Ming-Ming Cheng(程明明)

程明明，男，1985年9月出生于陕西咸阳。2007年从西安电子科技大学计算机系获得学士学位。现就读于清华大学计算机系，在导师胡事民教授的指导下攻读博士学位。我的研究兴趣包括: 视频图像处理, 计算机视觉和机器学习。我的工作受到了Google PhD Fellowship、IBM PhD Fellowship、和教育部博士研究生学术新人奖的资助。我目前在清华大学图形学与几何处理实验室学习。

联系电话: 86-10-62797001-807 (办公室)
联系地址: 北京市，清华大学FIT楼3-523，邮编：100084.

摘要: 可靠的视觉显著性估计使得即使在没有先验知识的情况下也可以对图像进行适当的处理。 因此，视觉显著性估计仍然是许多计算机视觉任务的重要步骤， 其中包括：图像分割、目标识别、自适应压缩、内容敏感图像编辑、图像检索等。 我们提出了一种基于区域对比度的视觉显著性区域检测算法。 该方法同时考虑全局对比度和空间相干性。 该算法简单、高效，并且可以产生全分辨率的显著性图。 在国际上现有最大的公开测试集上，该方法的检测结果具有更高的精度和更好的召回率， 明显优于现有视觉显著性检测方法。 我们进一步提出了基于该显著性图的图像中视觉显著性区域自动分割方法，该方法在国际上现有最大公开测试集上的结果(正确率=90%，召回率=90%) 也明显高于现有其它方法的最好结果(正确率=75%，召回率=83%)。[Project page] [C++] [Bib]

摘要: 基于随机游走模型的图像分割方法是图像分割的主流方法之一。关于该方法的传统研究认为：随机游走模型的分割结果具有连通性。本文通过研究与随机游走模型等价电路网络，提出了关于随机游走模型的几个新性质，同时通过理论证明和实际例子否定了传统观点。[Matlab Code][Pdf][Bib]

Index Terms: Image segmentation, random walk, Laplace’s equation, counterexample, connectedness.

摘要：重复元素大量存在于人造和自然场景之中。受遮挡、部分缺失、物体间形变、光照变化等影响，在编辑这些图片过程中保持重复元素及其相互关系并非易事。手工保持重复元素之间的关系是非常耗时而且容易出错的。我们提出了一中新的算法框架：利用简单的用户交互来辅助检测和提取这些重复元素。该检测方法基于一中新的轮廓带图的方法，能够鲁棒的提取重复元素并获得它们之间的遮挡关系。进一步采用拓扑排序，可以建立这些相互遮挡重复实例之间的局部深度关系。通过分析其它未被遮挡的重复实例，我们可以对被遮挡物体进行补全。这些提取出的重复实例可以进一步被用于多种无缝的高层次图像编辑应用中。我们通过很多的不同复杂度的实例，演示了该算法框架的多种应用。这些应用包括：图像重排、编辑传播、变形传递和实例替换。 [Project page] [Bib]

Index Terms: image editing, shape-aware manipulation, edit propagation.

Abstract: We present a system that composes a realistic picture from a user provided sketch with text labels. The composed picture is generated by seamlessly stitching several photographs automatically searched from internet according to the sketch and its text labels. While on line image search generates noisy results, our system can automat ically select suitable photographs to generate a high quality com position. To achieve this, we first design a filtering scheme to exclude undesirable images from searched results. Then we propose a novel image blending algorithm for seamless image composition. Our blending algorithm returns a numeric score for each blending, which is used to optimize the combination of searched images. Several vivid results are generated in the experiments. We also perform a user study to demonstrate the advantages of our system. [Project page] [Bib] [PPT]

Note: I'm in charge of the image filter scheme in this work.

Abstract:我们提出一个基于共性能量的内容敏感图像缩放方法来解决任意尺寸图像缩放问题。通过把几何处理中常用的共形能量引入到图像处理领域，本方法可以方便的约束图像缩放前后重要物体的局部和整体形状在变形前后尽量相似。本算法的特点在于：不同于传统方法只能保持图像缩放中的局部相似性，本方法可以保持图像中重要物体变形前后的整体相似性。而且本方法可以方便的计算全局最优的闭形式解。此外本方法比现有最快速的内容敏感图像缩放方法的运行速度提升了数十倍。[Pdf] [C++] [PPT] [Video] [Supplemental] [Bib] [Code]

Abstract:We propose a novel method for curve structure extraction of cartoon images. Our method handles two types of cartoon curves, decorative curves and boundary curves, in a uniform way. The method consists of two steps. First, we calculate curve points by applying non-maximal suppress on secondary derivative of cartoon images. Second, these curve points are linked together to form structure curves while unreliable curves are removed away. Compared to curve structure extraction algorithm proposed by Steger, the number of curves generated by our algorithm is only 19% of Steger’s on average, with better curve quality. Furthermore, more accurate curve position can be obtained by our method. [Doc] [Pdf] [C++ code]

Abstract:Public lighting consumes 30% of the amount of electric power in the entire lighting system and seems to be a growing tendency of bottleneck in energy conservation. In this paper, Starswave, an intelligent street lamp control system, is present to address this problem. It incorporates LEDs to replace the traditional lamps, utilizes the sensors, and controls light switches according to the arrival and departure of vehicles in an intelligent way. In addition, it collects and analyzes the related information from various sources, such as Internet and weather broadcast to improve the management and maintenance of lamps. This paper first describes the design of system components and their implementation, and then provides an algorithm description in details. Of particular concern is the way in which both components and algorithms work together. Further, an extensive experiment study was conducted and the results show that Starswave achieves both high system performance and energy conservation, and outcomes the traditional public lighting facilities significantly.[Pdf][PPT][FLASH]

MiM Cheng, WB Li, L Qiu, SF Zhang, D Zhou. Starswave: an Intelligent Street Lamp System, in Windows Embedded Student Challenge, 2006. (The third place in world final)