A Study on Hand Region Detection for Kinect-Based Hand Shape Recognition

Park, Hanhoon; Choi, Junyeong; Park, Jong-Il; Moon, Kwang-Seok

doi:10.5909/jbe.2013.18.3.393

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…As shown in Figure 3 , the principal point of the world coordinates is located in the top-left position of the screen. The transformation is performed according to the following equations [ 15 ]:

where minDist = − 10 and SF =0.0021 are based on the calibration results of previous works [ 11 ] and i and j are the horizontal and vertical pixel positions of the captured image frame with a spatial resolution of 640 × 480 pixels. Because the default Z -distance value ( z k ) can be as small as 400 mm, the Z -axis value in ( 3 ) must be compensated accordingly.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Kim

Lee

2014

The Scientific World Journal

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Although many three-dimensional pointing gesture recognition methods have been proposed, the problem of self-occlusion has not been considered. Furthermore, because almost all pointing gesture recognition methods use a wide-angle camera, additional sensors or cameras are required to concurrently perform finger gesture recognition. In this paper, we propose a method for performing both pointing gesture and finger gesture recognition for large display environments, using a single Kinect device and a skeleton tracking model. By considering self-occlusion, a compensation technique can be performed on the user's detected shoulder position when a hand occludes the shoulder. In addition, we propose a technique to facilitate finger counting gesture recognition, based on the depth image of the hand position. In this technique, the depth image is extracted from the end of the pointing vector. By using exception handling for self-occlusions, experimental results indicate that the pointing accuracy of a specific reference position was significantly improved. The average root mean square error was approximately 13 pixels for a 1920 × 1080 pixels screen resolution. Moreover, the finger counting gesture recognition accuracy was 98.3%.

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Section: Proposed Methodsmentioning

confidence: 99%

“…To facilitate interactive display manipulation, many finger gesture recognition methods have been studied. In a previous research effort [ 11 ], a fingertip detection method that combined depth images with color images was proposed. In this method, a finger outline tracking scheme was used, and its accuracy was relatively high.…”

Section: Introductionmentioning

confidence: 99%

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Kim

Lee

2014

The Scientific World Journal

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“…On the other hand, the markerless method utilizes techniques such as optical flow, background subtraction, and motion history image to detect the direction and speed of a user's movement; it can quickly track the movements on a real -time basis and also can use brightness of light source. However, the markerless method is susceptible to external factors such as the location of camera, light source interference, and shadows [3][4][5][6].…”

Section: Kinect-based Non-contact Interfacementioning

confidence: 99%

“…Dots with weak intensity of reflection are assumed to come from far away and those with strong intensity of reflection are assumed to come from the user on the front. Thereby Kinect sensor identifies a person's main bone joints and detects body movements [2][3][4][5][6][7].…”

Section: Kinect-based Non-contact Interfacementioning

confidence: 99%

A Study on Motion Recognition through Kinect-based Machine- Learning

Jung¹

2018

IJCA

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Motions are a representative, readily accessible visual language. Typical visual languages include predefined sign languages and hand signals for traffic control. However, there are situations where intentions and thoughts should be expressed with motions faster than dialogue.Understanding of motions requires comprehensive judgment on their lines, time points of occurrence, and appearances from the starting to the ending point.One of the difficulties is to distinguish whether a motion is an intended one or not. To resolve such difficulty, research has been conducted where different motions are taught to a machine using a learning algorithm to predict certain motions of a user. Accordingly, this study assumed a situation where data on a user's own custom hand signals of static and dynamic types obtained from Kinect's sensor is stored and machinelearned so that the user who will learn the hand signals can check the data.

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“…Park recognized the hand shape using the depth information, a color image, and the geometric characteristics of the hand. [16]. First, he found the common part of the hand region using the skin color and depth information of the hand region in an image.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Recognition Method of Counting Fingers for Natural User Interface

2016

KSII TIIS

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Communication occurs through verbal elements, which usually involve language, as well as non-verbal elements such as facial expressions, eye contact, and gestures. In particular, among these non-verbal elements, gestures are symbolic representations of physical, vocal, and emotional behaviors. This means that gestures can be signals toward a target or expressions of internal psychological processes, rather than simply movements of the body or hands. Moreover, gestures with such properties have been the focus of much research for a new interface in the NUI/NUX field. In this paper, we propose a method for recognizing the number of fingers and detecting the hand region based on the depth information and geometric features of the hand for application to an NUI/NUX. The hand region is detected by using depth information provided by the Kinect system, and the number of fingers is identified by comparing the distance between the contour and the center of the hand region. The contour is detected using the Suzuki85 algorithm, and the number of fingers is calculated by detecting the finger tips in a location at the maximum distance to compare the distances between three consecutive dots in the contour and the center point of the hand. The average recognition rate for the number of fingers is 98.6%, and the execution time is 0.065 ms for the algorithm used in the proposed method. Although this method is fast and its complexity is low, it shows a higher recognition rate and faster recognition speed than other methods. As an application example of the proposed method, this paper explains a Secret Door that recognizes a password by recognizing the number of fingers held up by a user.

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A Study on Hand Region Detection for Kinect-Based Hand Shape Recognition

Cited by 12 publications

References 7 publications

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

A Study on Motion Recognition through Kinect-based Machine- Learning

Real-Time Recognition Method of Counting Fingers for Natural User Interface

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