基于差分的火焰识别方法是根据运动目标的速度进行相邻帧或者隔帧差分以得到运动信息。
The method of flame recognition based on image difference detects the moving information by differencing two sequential frames or every two frames according to the speed of object.
实验证明，与传统火焰识别方法相比，本文所提出的火焰识别方法能有效的提高火焰识别的正确率，达到了预期的设计目的。
The experiment proves that the flame detector mentioned in this paper is performances better than traditional detector and realizes the expected design aim.
本文利用火焰的颜色信息，火焰的图像序列的边缘不稳定和相似性等可识别特征，实现对火焰的识别。
This paper use effective character of the fire, for example, color information, instability and comparability of the edge of image sequence to realize the automatic alarming for fire.
氧乙炔火焰的燃烧过程是一个随机过程，用对随机过程有很好模拟能力的隐马尔可夫模型(HMM)对其进行建模与识别，可以达到更好的效果。
The combustion process of the oxyacetylene flame is a random one, it obtains good result using the Hidden Markov Model (HMM) which has good imitation ability on the random process.
实验证明，综合烟雾及火焰的静态特征及动态特征的火灾检测方法，识别率高。
The experimentation results show that the fire detecting method which integrating smoke"s and flame"s static character and dynamic character has high recognition rate.
针对全炉膛火焰检测的主要问题，提出了一种基于粗糙集理论的火焰状态识别方法。
A flame status discrimination method based on rough sets theory has been presented in the light of the main problems concerning the flame detection of an entire furnace.
它根据火焰辐射的光谱特征，利用多个红外传感器信号的特征与相互关系识别火焰信号。
Based on the spectral characteristics of flame radiation, the detector USES the characteristics and the correlation of data received by these infrared sensors to achieve fire determination.
实验结果表明，该火灾识别方法能够有效地识别出火灾火焰并具一定的抗干扰能力。
The results show that this fire detection method can recognize the fire effectively and has good automatic recognition ability.
然后，根据所提取出的像素点的分布特点识别出烟雾，从而实现烟雾和火焰与其复杂背景的准确分割。
Then, distinguish smoke by the characteristics of distribution of pixel. In this way, smork and flames will be segmented from complex background area.
实现对氧乙炔火焰燃烧状态的计算机识别，可以更好地实现其燃烧过程的自动控制，有利于提高生产力。
If the combustion states of the oxyacetylene can be identified by computer, it would make the auto-control of the combustion process better and the productivity improved.
最后运用RBF神经网络建立火灾识别模型，将提取出的火焰特征作为输入量，对火灾图像进行分类识别。
Finally, the model of fire detector algorithms is established by using RBF neural network net in which flame characters are taken as inputs and by which is used to classify and recognize fire image.
以火灾视频和干扰视频为分析对象．利用支持向量机研究火焰及干扰物体的分类识别问题。
Presents support vector machine to solve the classification problem based on the fire video and suspected fire video.
提出了一种基于火焰图像动态特征的火灾识别算法。
A fire detection method based on flame dynamic features is proposed.
提出了一种基于火焰图像动态特征的火灾识别算法。
A fire detection method based on flame dynamic features is proposed.
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