Computer Vision: Segmentation and Pose Identification
Computer vision requires the ability to segment and identify poses. Segmentation refers to the division of a scene into distinct regions. Segmentation is useful for vision tasks like object recognition and tracking, where they are able to better analyse the objects by their size, shape and color. Pose identification, on the other hand is used for identifying the location and orientation of objects within an environment (Chen, et al. 2015). It is useful in facial recognition applications like gesture recognition. This allows for the identification of smaller variations of the exact same pose. Segmentation can also be combined with pose identification to enable complex tasks such as the identification of six-dimensional poses. To be able to navigate accurately with autonomous vehicles, it is necessary that precise information regarding the 3D position and orientation of vehicles on the roads can be obtained (Li et. al., 2002). Segmentation and pose identification are also part of simultaneous mapping and localization, indoor and outdoor. The two concepts are an essential part of modern computer visualisation. References Chen, M. C., Jhuang, H., Garnett, R., & Dollár, P. (2015). Flexible mixtures of parts allow for articulated pose estimation. Proceedings of IEEE International Conference on Computer Vision (pp. 4175-4183). Li, Z., Randlov, M., & Andresen, K. (2021). U-ReaL is a 6D Pose Estimation Network that includes Uncertainty, Realism-Aware Loss Functions and is 6D. IEEE Robotics and Automation Letters 6(1): 465-472. Toth, P. (2021). Computer Vision: Segmentation and Object Detection. https://www.safaribooksonline.com/library/view/machine-learning/9781789533572/b55c50e7-90d8-48fe-acla-2ac51783d476.xhtmlCont…
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