HDOJ1010 Tempter of the Bone(DFS)

一只小狗在古老迷宫中发现了一块骨头,但这却触发了迷宫的机关,地面开始崩塌。小狗必须在限定时间内找到出口逃生。此问题通过深度优先搜索算法解决,利用奇偶剪枝技巧来优化搜索过程。

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Tempter of the Bone

The doggie found a bone in an ancient maze, which fascinated him a lot. However, when he picked it up, the maze began to shake, and the doggie could feel the ground sinking. He realized that the bone was a trap, and he tried desperately to get out of this maze.

The maze was a rectangle with sizes N by M. There was a door in the maze. At the beginning, the door was closed and it would open at the T-th second for a short period of time (less than 1 second). Therefore the doggie had to arrive at the door on exactly the T-th second. In every second, he could move one block to one of the upper, lower, left and right neighboring blocks. Once he entered a block, the ground of this block would start to sink and disappear in the next second. He could not stay at one block for more than one second, nor could he move into a visited block. Can the poor doggie survive? Please help him.


Input

The input consists of multiple test cases. The first line of each test case contains three integers N, M, and T (1 < N, M < 7; 0 < T < 50), which denote the sizes of the maze and the time at which the door will open, respectively. The next N lines give the maze layout, with each line containing M characters. A character is one of the following:

'X': a block of wall, which the doggie cannot enter;
'S': the start point of the doggie;
'D': the Door; or
'.': an empty block.

The input is terminated with three 0's. This test case is not to be processed.


Output

For each test case, print in one line "YES" if the doggie can survive, or "NO" otherwise.


Sample Input

4 4 5
S.X.
..X.
..XD
....
3 4 5
S.X.
..X.
...D
0 0 0


Sample Output

NO
YES


题解:dfs 搜索题,也是搜素入门的题目,第一个奇偶剪枝一般是用这个去学习的,作为记录。


#include<stdio.h>
#include<string.h>
#include<iostream>
#include<math.h>
#include<stdlib.h>
using namespace std;

char s[8][8];
int dir[4][2]={{0,-1},{0,1},{1,0},{-1,0}};//遍历方向
int n,m,k,step,aa,bb,vis[108][108];
bool ans;

void dfs(int x,int y,int deep)
{
	if(deep==k&&x==aa&&y==bb){
		ans=1;
	}
	if(ans)
		return ;
	int temp=k-deep-abs(x-aa)-abs(y-bb);//奇偶剪枝
	if(temp&1||temp<0)
		return ;
	for(int i=0;i<4;i++){
		int dx=x+dir[i][0];
		int dy=y+dir[i][1];
		if(dx<n&&dx>=0&&dy<m&&dy>=0&&s[dx][dy]!='X'&&vis[dx][dy]==0){
			vis[dx][dy]=1;
			dfs(dx,dy,deep+1);
			vis[dx][dy]=0;
		}
	}
}

int main()
{
	while(scanf("%d%d%d",&n,&m,&k)==3&&(n||m||k)){
		for(int i=0;i<n;i++)
			scanf("%s",s[i]);
		int xx,yy;
		int sum=0;
		memset(vis,0,sizeof(vis));
		for(int i=0;i<n;i++)
			for(int j=0;j<m;j++){
				if(s[i][j]=='S'){
					xx=i;
					yy=j;
				}
				if(s[i][j]=='D'){
					aa=i;
					bb=j;
					sum++;
				}
				if(s[i][j]=='.')
					sum++;
			}
		ans=0;
		vis[xx][yy]=1;
		if(k<=sum)//一个简单的剪枝
			dfs(xx,yy,0);
		if(ans)
			cout<<"YES"<<endl;
		else
			cout<<"NO"<<endl;
	}
	return 0;
}


内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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