Closest Point Search in Lattices
標簽： Lattices Closest Search Point
上傳時間： 2017-07-03
上傳用戶：王者A
abel Tool Sample Requires: Visual Basic 6 and MapObjects 2.x Data: redlands.shp (Redlands sample data set from MO 2.x) Interactive Labeling Tool If the check box is checked, then the mouse down location will search for the Closest line, and label it with the street name. If the check box is not checked, then the mouse down will turn into a pan/zoom tool. There is a slider bar to control the search tolerance in screen pixels for the labeling.
標簽： MapObjects Requires Redlands redlands
上傳時間： 2013-12-17
上傳用戶：sunjet
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is Closest to the initial configuration.
標簽： model distributed associative Hopfield
上傳時間： 2015-06-17
上傳用戶：l254587896
ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the Closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information
標簽： points the minimize respect
上傳時間： 2014-01-02
上傳用戶：gyq
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the Closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： approach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the Closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
This directory includes matlab interface of the curvelet transform using usfft. Basic functions fdct_usfft.m -- forward curvelet transform afdct_usfft.m -- adjoint curvelet transform ifdct_usfft.m -- inverse curvelet transform fdct_usfft_param.m -- returns the location of each curvelet in phase-space Useful tools fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet coefficients fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns the curvelet which is Closest to a certain point on the image Demos fdct_usfft_demo_basic.m -- display the shape of a curvelet fdct_usfft_demo_recon.m -- partial reconstruction using curvelet fdct_usfft_demo_disp.m -- display all the curvelet coefficients of an image fdct_usfft_demo_denoise.m -- image denoising using curvelet
標簽： directory functions interface transform
上傳時間： 2016-08-31
上傳用戶：cooran
用prim算法實驗最小生成樹 本程序中用到函數adjg( )，此函數作用是通過接受輸入的點數和邊數，建立無向圖。函數prg( )用于計算并輸出無向圖的鄰接矩陣。函數prim( )則用PRIM算法來尋找無向圖的最小生成樹 定義了兩個數組lowcost[max],Closest[max]，若頂點k加入U中，則令lowcost[k]=0。 定義二維數組g[ ][ ]來建立無向圖的鄰接矩陣。
上傳時間： 2016-10-07
上傳用戶：tonyshao
給出一個非負小數，找出分子不超過M，分母不超過N的最簡分數或整數， 使其最接近給出的小數。如果這個分數不唯一，輸出‘TOO MANY’。 輸入文件格式(Closest.in) 第一行，M，N（1<=M,N<=10^9） 第二行，即小數R，(0<R 輸出文件格式(Closest.out) 僅一行，若解唯一輸出 分子 / 分母(整數K寫成K/1)，否則輸出TOO MANY 樣例輸入： 360 120 3.1415926536 樣例輸出： 355/113
標簽：
上傳時間： 2017-01-08
上傳用戶：iswlkje
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