图 书 馆
在学习非参数零件模型中的链接特征Using linking features in learning non-parametric part models |
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| 课程网址: | http://videolectures.net/eccv2012_karlinsky_models/ |
| 主讲教师: | Stefan Carlsson; Antonio Torralba; Leonid Karlinsky |
| 开课单位: | 魏茨曼科学研究所 |
| 开课时间: | 2012-11-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present an approach to the detection of parts of highly deformable objects, such as the human body. Instead of using kinematic constraints on relative angles used by most existing approaches for modeling part-to-part relations, we learn and use special observed 'linking' features that support particular pairwise part configurations. In addition to modeling the appearance of individual parts, the current approach adds modeling of the appearance of part-linking, which is shown to provide useful information. For example, configurations of the lower and upper arms are supported by observing corresponding appearances of the elbow or other relevant features. The proposed model combines the support from all the linking features observed in a test image to infer the most likely joint configuration of all the parts of interest. The approach is trained using images with annotated parts, but no a-priori known part connections or connection parameters are assumed, and the linking features are discovered automatically during training. We evaluate the performance of the proposed approach on two challenging human body parts detection datasets, and obtain performance comparable, and in some cases superior, to the state-of-the-art. In addition, the approach generality is shown by applying it without modification to part detection on datasets of animal parts and of facial fiducial points. |
| 关 键 词: | 计算机科学; 计算机视觉; 物体识别 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-07：lxf |
| 阅读次数: | 24 |