Image Reconstruction and Per-pixel Classification
Gondhi Navabharat Reddy1, Sruthi Setlem3
1Gondhi Navabharat Reddy, Assistant professor, ECE, Vignan Institute of Technolgy and science(VITS).
2Sruthi Setlem, Assistant professor ECE.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5612-5617 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9941038620/2020©BEIESP | DOI: 10.35940/ijrte.F9941.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We describe face classification algorithm which can be used for object recognition, pose estimation, tracking and gesture recognition which are useful for human-computer interaction. We make use of depth camera (Creative Interactive Gesture Camera – Kinect®) to acquire the images which gives several advantages when compared over a normal RGB optical camera. In this paper we demonstrate a intermediate parsing scheme, so that an accurate per-pixel classification is used to localize the joints. We make use of an efficient random decision forest to classify the image which in turn helps to estimate the pose. As we employ depth camera to acquire depth image it may contain holes on or around depth map, so we first fill those holes and the classify the image. Simulation results was observed by varying several training parameters of the decision forest. We generally learned an efficient method which stems the basics in the development of pose estimation and tracking. Also we gained an intensive knowledge on Decision forests.
Keywords: Depth Map, Decision Tree, RDF, Per-pixel Classification, Weak learner function, Entropy.
Scope of the Article: Classification.