NDLI: Active contour image segmentation model with de-hazing constraints

Content Provider	IET Digital Library
Author	Ali, Haider Sher, Awal Saeed, Maryam Rada, Lavdie
Abstract	Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which makes the objects and their features difficult to be identified by computer vision systems. In the past decades, image de-hazing is used to remove the influence of weather factors and improve image visualisation in hazy scenes by providing easy image post-processing towards human assistance systems benefit. In this study, the authors present a variational segmentation model equipped with de-hazing constraint terms in a new coupled dehazing-segmentation model. The proposed hybrid formulation not only recovers/restores the fog/haze degradation but at the same time segments image degraded object/objects by solving in this way the difficulties of simultaneously performed dehazing and segmentation pre/post-processing. This combination takes into account the image structure boundaries and the image quality, leading in this way to a robust dehazing segmentation scheme. The advantages of the proposed method are the suitability of the model for grey and vector-valued images, a small number of parameters involved, and a rather good speed of the algorithm. Experiments show that their approach outperforms the state-of-the-art algorithms in terms of segmentation accuracy while avoiding a dehazing preprocessing which reflects an extended CPU time.
Starting Page	921
Ending Page	928
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5987
Journal	IET Image Processing
Publisher Date	2019-11-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Active Contour Image Segmentation Model Atmospheric Particles Computer Vision Computer Vision And Image Processing Technique Computer Vision System Coupled Dehazing-segmentation Model De-hazing Constraint De-hazing Constraint Term Dehazing Preprocessing Easy Image Postprocessing Fog Foggy Weather Condition Hazy Scenes Hazy Weather Condition Human Assistance System Image Colour Analysis Image De-hazing Image Denoising Image Enhancement Image Quality Image Segmentation Image Structure Boundaries Image Visualisation Optical, Image And Video Signal Processing Robust Dehazing-segmentation Scheme Variational Segmentation Model Vector-valued Image Weather Factors
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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