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    • 2. 发明申请
    • METHOD, APPARATUS AND PROGRAM FOR DETECTING AN OBJECT
    • 用于检测对象的方法,装置和程序
    • WO2005081792A3
    • 2006-09-21
    • PCT/US2005003822
    • 2005-02-07
    • HONDA MOTOR CO LTDYANG MING-HSUANLIM JONGWOOROSS DAVID AOHASHI TAKAHIRO
    • YANG MING-HSUANLIM JONGWOOROSS DAVID AOHASHI TAKAHIRO
    • G06K9/36G06K9/00G06K9/48
    • G06K9/00201
    • The advantage of the present invention is to appropriately detect the object. The object detection apparatus in the present invention has a plurality of cameras (2) to determine the distance to the objects, a distance determination unit (11) to determine the distance therein, a histogram generation unit (13) to specify the frequency of the pixels against the distances to the pixels, an object distance determination unit (14) that determines the most likely distance, a probability mapping unit (15) that provides the probabilities of the pixels based on the difference of the distance, a kernel detection unit (16a) that determines a kernel region as a group of the pixels, a periphery detection unit (16b) that determines a peripheral region as a group of the pixels, selected from the pixels being close to the kernel region and an object specifying unit (17) that specifies the object region where the object is present with a predetermined probability.
    • 本发明的优点是适当地检测物体。 本发明的物体检测装置具有多个照相机(2),用于确定与物体的距离,距离确定单元(11),用于确定其中的距离;直方图生成单元(13),用于指定 相对于像素的距离的像素,确定最可能的距离的对象距离确定单元(14),基于距离的差异提供像素的概率的概率映射单元(15),内核检测单元 确定作为像素组的核心区域的周边检测单元(16b),其将周围区域确定为从接近所述核心区域的像素中选择的像素组,以及对象指定单元(17 )以预定概率指定对象存在的对象区域。
    • 3. 发明申请
    • FACE RECOGNITION SYSTEM
    • 脸部识别系统
    • WO2005079237A3
    • 2007-12-27
    • PCT/US2005003818
    • 2005-02-07
    • HONDA MOTOR CO LTDYANG MING-HSUANLIM JONGWOOROSS DAVID AOHASHI TAKAHIRO
    • YANG MING-HSUANLIM JONGWOOROSS DAVID AOHASHI TAKAHIRO
    • G06K9/62G06K9/00G06K9/68G06K9/74
    • G06K9/6269G06K9/00228G06K9/6857
    • The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.
    • 面部检测系统和方法在执行所有内核评估之前尝试对测试图像进​​行分类。 许多子图像不是面孔,应该比较容易识别。 因此,SVM分类器尝试使用级联SVM分类尽可能少地使用内核评估来丢弃非面部图像。 在第一阶段,对前两个支持向量计算分数,并将分数与阈值进行比较。 如果分数低于阈值,则子图像被分类为不是脸部。 如果分数高于阈值,则级联SVM分类功能继续应用更复杂的决策规则,每次将内核评估的数量加倍,将图像分类为非面(并因此终止进程),一旦 测试图像不能满足其中一个决策规则。 最后,如果子图像满足了所有的中介决策规则,并且现在已经到了必须考虑所有支持向量的点,则应用原始决策函数。 满足这个最终规则和所有中介规则是测试图像获得积极(面部)分类的唯一方法。
    • 5. 发明申请
    • DIRECT METHOD FOR MODELING NON-RIGID MOTION WITH THIN FILM PLATE SPLINE TRANSFORMATION
    • 用薄膜板样条变换建立非刚性运动的直接方法
    • WO2007001884A3
    • 2007-12-06
    • PCT/US2006023350
    • 2006-06-15
    • HONDA MOTOR CO LTDLIM JONGWOOYANG MING-HSUAN
    • LIM JONGWOOYANG MING-HSUAN
    • G06K9/36
    • G06T7/20G06K9/6206
    • A system and a method model the motion of a non-rigid object using a thin plate spline (TPS) transform. A first image of a video sequence is received, and a region of interest, referred to as a template, is chosen manually or automatically. A set of arbitrarily-chosen fixed reference points is positioned on the template. A target image of the video sequence is chosen for motion estimation relative to the template. A set of pixels in the target image corresponding to the pixels of the template is determined, and this set of pixels is back- warped to match the template using a thin-plate-spline-based technique. The error between the template and the back-warped image is determined and iteratively minimized using a gradient descent technique. The TPS parameters can then be used to estimate the relative motion between the template and the corresponding region of the target image. According to one embodiment, a stiff-to-flexible approach mitigates instability that can arise when reference points lie in textureless regions, or when the initial TPS parameters are not close to the desired ones. The value of a regularization parameter is varied from a larger to a smaller value, varying the nature of the warp from stiff to flexible, so as to progressively emphasize local non-rigid deformations.
    • 系统和方法使用薄板样条(TPS)变换来模拟非刚性物体的运动。 接收视频序列的第一图像,并且手动或自动选择被称为模板的感兴趣区域。 一组任意选择的固定参考点位于模板上。 视频序列的目标图像被选择用于相对于模板的运动估计。 确定目标图像中与模板的像素相对应的一组像素,并且使用基于薄板样条的技术来对这组像素进行反向翘曲以匹配模板。 使用梯度下降技术确定模板和反向变形图像之间的误差并迭代最小化。 然后可以使用TPS参数来估计模板与目标图像的对应区域之间的相对运动。 根据一个实施例,当参考点位于无纹理区域中时,或者当初始TPS参数不接近期望值时,刚性到柔性方法减轻可能出现的不稳定性。 正则化参数的值从较大值变化到较小值,从而使经线的性质从刚性变为柔性,从而逐渐强调局部非刚性变形。
    • 6. 发明申请
    • LEVERAGING TEMPORAL CONTEXTUAL AND ORDERING CONSTRAINTS FOR RECOGNIZING COMPLEX ACTIVITIES IN VIDEO
    • 利用时间上下文和订购限制来识别视频中的复杂活动
    • WO2008076587A3
    • 2008-08-07
    • PCT/US2007085273
    • 2007-11-20
    • HONDA MOTOR CO LTDLIM JONGWOOLAXTON BENJAMIN
    • LIM JONGWOOLAXTON BENJAMIN
    • G06K9/00
    • G06K9/00335
    • A system (and a method) are disclosed for recognizing and representing activities in a video sequence. The system includes an activity dynamic Bayesian network (ADBN), an object/action dictionary, an activity inference engine and a state output unit. The activity dynamic Bayesian network encodes the prior information of a selected activity domain. The prior information of the selected activity domain describes the ordering, temporal constraints and contextual cues among the expected actions. The object/action dictionary detects activities in each frame of the input video stream, represents the activities hierarchically, and generates an estimated observation probability for each detected action. The activity inference engine estimates a likely activity state for each frame based on the evidence provided by the object/action dictionary and the ADBN. The state output unit outputs the likely activity state generated by the activity inference engine.
    • 公开了用于识别和表示视频序列中的活动的系统(和方法)。 该系统包括活动动态贝叶斯网络(ADBN),对象/动作字典,活动推断引擎和状态输出单元。 活动动态贝叶斯网络对所选活动域的先前信息进行编码。 所选活动领域的先验信息描述预期行动中的排序,时间限制和上下文线索。 对象/动作字典检测输入视频流的每个帧中的活动,分层地表示活动,并为每个检测到的动作生成估计的观察概率。 活动推理引擎根据对象/动作字典和ADBN提供的证据估计每帧的可能活动状态。 状态输出单元输出由活动推理引擎生成的可能的活动状态。