Search - c 15.4

Search - c 15.4 - List

[Special Effects] Ccode2.tar DL : 0: c语言计算几何三角化 Ch1, Code 1.14 凸形外壳[2D] Ch3, Code 3.8 凸形外壳[3D] Ch4, Code 4.8 球 Chapter 4, Fig. 4.15 德劳内类型 Ch5, Code 5.2 ...See *English version.-\Computational Geometry in C\ the book s recipe Triangulate Chapter 1, Code 1.14 /tri Convex Hull[2D] Chapter 3, Code 3.8 /graham Convex Hull[3D] Chapter 4, Code 4.8 /chull sphere.c Chapter 4, Fig. 4.15 /sphere Delaunay Triang Chapter 5, Code 5.2 /dt SegSegInt Chapter 7, Code 7.2 /segseg Point-in-poly Chapter 7, Code 7.13 /inpoly Point-in-hedron Chapter 7, Code 7.15 /inhedron Int Conv Poly Chapter 7, Code 7.17 /convconv Mink Convolve Chapter 8, Code 8.5 /mink Arm Move Chapter 8, Code 8.7 /arm
Date : 2025-12-19 Size : 56kb User : XJ
[Special Effects] LowpassFiltering DL : 0: 本实验要求要求实现高斯低通滤波器的程序，并设定截至频率半径为15时得到如课本上图4.18图c的图。使低频通过而使高频衰减的滤波器成为低通滤波器，被低通滤波的图像比原始图像少一些尖锐的细节部分。本实验就是设计实现高斯低通滤波器。-Requirements of this experiment requires low-pass Gaussian filter procedure and set the frequency up to a radius of 15 textbooks, such as when the map of Figure c Figure 4.18. So that the adoption of high-frequency low-frequency attenuation of the filter as a low-pass filter, low pass filter by the image less than the original sharp image details. The realization of this experiment is to design low-pass Gaussian filter.
Date : 2025-12-19 Size : 69kb User : jhm
[Special Effects] CRL-2001-1 DL : 0: 这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second
Date : 2025-12-19 Size : 766kb User : lai