Motion-based finger tracking for user interaction with mobile devices

Hannuksela, Jari; Huttunen, Sami; Sangi, Pekka; Heikkilä, Janne

doi:10.1049/cp:20070038

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…Methods that detect hand regions from gray scale images have been proposed [24,25]; only uniform backgrounds were considered. Motion features may be used to track fingers [26,27], but precise finger motions may not easily be tracked by region motion.…”

Section: Proposed Methodologymentioning

confidence: 99%

Finger Detection Algorithm For Computer Mouse Control

Rodrigue¹,

Lee²

2017

Journal of Korea Multimedia Society

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We propose a finger detection algorithm for computer mouse control to control the most commonly actions of a computer mouse(left, right and double click, scroll up and down then we add open and close, minimize and maximize a window, control the mouse.) We use a built-in web camera to control the mouse tasks. We detected, segment, then recognize the hand in our previous papers [1,2]. The user will be able to interact with the computer with the number of fingers detected.

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Section: Proposed Methodologymentioning

confidence: 99%

Finger Detection Algorithm For Computer Mouse Control

Rodrigue¹,

Lee²

2017

Journal of Korea Multimedia Society

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“…One of the benefits of this approach is that no iterations are needed, which makes the algorithm computationally efficient. The basic idea of the method was originally presented in our previous paper [6]. In this paper, we extend that work, and propose a new method for tracking multiple objects from image sequences that also includes the procedure for extracting the measurements from binary masks that have been obtained using some segmentation method.…”

Section: Introductionmentioning

confidence: 90%

Multi-object tracking using binary masks

Huttunen

Heikkilä

2008

2008 15th IEEE International Conference on Image Processing

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In this paper, we introduce a new method for tracking multiple objects. The method combines Kalman filtering and the Expectation Maximization (EM) algorithm in a novel way to deal with observations that obey a Gaussian mixture model instead of a unimodal distribution that is assumed by the ordinary Kalman filter. It also involves a new approach to measuring the object locations using a series of morphological operations with binary masks. The benefit of this approach is that soft assignment of the measurements to corresponding objects can be performed automatically using their a posteriori probabilities. This is a general approach for multi-object tracking, and there are basically various ways to segment the objects, but in this paper we use simple color features simply to demonstrate the feasibility of the concept.

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“…Camera input can also be used for tracking the self-movement of the device, which can be used for controlling scroll and view direction in an application [45]. Researchers have also proposed a finger tracking system, which allows the user to interact with 3-D scenes floating in 3-D [46].…”

Section: Gotchev Et Al: Three-dimensional Media For Mobile Devicesmentioning

confidence: 99%