UVa 10004 Bicoloring (DFS&二分图)

本文介绍了一种基于邻接矩阵的二分图判定算法,并提供了一个完整的C++实现示例。该算法通过深度优先搜索(DFS)来判断一个给定的强连通图是否可以被二染色。

10004 - Bicoloring

Time limit: 3.000 seconds

http://uva.onlinejudge.org/index.php?option=com_onlinejudge&Itemid=8&category=24&page=show_problem&problem=945

In 1976 the ``Four Color Map Theorem" was proven with the assistance of a computer. This theorem states that every map can be colored using only four colors, in such a way that no region is colored using the same color as a neighbor region.

Here you are asked to solve a simpler similar problem. You have to decide whether a given arbitrary connected graph can be bicolored. That is, if one can assign colors (from a palette of two) to the nodes in such a way that no two adjacent nodes have the same color. To simplify the problem you can assume:

  • no node will have an edge to itself.
  • the graph is nondirected. That is, if a node a is said to be connected to a node b, then you must assume that b is connected to a.
  • the graph will be strongly connected. That is, there will be at least one path from any node to any other node.

Input 

The input consists of several test cases. Each test case starts with a line containing the number  n  (  1 <  n < 200) of different nodes. The second line contains the number of edges  l . After this,  l  lines will follow, each containing two numbers that specify an edge between the two nodes that they represent. A node in the graph will be labeled using a number  a  (  $0 \le a < n$ ).

An input with n = 0 will mark the end of the input and is not to be processed.

Output 

You have to decide whether the input graph can be bicolored or not, and print it as shown below.

Sample Input 

3
3
0 1
1 2
2 0
9
8
0 1
0 2
0 3
0 4
0 5
0 6
0 7
0 8
0

Sample Output 

NOT BICOLORABLE.
BICOLORABLE.

简单的二分图判定问题,用邻接矩阵算了。


完整代码:

/*0.025s*/

#include<cstdio>
#include<cstring>

int a[201][201], vist[202], t[202];
int n, m, p;

int dfs()
{
	for (int i = 0; i < n; i++)
		for (int j = 0; j < n; j++)
			if (a[i][j])
			{
				if (t[i] && t[j] && vist[i] == vist[j])
					return 0;
				else if (t[i] && !t[j])
				{
					vist[j] = -vist[i];
					t[j] = 1;
				}
				else if (!t[i] && t[j])
				{
					vist[i] = -vist[j];
					t[i] = 1;
				}
			}
	return 1;
}

int main(void)
{
	while (scanf("%d%d", &n, &m), n)
	{
		memset(a, 0, sizeof(a));
		memset(vist, 0, sizeof(vist));
		memset(t, 0, sizeof(t));
		for (int i = 0; i < m; i++)
		{
			int c, d;
			scanf("%d%d", &c, &d);
			a[c][d]++;
		}
		vist[0] = 1;
		t[0] = 1;
		puts(dfs() ? "BICOLORABLE." : "NOT BICOLORABLE.");
	}
	return 0;
}


虽然给定的参考内容未涉及UVA紫外图像中放电提取的方法和技术,但在实际中,有多种相关的方法和技术可用于此目的。 ### 基于阈值分割的方法 阈值分割是一种简单且常用的方法。该方法根据图像的灰度值,设定一个合适的阈值,将图像分为目标(放电区域)和背景两部分。例如,在UVA紫外图像中,放电区域通常具有较高的灰度值,可以通过设定一个灰度阈值,将灰度值高于该阈值的像素点判定为放电区域。可以使用Otsu算法自动确定最优的阈值,该算法通过最大化类间方差来找到一个合适的阈值,使得目标和背景的区分度最大。 ```python import cv2 import numpy as np # 读取UVA紫外图像 image = cv2.imread('uva_image.jpg', 0) # 使用Otsu算法进行阈值分割 _, binary_image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) ``` ### 形态学处理方法 形态学处理可以用于去除噪声、连接断裂的放电区域以及平滑放电区域的边界。常用的形态学操作包括膨胀、腐蚀、开运算和闭运算。膨胀操作可以将放电区域扩大,使得断裂的区域连接起来;腐蚀操作则相反,可以去除小的噪声点。开运算先进行腐蚀再进行膨胀,能够去除小的噪声;闭运算先进行膨胀再进行腐蚀,可以填充放电区域内的小孔。 ```python # 定义结构元素 kernel = np.ones((3, 3), np.uint8) # 进行开运算 opened_image = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel) # 进行闭运算 closed_image = cv2.morphologyEx(opened_image, cv2.MORPH_CLOSE, kernel) ``` ### 基于机器学习的方法 可以使用机器学习算法,如支持向量机(SVM)、随机森林等,对UVA紫外图像中的放电区域进行分类。首先,需要从图像中提取特征,如颜色特征、纹理特征、形状特征等。然后,使用这些特征训练机器学习模型,最后使用训练好的模型对新的图像进行分类,判断哪些像素点属于放电区域。 ```python from sklearn import svm from skimage.feature import hog # 提取HOG特征 features, _ = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True, multichannel=False) # 假设已经有了训练数据X_train和标签y_train clf = svm.SVC() clf.fit(X_train, y_train) # 对新的特征进行预测 prediction = clf.predict([features]) ``` ### 基于深度学习的方法 深度学习在图像识别领域取得了巨大的成功,也可以用于UVA紫外图像中放电区域的提取。例如,使用卷积神经网络(CNN),如U-Net、Mask R-CNN等。这些网络可以自动学习图像中的特征,直接输出放电区域的分割结果。 ```python import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Concatenate # 构建简单的U-Net模型 def unet_model(input_size=(256, 256, 1)): inputs = Input(input_size) # 编码器部分 conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) # 解码器部分 up1 = UpSampling2D(size=(2, 2))(pool1) merge1 = Concatenate(axis=3)([conv1, up1]) conv2 = Conv2D(1, 1, activation='sigmoid')(merge1) model = Model(inputs=inputs, outputs=conv2) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) return model model = unet_model() model.fit(X_train, y_train, epochs=10) ```
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