Accurate and fast hand-forearm segmentation algorithm based on silhouette

Wang, Bosheng; Xu, Jiaqi

doi:10.1109/ccis.2012.6664322

Cited by 2 publications

(1 citation statement)

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“…In [5], the segmentation purely depends on the employing the depth information to facilitate the natural gestural interface development, but the applicability will be bounded on well-controlled settings. The work in [6] achieved a well geometric combination of processes including the searching of reliable center in the targeted palm, the mean shift algorithm and Distance Transform (DT) algorithm for moving hand segmentation. Recenth and segmentation approaches are focused on how to acquire robustness in real time or at least just-in-time [7].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Posture Recognition of Moving Hands by Profile-Mold Construction in Cluttered Background and Occlusion

Chan¹,

Lin²,

Wu³

2018

JSIP

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In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible occlusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.

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Section: Introductionmentioning

confidence: 99%