The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like featur...The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like feature is composed of two or more attached same rectangles. Inefficiency of the Haar-like feature often results from two or more attached same rectangles. But the proposed SSRF are composed of two separated same rectangles. So, it is very flexible and detailed. Therefore it creates more accurate strong classifier than Haar-like feature. SSRF uses integral image to reduce execuive time. Haar-like feature calculates the Sanl of intmsities of pixels on two or more rectangles. But SSRF always calculates the stun of intensities of pixels on only two rectangles. The weak classifier of Ariaboost algorithm based on SSRF is fastex than one based on Haar-like feature. In the experiment, we use 1 000 face images and 1 000nm- face images for Adaboost training. The proposed SSRF shows about 0.9% higher acctwacy than Haar-like features.展开更多
The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike...The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike featrue of Viola, which is commonly used for the Adaboost training algorithm. The Split Rectangle feature uses the nmsk-like shape composed with 2 independent rectangles, instead of using mask-like shape of Haar-like feature, which is composed of 2 --4 adhered rectangles of Viola. Split Rectangle feature has less di- verged operation than the Haar-like feaze. It also requires less oper- ation because the stun of pixels requires ordy two rectangles. Split Rectangle feature provides various and fast features to the Adaboost, which produrces the strong classifier with increased accuracy and speed. In the experiment, the system had 5.92 ms performance speed and 84 %--94 % accuracy by leaming 5 facial expressions, neutral, happiness, sadness, anger and surprise with the use of the Adaboost based on the Split Rectangle feature.展开更多
A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessor...A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessory coordination) is used to filter the image database to decouple the image search scope. After the accessory contour information and region information are extracted, the fusion multi-feature of the centroid distance Fourier descriptor and distance distribution histogram is adopted to finish image retrieval accurately. All the features above are invariable under translation, scaling and rotation. Results from the test on the image database including 1,000 accessory images demonstrate that the method is effective and practical with high accuracy and fast speed.展开更多
配电网运行包络将配电网安全运行与分布式能源(distributed energy resource,DER)调控解耦,将配电网安全运行约束映射到DER出力约束空间,为解决配电网和DER分属主体不同、调控目标差异的问题提供有效手段。设计配电网运行包络的关键是...配电网运行包络将配电网安全运行与分布式能源(distributed energy resource,DER)调控解耦,将配电网安全运行约束映射到DER出力约束空间,为解决配电网和DER分属主体不同、调控目标差异的问题提供有效手段。设计配电网运行包络的关键是计算各个节点功率准许波动范围,现有研究主要关注有功运行包络,忽略无功波动范围。该文研究配电网的有功-无功运行包络计算方法。首先,构建有功-无功运行包络的旋转矩形模型,通过引入旋转参数,扩大运行包络中有功-无功区域;其次,建立考虑配电网电压与潮流约束的运行包络鲁棒优化模型,并推导其二次约束规划问题;然后,提出基于网络拓扑参数特征的运行包络快速算法,显著提升优化问题求解速度;最后,采用不同规模配电网进行算例分析,验证所提出的配电网运行包络计算方法的有效性和先进性。展开更多
基金supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD),the MKE(The Ministry of Knowledge Economy,Korea)the ITRC(Information Technology Research Center)support program(NIPA-2009-(C1090-0902-0007))
文摘The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like feature is composed of two or more attached same rectangles. Inefficiency of the Haar-like feature often results from two or more attached same rectangles. But the proposed SSRF are composed of two separated same rectangles. So, it is very flexible and detailed. Therefore it creates more accurate strong classifier than Haar-like feature. SSRF uses integral image to reduce execuive time. Haar-like feature calculates the Sanl of intmsities of pixels on two or more rectangles. But SSRF always calculates the stun of intensities of pixels on only two rectangles. The weak classifier of Ariaboost algorithm based on SSRF is fastex than one based on Haar-like feature. In the experiment, we use 1 000 face images and 1 000nm- face images for Adaboost training. The proposed SSRF shows about 0.9% higher acctwacy than Haar-like features.
基金supported by the Brain Korea 21 Project in2010,the MKE(The Ministry of Knowledge Economy),Koreathe ITRC(Information Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NI-PA-2010-(C1090-1021-0010))
文摘The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike featrue of Viola, which is commonly used for the Adaboost training algorithm. The Split Rectangle feature uses the nmsk-like shape composed with 2 independent rectangles, instead of using mask-like shape of Haar-like feature, which is composed of 2 --4 adhered rectangles of Viola. Split Rectangle feature has less di- verged operation than the Haar-like feaze. It also requires less oper- ation because the stun of pixels requires ordy two rectangles. Split Rectangle feature provides various and fast features to the Adaboost, which produrces the strong classifier with increased accuracy and speed. In the experiment, the system had 5.92 ms performance speed and 84 %--94 % accuracy by leaming 5 facial expressions, neutral, happiness, sadness, anger and surprise with the use of the Adaboost based on the Split Rectangle feature.
文摘A hierarchical retrieval scheme of the accessory image database is proposed based on textile industrial accessory contour feature and region feature. At first smallest enclosed rectangle[1] feature (degree of accessory coordination) is used to filter the image database to decouple the image search scope. After the accessory contour information and region information are extracted, the fusion multi-feature of the centroid distance Fourier descriptor and distance distribution histogram is adopted to finish image retrieval accurately. All the features above are invariable under translation, scaling and rotation. Results from the test on the image database including 1,000 accessory images demonstrate that the method is effective and practical with high accuracy and fast speed.
文摘配电网运行包络将配电网安全运行与分布式能源(distributed energy resource,DER)调控解耦,将配电网安全运行约束映射到DER出力约束空间,为解决配电网和DER分属主体不同、调控目标差异的问题提供有效手段。设计配电网运行包络的关键是计算各个节点功率准许波动范围,现有研究主要关注有功运行包络,忽略无功波动范围。该文研究配电网的有功-无功运行包络计算方法。首先,构建有功-无功运行包络的旋转矩形模型,通过引入旋转参数,扩大运行包络中有功-无功区域;其次,建立考虑配电网电压与潮流约束的运行包络鲁棒优化模型,并推导其二次约束规划问题;然后,提出基于网络拓扑参数特征的运行包络快速算法,显著提升优化问题求解速度;最后,采用不同规模配电网进行算例分析,验证所提出的配电网运行包络计算方法的有效性和先进性。
文摘针对车牌无法识别的车辆,研究了一种车脸定位及识别方法。该方法分为两个阶段:首先,使用Adaboost算法进行车脸定位,并利用经验矩形方法进行定位改进;其次,在定位出来的车脸区域提取SIFT(scale-invariantfeature transform)和SURF(speeded up robust feature)局部不变性特征,利用这两种不变性特征的叠加及位置约束改进匹配算法,与标准车型数据库中的车脸特征进行匹配,根据匹配结果进行车脸识别,从而得到车辆类型。实验结果表明,该方法的正确识别率达到83.6%。交通卡口抓拍到的车辆照片基本是正前照,无法获取车身侧面信息分析其车型。针对车牌无法识别的车辆,通过车脸定位、特征提取,并与标准车型库中车脸进行对比,进而识别车脸,该识别车脸的方法为识别车型提供了一种新途径。
