Application and Evaluation of Texture-Adaptive Skin Detection in TV Image Enhancement

被引:0
|
作者
Zafarifar, Bahman [1 ]
Bellers, Erwin B. [1 ]
de With, Peter H. N. [2 ]
机构
[1] Sigma Designs, Eindhoven, Netherlands
[2] Eindhoven Univ Technol, Eindhoven, Netherlands
来源
2013 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | 2013年
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper evaluates a case study where a previously reported texture-adaptive skin detection algorithm is applied for TV image enhancement. A color-only skin detector of an existing high-end TV chip is extended with a texture feature, enabling exclusion of skin-colored textured areas. We report the performance in terms of detection result, and in terms of image quality in a cascade of three image enhancement functions. In terms of detection score, at 80% true positive rate, the false positive rate of the texture-adaptive skin detector is 29% lower than that of the color-only skin detector, forming a clear improvement. With respect to its application in enhancement, we assess the enhancement quality by measuring the RMS error of the enhancement output compared to an optimally enhanced image based on ground-truth skin areas. When using the texture-adaptive skin detector, the enhancement RMS error is 44% lower than the RMS error when using the color-only skin detector, thereby confirming the applicability of the proposal. Subjective evaluation indicates that the proposed algorithm is better suitable for mid/high-frequency boosting applications like sharpness enhancement, and less suitable for enhancements that operate on low frequencies like color correction functions.(1)
引用
收藏
页码:88 / 91
页数:4
相关论文
共 50 条
  • [1] Texture-adaptive Skin Detection for TV and its Real-time Implementation on DSP and FPGA
    Zafarifar, Bahman
    van den Kerkhof, Tim
    de With, Peter H. N.
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2012, 58 (01) : 161 - 169
  • [2] Deep Texture-adaptive Image Denoising for Practical Application
    Woo S.-M.
    Lee S.-E.
    Kim J.-O.
    IEIE Transactions on Smart Processing and Computing, 2022, 11 (06): : 412 - 420
  • [3] Texture-adaptive image colorization framework
    Michal Kawulok
    Bogdan Smolka
    EURASIP Journal on Advances in Signal Processing, 2011
  • [4] Texture-adaptive image colorization framework
    Kawulok, Michal
    Smolka, Bogdan
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2011, : 1 - 15
  • [5] Improved Skin Segmentation for TV Image Enhancement, Using Color and Texture Features
    Zafarifar, Bahman
    Martiniere, Anthony
    de With, Peter H. N.
    2010 DIGEST OF TECHNICAL PAPERS INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS ICCE, 2010,
  • [6] Texture image classification using improved image enhancement and adaptive SVM
    Lydia Binti Abdul Hamid
    Anis Salwa Mohd Khairuddin
    Uswah Khairuddin
    Nenny Ruthfalydia Rosli
    Norrima Mokhtar
    Signal, Image and Video Processing, 2022, 16 : 1587 - 1594
  • [7] Texture image classification using improved image enhancement and adaptive SVM
    Hamid, Lydia Binti Abdul
    Khairuddin, Anis Salwa Mohd
    Khairuddin, Uswah
    Rosli, Nenny Ruthfalydia
    Mokhtar, Norrima
    SIGNAL IMAGE AND VIDEO PROCESSING, 2022, 16 (06) : 1587 - 1594
  • [8] Objective Evaluation of Skin Texture Condition by Image Analysis
    Tanaka, Toshiyuki
    Suzuta, Haruna
    16TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, 2017, 61 : 7 - 12
  • [9] Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details
    Jia, Xiang-Yu
    Dongye, Chang-Lei
    NONLINEAR PROCESSES IN GEOPHYSICS, 2020, 27 (02) : 253 - 260
  • [10] REGION-ADAPTIVE TEXTURE ENHANCEMENT FOR DETAILED PERSON IMAGE SYNTHESIS
    Yang, Lingbo
    Wang, Pan
    Zhang, Xinfeng
    Wang, Shanshe
    Gao, Zhanning
    Ren, Peiran
    Xie, Xuansong
    Ma, Siwei
    Gao, Wen
    2020 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2020,