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    • 92. 发明授权
    • System and method for detection of multi-view/multi-pose objects
    • 用于检测多视点/多姿态对象的系统和方法
    • US08391592B2
    • 2013-03-05
    • US13134885
    • 2011-06-20
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • G06K9/62
    • G06K9/6256
    • The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.
    • 本发明提供了一种用于检测多视点多姿态对象的计算机实现过程。 该过程包括针对每个类内样本的分类器的训练,强分类器的训练和将单个基于样本的分类器与单个目标函数组合。 使用两个嵌套的AdaBoost循环来优化此功能。 第一个循环是选择区分候选样本的外循环。 第二个循环,内循环选择所选样本上的鉴别候选特征,以计算特定位置(例如视图/姿态)的所有弱分类器。 然后将所有计算的弱分类器自动组合成最终分类器(强分类器),该分类器是要检测的对象。
    • 99. 发明申请
    • MICROWAVE INDUCED SINGLE STEP GREEN SYNTHESIS OF SOME NOVEL 2-ARYL ALDEHYDES AND THEIR ANALOGUES
    • 微波诱导的单步绿色合成一些新型二芳基醛及其类似物
    • US20120041234A1
    • 2012-02-16
    • US13203100
    • 2010-02-25
    • Arun Kumar SinhaAbhishek SharmaRakesh KumarNaina Sharma
    • Arun Kumar SinhaAbhishek SharmaRakesh KumarNaina Sharma
    • C07C251/80C07C45/00B01J19/12C07C47/27
    • C07C45/28C07C45/30C07C249/16C07C311/49C07D317/54C07C47/228C07C47/24C07C47/23C07C47/277
    • The present invention provides a process for the preparation of some novel 2-aryl and 2,2-diaryl aldehydes and analogues which are privileged intermediates for commercially important nonsteroidal anti-inflammatory drugs including naproxen, flurbiprofen and potent anticancer drug candidates, including phenstatin through a unique single step synthetic methodology utilizing easily available substrates in the form of aryl alkenes as well as environmentally benign aqueous reaction conditions in the form of solvents such as mixtures of water and DMSO or Dioxane and reagents N-bromosuccinimide, N-iodosuccinimide, N-cholorosuccinimide and phase transfer catalyst such as cetyltrimethyl ammonium bromide, N-hexyl ammonium chloride for a reaction time varying from 1 min-30 min, depending upon microwave or conventional heating, without using expensive transition metal catalysts or lewis acids/bases with yield varying from 35-55%, depending upon the solvent and substrate used. The developed method provides a clean and convenient alternative to access a diverse range of medicinally important 2-aryl and 2,2-diaryl aldehyde based scaffolds in lieu of the conventional multistep protocols employing expensive and hazardous transition metal catalysts and lewis acids/bases.
    • 本发明提供了一种制备一些新的2-芳基和2,2-二芳基醛和类似物的方法,其是用于商业上重要的非甾族抗炎药物的特权中间体,包括萘普生,氟比洛芬和有效的抗癌药物候选物,包括通过 使用易于获得的芳基烯烃底物的独特的单步合成方法以及溶剂形式的环境友好的水性反应条件,例如水和DMSO或二恶烷的混合物以及试剂N-溴琥珀酰亚胺,N-碘代琥珀酰亚胺,N-氯代氯代琥珀酰亚胺 和相转移催化剂如十六烷基三甲基溴化铵,N-己基氯化铵,反应时间为1分钟-30分钟,取决于微波或常规加热,不使用昂贵的过渡金属催化剂或路易斯酸/碱,产率从35 -55%,这取决于所用的溶剂和底物。 开发的方法提供了一种干净和方便的替代方案,以便获得各种各样的药物重要的2-芳基和2,2-二芳基醛基支架,代替常规的多步骤方案,其采用昂贵且危险的过渡金属催化剂和路易斯酸/碱。
    • 100. 发明授权
    • System and method for detection of multi-view/multi-pose objects
    • 用于检测多视点/多姿态对象的系统和方法
    • US07965886B2
    • 2011-06-21
    • US11762400
    • 2007-06-13
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • Feng HanYing ShanHarpreet Singh SawhneyRakesh Kumar
    • G06K9/62
    • G06K9/6256
    • The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.
    • 本发明提供了一种用于检测多视点多姿态对象的计算机实现过程。 该过程包括针对每个类内样本的分类器的训练,强分类器的训练和将单个基于样本的分类器与单个目标函数组合。 使用两个嵌套的AdaBoost循环来优化此功能。 第一个循环是选择区分候选样本的外循环。 第二个循环,内循环选择所选样本上的鉴别候选特征,以计算特定位置(例如视图/姿态)的所有弱分类器。 然后将所有计算的弱分类器自动组合成最终分类器(强分类器),该分类器是要检测的对象。