Finger Localization and Classification in Images based on Generalized Hough Transform and Probabilistic Models

Barrho, Jörg; Adam, M.Immanuel; Kiencke, Uwe

doi:10.1109/icarcv.2006.345301

Cited by 12 publications

(13 citation statements)

References 4 publications

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“…Two major related works for this thesis are an automatic ship-types classification scheme [5] and a GHT based scheme for fingerprint localization and classification [6].…”

Section: B Related Workmentioning

confidence: 99%

“…The GHT algorithm has been shown to be an effective technique for creating Rtables used as models for fingerprinting [6]. Then, these tables are used for feature selection prior to classification.…”

Section: B Related Workmentioning

confidence: 99%

“…The first element in a DCT coefficient matrix is called the DC coefficient (zero frequency) which accounts for the average brightness of an image [6], [14]. The remaining elements are AC coefficients.…”

Section: Frequency Interpretation Of Dct Coefficientsmentioning

confidence: 99%

“…The GHT algorithm has been successfully used for pattern recognition [23], detection [24], and classification [6]. Due to its high computational cost [25], a few variants of the GHT, such as the invariant generalized Hough transform [25], the randomized Hough transform [26], and the randomized, generalized Hough transform [9] have been developed to improve the detection time and storage capacity.…”

Section: Generalized Hough Transformmentioning

confidence: 99%

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Generalized Hough Transform for Object Classification in the Maritime Domain

Rerkngamsanga¹

2015

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. FUNDING NUMBERS AUTHOR(S) Pornrerk Rerkngamsanga PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Naval Postgraduate School Monterey, CA 93943-5000 PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES)N/A SPONSORING / MONITORING AGENCY REPORT NUMBER11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol number ____N/A____. 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited 12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)A Generalized Hough Transform (GHT)-based classification scheme for an object-of-interest in maritime-domain images is proposed in this thesis. First, the object edge points are extracted and used to generate a representation of the object as a Hough coordinate table by using the GHT algorithm. The table is then reformatted to a contour map called a Hough features map. The coordinates of dominant peaks or Hough features on the map are extracted and fed into a feed-forward, back-propagation neural network for classification. In this research, the scheme is tested using perfect shapes of triangles, squares, circles, and stars and maritime-domain images of ships, aircraft, and clouds, and the classification results obtained are reported. SUBJECT TERMS

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“…Two major related works for this thesis are an automatic ship-types classification scheme [5] and a GHT based scheme for fingerprint localization and classification [6].…”

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Frequency Interpretation Of Dct Coefficientsmentioning

confidence: 99%

Section: Generalized Hough Transformmentioning

confidence: 99%

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Generalized Hough Transform for Object Classification in the Maritime Domain

Rerkngamsanga¹

2015

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“…However, the objects which share the similar shape of fingertips, will result in a misjudgment. In [1], fingertips were located by generalized Hough Transform and probabilistic models. The detected edge segments of the black and white images is computed by Hough Transform for fingertip detection.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition Based on the Parallel Edge Finger Feature and Angular Projection

Zhou

Jiang

et al. 2015

Computer Vision - ACCV 2014 Workshops

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Abstract. In this paper, a novel high-level hand feature extraction method is proposed by the aid of finger parallel edge feature and angular projection. The finger is modelled as a cylindrical object and the finger images can be extracted from the convolution with a specific operator as salient hand edge images. Hand center, hand orientation and wrist location are then determined via the analysis of finger image and hand silhouette. The angular projection of the finger images with origin point on the wrist is calculated, and five fingers are located by analyzing the angular projection. The proposed algorithm can detect extensional fingers as well as flexional fingers. It is robust to the hand rotation, side movements of fingers and disturbance from the arm. Experimental results demonstrate that the proposed method can directly estimate simple hand poses in real-time.

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Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

Cao

Wang

2019

Multimed Tools Appl

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Finger Localization and Classification in Images based on Generalized Hough Transform and Probabilistic Models

Cited by 12 publications

References 4 publications

Generalized Hough Transform for Object Classification in the Maritime Domain

Generalized Hough Transform for Object Classification in the Maritime Domain

Hand Gesture Recognition Based on the Parallel Edge Finger Feature and Angular Projection

Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

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