearance

Modelling of the background (“uninteresting parts of the scene”), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of the background, per pixel. SACON exploits both color and motion information to detect foreground objects. SACON can deal with complex background scenarios including nonstationary scenes (such as moving trees, rain, and fountains), moved/inserted background objects, slowly moving foreground objects, illumination changes etc.However, it is one thing to detect objects that are not likely to be part of the background; it is another task to track those objects. Sample consensus is again utilized to model the appearance of foreground objects to facilitate tracking. This appearance model is employed to segment and track people through occlusions. Experimental results from several video sequences validate the effectiveness of the proposed method.     

Superpixel-Guided Two-View Deterministic Geometric Model Fitting

International Journal of Computer Vision - Geometric model fitting is a fundamental research topic in computer vision and it aims to fit and segment multiple-structure data. In this paper, we...     

一种基于锁相环梳状谱发生器的设计

针对传统模拟梳状谱发生器难以实现高平坦度、窄间距、灵活操控的缺陷,文中提出了一种基于锁相环机理实现梳状信号的新方式。该方法利用锁相环反馈回路的N分频器对压控振荡器输出的高频信号进行分频以得到脉冲宽度窄的脉冲信号,然后再令其通过滤波器得到满足目标带宽的高平坦度梳状谱信号。通过对分频器进行灵活编程控制可以得到不同间距的谱线,且不需要修改硬件电路设计。实验结果表明,这种基于锁相环机理及的梳状谱发生器所产生的谱线具备信号带宽大、幅度一致性高、间距可调、小型化、低功耗等优点。     

Visual tracking via weakly supervised learning from multiple imperfect oracles

Notwithstanding many years of progress, visual tracking is still a difficult but important problem. Since most top-performing tracking methods have their strengths and weaknesses and are suited for handling only a certain type of variation, one of the next challenges is to integrate all these methods and address the problem of long-term persistent tracking in ever-changing environments. Towards this goal, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., different trackers). These trackers naturally have intrinsic diversity due to their different design strategies, and we propose a probabilistic method to simultaneously infer the most likely object position by considering the outputs of all trackers, and estimate the accuracy of each tracker. An online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and efficient. Consequently, the proposed method can avoid the pitfalls of purely single tracking methods and get reliably labeled samples to incrementally update each tracker (if it is an appearance-adaptive tracker) to capture the appearance changes. Extensive experiments on challenging video sequences demonstrate the robustness and effectiveness of the proposed method.     

Rapid hypothesis generation by combining residual sorting with local constraints

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.     

“低碳”理念下的城市滨水景观设计探析

“低碳经济”已经成为世界性的发展模式,而构成城市化发展重要内容的城市意象、景观规划设计该如何应对“低碳”理念,是景观学研究的焦点问题。本文对现代城镇滨水景观意象进行分析,探索低碳理念下景观设计的和谐方法,营造更加人性、生态的城镇景观形象,对和谐社会、和合人生具有重要意义。     

A Generalized Kernel Consensus-Based Robust Estimator

In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum Kernel Density Estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.     

MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation

In this paper, we propose a novel and highly robust estimator, called MDPE¹ (Maximum Density Power Estimator). This estimator applies nonparametric density estimation and density gradient estimation techniques in parametric estimation (model fitting). MDPE optimizes an objective function that measures more than just the size of the residuals. Both the density distribution of data points in residual space and the size of the residual corresponding to the local maximum of the density distribution, are considered as important characteristics in our objective function. MDPE can tolerate more than 85% outliers. Compared with several other recently proposed similar estimators, MDPE has a higher robustness to outliers and less error variance.We also present a new range image segmentation algorithm, based on a modified version of the MDPE (Quick-MDPE), and its performance is compared to several other segmentation methods. Segmentation requires more than a simple minded application of an estimator, no matter how good that estimator is: our segmentation algorithm overcomes several difficulties faced with applying a statistical estimator to this task.     

Effect of the chain‐transfer‐agent content on the emulsion polymerization process and adhesive properties of poly(n‐butyl acrylate‐co‐acrylic acid) latexes

The effect of water on regenerated silkworm silk fibers has been studied and compared with that of water on natural silkworm silk fibers. Regenerated fibers are spun from an N‐methylmorpholine‐N‐oxide (NMMO) fibroin solution through a wet‐spinning process, leading to fibers with two distinct tensile behaviors, labeled as brittle and ductile, respectively. Regenerated fibers show a significant contraction when immersed in water. Contraction increases further after drying. In contrast, natural silkworm silk fibers show a negligible contraction when submerged in water. Regenerated fibers tested in water are considerably more compliant than samples tested in air, though their stiffness and tensile strength are significantly reduced. It has been shown that the tensile properties of brittle regenerated fibers can be modified by a wet‐stretching process, which consists of deforming the fiber while immersed in water. Regenerated wet‐stretched fibers always show a ductile behavior independent from their initial tensile behavior. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008