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FlashDetection
- 本程式目的在于侦测闪光(flash)，以提供scene detection一个优化作业，避免scene detection因为闪光而造成的误判。
centralizing
- 自动指纹识别系统的工作模式可以分为两类:验证模式和辨识模式。验证就是通过把一个现场采集到的指纹与一个以登记的指纹进行一对一得比对，来确认身份的过程。作为验证的前提条件，验证者的指纹必须在指纹库中已经注册。-Automated Fingerprint Identification System can be divided into two categories: model verification and identification modes. Authentication is thro
bundler-v0.3-source.tar
- Bundler is a structure-from-motion system for unordered image collections (for instance, images from the Internet). Bundler takes a set of images, image features, and image matches as input, and produces a 3D reconstruction of the camera and (s
Motion_Detection
- 一个人在视频场景里面走动，可以对其运动轨迹进行捕捉-In the video scene inside of a person walking, you can track its movement to capture
AVehicleContourbasedMethodforOcclusion
- 摘要：在交通场景下进行多目标跟踪时，如何正确检测出车辆间的相互遮挡是影响车辆跟踪结果的关键。针对问题，运用投 影理论分析交通场景的三维几何投影特征．用长方体投影轮廓模型对车辆进行建模，重构其乏维投影轮廓，以进行遮挡的检 测和分离。与以往的方法相比，它在估计出的车辆外形轮廓基础t-进行遮挡检测，不需要匹配操作，计算量较小，并能解决 基于匹配的方法无法对付的初始遮挡问题。用实验验证了该算法的有效性。-In multi—object tracking of traf氍c scene。how
ImageSeg
- 提出了一种适用于视频监控场景的基于物理反射模型的阈值分割算法，该算法主要解决背景颜色识别受 光强非均匀分布、高光效应影响的问题．算法步骤主要包括：首先基于Phong反射模型推导出漫反射分量颜色不 变性并根据这一判定条件计算得到漫反射分量系数；其次，利用微分法则实现对模型镜面反射分量系数和镜面 反射强度指数的估计；最后，根据建立的物理反射模型实现背景阚值分割．大量实验分析结果表明，文中提出的 算法利用视频监控的物理反射模型和大量统计信息，能够更好地解决受光强非均匀分布和高光效应影响
sequences
- standard test image face and scene
VideoScene
- 最近几年国内外研究视频场景。风格的十几篇文章，综述了最近几年场景分割的特点，都是比较不错的文章，值得做视频研究的人学习-Of video scenes at home and abroad in recent years. Style more than a dozen articles, reviews the characteristics of scene segmentation in recent years, are quite good article, it is worth d
surf
- 自然场景图像局部不变特征检测与描述,surf算法的图像匹配-Natural scene image detection and descr iption of local invariant features, surf image matching algorithm
Detecting-Text-in-Natural-Scenes
- 关于场景文本定位、分割、识别的一些国外论文，根据图像的亮度和色彩信息、轮廓宽度变换等算法。-A Novel Algorithm for Text Detection and Localization in Natural Scene Images，Detecting Text in Natural Scenes with Stroke Width Transform，Scene Text Extraction using Image Intensity and Color Information
SensorSystems
- It presents the lab work for university course Sensor Systems. On the work screen we generate some triangles, placing player-dot to scene and throw it to analize triangles.
Pattern-Matching-Alg
- 利用①相关匹配（Correlation Matching）、②基于Hausdorff距离匹配方法 及③考虑对场景图象距离变换（Distance Transform）的Hausdorff距离匹配方法,实现模板目标在场景图象中的定位-Use ① correlation matching (Correlation Matching), ② matching method based on Hausdorff distance and image of the scene ③ consider the
Scene-Classification
- 提供了三类场景“bedroom”、“CALsuburb”、“industrial”的样本特征集以及原始图像，分别用线性分类器、树状分类器、SVM分类器以及AdaBoost分类器对其进行区分。其中AdaBoost分类器有部分内容调用了Vezhnevets Alexander编写的源码-Provides three types of scenes " bedroom" , " CALsuburb" , " industrial" sample fea
rough-set
- 图像场景分类中视觉词包分类的应用与操作代码-Review of the bag-of-visual-words models in image scene classification
kalman-object-detection
- 用卡尔曼滤波来检测视频中的小运动目标，例子为检测一个运动场景中的乒乓球-Using kalman filter to detect small moving targets in video, example for detecting a movement in the scene of table tennis
strokes
- 外国人写的基于笔画特征的字文本定位,特别可以检测自然场景中的一些文本-Foreigners handwriting text location based stroke features, in particular a natural scene can detect some text
p835-snavely
- abstract: We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the vie
siftDemoV4
- SIFT算法是近几年才提出的一种新型局部特征描述子。SIFT特征独特性好，信息量丰富，对尺度缩放，旋转，视角变化，遮挡，噪声和亮度变化等大部分干扰都具有很好的鲁棒性，它在在场景匹配，目标识别等领域已获得成功利用-SIFT algorithm is a new feature in recent years that it has made a partial descr iption of the child. SIFT features unique and good, rich amount
TSDFNS
- 自然场景中的字符识别，使用笔画宽度变换，效果不是很理想，但有助于初学者进一步学习字符识别-Natural scene character recognition, using stroke width conversion, the effect is not very satisfactory, but further help beginners learn character recognition
code
- 基于词袋的场景分类，分类器采用SVM和最近邻，需要vlfeat和图片见http://cs.brown.edu/courses/csci1430/proj3/-Based on word bag scene classification, SVM classifier using the nearest neighbor and need vlfeat and pictures see http://cs.brown.edu/courses/csci1430/proj3/