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Description: 本程序是计算机视觉稠密匹配的程序，对两幅序列图像能够实现一个稠密视差图。-Computer Vision populated matching procedures, two image sequence to achieve a dense disparity map.
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Author: lhc2000 |
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Description: computing dense correspondence #
# (disparity map) between two images using graph cuts
This software implements stereo algorithms described in the following papers:
Vladimir Kolmogorov and Ramin Zabih
"Multi-Camera scene Reconstruction via Graph Cuts"
In: European Conference on Computer Vision, May 2002.
Vladimir Kolmogorov and Ramin Zabih
"Computing Visual Correspondence with Occlusions using Graph Cuts"
In: International Conference on Computer Vision, July 2001.
and
Yuri Boykov, Olga Veksler and Ramin Zabih
"Markov Random Fields with Efficient Approximations"
In: IEEE Computer Vision and Pattern Recognition Conference, June 1998.
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Author: newship@126.com |
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Description: 本程序是计算机视觉稠密匹配的程序，对两幅序列图像能够实现一个稠密视差图。-Computer Vision populated matching procedures, two image sequence to achieve a dense disparity map.
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Author: lhc2000 |
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Description: 一种基于最小割的稠密视差图恢复算法
摘要:针对经典最小割算法计算量大和适应性不足的问题,提出一种改进的基于网络最小割计算稠密深度图的全局优化方法。-Based on the minimum cut of a dense disparity map algorithm to restore
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Author: shuoshuo |
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Description: 本文提出了一种快速，可靠的立体匹配算法产生浓密视差图，通过使用快速互相关、矩形秩和3D最大表面技术粗到细计划。-This paper presents a fast and reliable stereo matching
algorithm which produces a dense disparity map
by using fast cross correlation, rectangular subregioning
(RSR) and 3D maximum-surface techniques in a
coarse-to-fine scheme.
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Author: swl |
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Description: 提出了一种用于物体外形非接触式提取的综合立体匹配方法。该方法综合了基于面积的立体匹配过程和基于特征的立体匹配过程。能充分利用它们各自的优点。 既能产生致密视差图。又能获得不连续处的准确匹配。 解决了物体外形非接触式提取中的关键性的问题实验结果以及实际应用都收到了很好的效果。-Proposed for object shape extraction of non-contact integrated stereo matching method. This method is an integrated area-based stereo matching process and feature-based stereo matching process. Able to make full use of their respective advantages. Both produce dense disparity map. Can be discontinuous at the exact match. Solve the non-contact object shape extraction of critical issues, as well as the practical application of experimental results have received very good results.
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Author: liaozw |
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Description: 机器人地图创建新算法，DP-SLAM源程序-Abstract—Probabilistic approaches have proved very successful
at addressing the basic problems of robot localization and
mapping and they have shown great promise on the combined
problem of simultaneous localization and mapping (SLAM). One
approach to SLAM assumes relatively sparse, relatively unambiguous
landmarks and builds a Kalman filter over landmark
positions. Other approaches assume dense sensor data which
individually are not very distinctive, such as those available from
a laser range finder. In earlier work, we presented an algorithm
called DP-SLAM, which provided a very accurate solution to the
latter case by efficiently maintaining a joint distribution over
robot maps and poses. The approach assumed an extremely
accurate laser range finder and a deterministic environment.
In this work we demonstrate an improved map representation
and laser penetration model, an improvement in the asymptotic
efficiency of the algorithm, and empirical results of loop closing
o
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Author: solon |
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Description: 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。
其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？
用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram
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Author: lipiji |
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Description: 《大话数据结构》主要内容包含：数据结构介绍、算法推导大O阶的方法；顺序结构与链式结构差异、栈与队列的应用；串的朴素模式匹配、KMP模式匹配算法；二叉树前中后序遍历、赫夫曼树及应用；图的深度、广度遍历；最小生成树两种算法、最短路径两种算法；拓扑排序与关键路径算法；折半查找、插值查找、斐波那契查找等静态查找；稠密索引、分块索引、倒排索引等索引技术；冒泡、选择、插入等简单排序；希尔、堆、归并、快速等改进排序-" Westward the data structure," the main contents include: Introduction of data structures, algorithms, Big O-order method is derived sequence differences in the structure and chain structure, the application stack and queue series of simple pattern matching, KMP pattern matching algorithm binary ago in post-order traversal, Huffman tree and applications map the depth, breadth traversal minimum spanning two algorithms, the shortest path of two algorithms topological sort and critical path algorithm binary search, interpolation search, Fibonacci search Find other static dense index, block index, inverted index and other indexing techniques bubble, selection, and other simple insertion sort Hill, heap, merge, quick sorting and other improvements
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Author: 陈雨 |
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Description: 很好的立体视觉生成深度图算法。你只要输入两张有重叠区域的图像，自动给你生成稠密深度图，让你的3D效果更好！-Good stereoscopic depth map generation algorithms. You just enter the two images overlap region, automatically give you generate dense depth map, 3D effects make you better!
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Author: hypthomas123 |
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Description: 出了一种恢复高质量稠密视差图的立体视觉合作算法．该算法采用基于形态学相似性的自适应加权方
法，迭代地进行局部邻域的自适应聚合和抑制放大，实现高效率和高质量稠密视差图计算．将该算法推广到三目摄像
机立体匹配系统中，通过重建摄像机坐标系实现图像校正，并根据连续性假设和唯一性假设，建立视差空间中的支持
关系和三目摄像机之间的抑制关系．实验结果表明，三目立体合作算法能够得到精确的场景视差映射，并可以实现多
基线方向的遮挡检测．该算法特别适用于由多个廉价摄像机组成的立体视觉系统，在几乎不增加软件和硬件资源的
情况下。就可以得到高质量的稠密视差图．-Out of a dense disparity map to restore quality stereoscopic cooperative algorithms. The algorithm based on adaptive weighted morphological similarity method iteratively local neighborhood adaptive aggregation and inhibition of amplification to achieve high efficiency and high quality dense disparity map calculation. The algorithm is extended to trinocular stereo matching system, by rebuilding the camera coordinate system for image correction, and assumptions based on continuity and uniqueness of the assumptions in establishing a supportive relationship disparity space and trinocular relationship between inhibition. Experimental results show that the algorithm can cooperate trinocular stereo scene accurate parallax map and can achieve more occlusion detection base line direction. The algorithm is particularly suitable for low-cost cameras consisting of a plurality of three-dimensional vision system, almost no increase in the software and the hardware resources. You can get high-quality
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Author: aaron |
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Description: “Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
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Author: happy |
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Description: 3D reconstruction using quasi dense method and depth map
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Author: imad |
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Description: 运用双目摄像头获取左右图像，得到致密性视差图-Get around using binocular camera images to obtain a dense disparity map
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Author: 李进 |
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Description:
基于dijkstra和广度搜索的加权有向图有必经点的点对点的最短路径算法，路径必须经过要求的必经点，且不成环。
该算法采用了一种自适应调整的方法，经过多次迭代，使解收敛。 但只是寻找了一个略优的可行解（每次迭代都基于贪婪算法寻找），不能保证最优解。而且如果图过于稀疏，因为收敛速度过快，可能导致问题无解（收敛于一个局部最优解，没有经过所有点）。 解决相对稠密的图（每个点的平均出入度4以上），表现良好。
备注：里面有一个QT的工程，可以直接打开（源码里没用QT的库）-Weighted Dijkstra and breadth first search based on a shortest path algorithm must point to point to the map, the path must pass through points, and not a ring.
This algorithm adopts an adaptive method to adjust, after several iterations, the convergence of solution. But only to find a slightly better feasible solution (each iteration is based on the greedy algorithm to find), can not guarantee the optimal solution. And if the graph is too sparse, because the convergence speed is too fast, it may lead to the problem of no solution (convergence to a local optimal solution, not after all points). To solve the relatively dense graphs (each entry point average above 4), good performance.
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Author: 凌凯 |
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Description: The analysis of four different WDM-PON architectures is based on the dimensioning (with real building/street distributions
based on Open Street Map) in a dense urban scenario. The dimensioning not only includes the required equipment
and its power consumption, but also the passive infrastructure layout in terms of fiber length, duct length and duct size.
Finally, the results of the reference scenario and the proposed scenarios will be compared and analyzed regarding the
sensitivity analysis to help deduct the appropriate conclusions.
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Author: ali |
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