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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM 被引量:4
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作者 Fangming Bi Xuanyi Fu +3 位作者 Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa 《Computers, Materials & Continua》 SCIE EI 2020年第1期199-216,共18页
Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed... Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios. 展开更多
关键词 Fire detection image segmentation feature extraction fruit fly optimization support vector machine
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METHOD TO EXTRACT BLEND SURFACE FEATURE IN REVERSE ENGINEERING 被引量:5
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作者 LUeZhen KeYinglin +2 位作者 SunQing KelvinW HuangXiaoping 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2003年第3期248-251,263,共5页
A new method of extraction of blend surface feature is presented. It contains two steps: segmentation and recovery of parametric representation of the blend. The segmentation separates the points in the blend region f... A new method of extraction of blend surface feature is presented. It contains two steps: segmentation and recovery of parametric representation of the blend. The segmentation separates the points in the blend region from the rest of the input point cloud with the processes of sampling point data, estimation of local surface curvature properties and comparison of maximum curvature values. The recovery of parametric representation generates a set of profile curves by marching throughout the blend and fitting cylinders. Compared with the existing approaches of blend surface feature extraction, the proposed method reduces the requirement of user interaction and is capable of extracting blend surface with either constant radius or variable radius. Application examples are presented to verify the proposed method. 展开更多
关键词 Reverse engineering Segmentation Blend surface Feature extraction
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Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas
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作者 Alexander Goltsev Vladimir Gritsenko Dušan Húsek 《Journal of Signal and Information Processing》 2020年第4期75-102,共28页
The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous ... The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishe</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, <i></span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;"></i>, it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes. 展开更多
关键词 Texture Feature Texture Window Homogeneous Fine-Grained Texture Segment extraction of Texture Segment Texture Segmentation
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