Pixel‐wise skin colour detection based on flexible neural tree

Xu, Tao; Wang, Yunlong; Zhang, Zhaoxiang

doi:10.1049/iet-ipr.2012.0657

Cited by 18 publications

(17 citation statements)

References 37 publications

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“…Most of the skin detectors [44][45][46][47] work on pixel level. In this paper, the per-pixel Naive Bayes skin detector [44] is extended to be applicable for super-pixels.…”

Section: Skin Detectionmentioning

confidence: 99%

Parsing fashion image into mid‐level semantic parts based on chain‐conditional random fields

Wang

Zhao

Yin

et al. 2016

IET Image Processing

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In this study, the authors address the problem of parsing fashion images into mid-level semantic parts including upper-clothing, lower-clothing, skin, hair and background. These mid-level parts provide the regional information of fashion items and have potential value in high-level parsing process. The key idea of the method is to parse the midlevel parts by region expanding. Owing to the co-occurrence of pose skeleton and the proposed parts, the region expanding process starts from the super-pixels crossed by specific segments of pose skeleton. The super-pixels are then merged with their neighbours by conditional inference based on their position and perceptual similarity. To avoid the difficulties of training on arbitrary graph structures, conditional random fields (CRFs) are constructed on super-pixel chains, which are extracted from the generated expanding trees. This is followed by a voting stage to mix up the probabilities estimated by the chain-CRFs to obtain the final result. Experiments on two datasets show that the new method outperforms related approaches in regional accuracy and has good generalisation capability. Furthermore, the method can be easily employed to improve the performance of high-level parsing. Its effectiveness has been verified by another group of experiments on two state-of-the-art high-level parsing approaches.

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“…Most of the skin detectors [44][45][46][47] work on pixel level. In this paper, the per-pixel Naive Bayes skin detector [44] is extended to be applicable for super-pixels.…”

Section: Skin Detectionmentioning

confidence: 99%

Parsing fashion image into mid‐level semantic parts based on chain‐conditional random fields

Wang

Zhao

Yin

et al. 2016

IET Image Processing

View full text Add to dashboard Cite

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“…The condition likewise computes the time it takes to totally consume the image because of sun consume. The second part is completely programmed skin investigation module used to perform order, highlight extraction and so on [7]proposes pixel insightful skin colour identification in light of neural system strategy. The value of the examination is demonstrated through test inquire about.…”

Section: Introductionmentioning

confidence: 99%

Study of Techniques Used For Segmentation and Classification of Skin Lesion

Kaur

Kapoor²

2017

ijsrm

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Skin lesion is the piece of the skin having strange development. The piece of skin seeming diverse then different parts of the skin is by and large falls into the class of skin lesion. The proposed work research the use of two calculations to shape half and half approach, Support vector machine(SVM) and Fuzzy Filtering instrument to distinguish skin lesion from the pictures exhibited. The writing utilizes shaded picture set gotten from the web. The proposed work starts by pre handling of picture i.e evacuating superfluous ancient rarities, for example, hair, shading influences and so on. Limit of picture is recognized through the following stage known as division. In the following stage fluffy separating is utilized to recognize the elements and arrangement is performed. The reenactment is led in MATLAB utilizing the utilization of picture preparing tool kit. The outcomes got are contrasted with existing division approaches with distinguish exactness of result as far as skin lesion discovery.

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“…This shortcoming seriously hampers the practical application of this technology as an help for diagnostic which could be done by dermatologists or even general practitioners. Fortunately, previous researches have shown that GA-based designs can be optimized via different parallel computing environments [2,13,14,36]. For KMGA, the huge quantity of data from multispectral images processing and GA's population information results in a bottleneck of memory consumption on GPUs and FPGAs, while multi-core CPUs have better overall properties, specially for efficiency and robustness performances.…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of a parallel and optimized light–tissue interaction-based method for fast skin lesion assessment

Brost

Benezeth

et al. 2015

J Real-Time Image Proc

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In recent years, image processing technics have attracted much attention as powerful tools in the assessment of skin lesions from multispectral images. The Kubelka-Munk Genetic Algorithm (KMGA) is a novel method which has been developed for this diagnostic purpose. It combines the Kubelka-Munk light-tissue interaction model with the Genetic Algorithm optimization process, and allows quantitative measure of cutaneous tissue by computing skin parameter maps such as melanin concentration, volume blood fraction, oxygen saturation or epidermis/dermis thickness. However, its efficiency is seriously reduced by the mass floating-point operations for each pixel of the multispectral image, and this prevents the algorithm from reaching industrial standards related to cost, power and speed for clinical applications. In this paper, our work focuses on the improvement of this theoretical achievement. Therefore, we repropose a new C-based Parallel and Optimized KMGA (PO-KMGA) technique designed and optimized using multiple ways: KM model optimized re-writing, operation massively parallelized using POSIX threads, memory use optimization and routine pipelining with Intel C?? Compiler, etc.Intensive experiments demonstrate that our introduced PO-KMGA framework spends less than 10 min to finish a job that the conventional KMGA spends around two days to do in the same hardware environment with a similar algorithm performance.

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Pixel‐wise skin colour detection based on flexible neural tree

Cited by 18 publications

References 37 publications

Parsing fashion image into mid‐level semantic parts based on chain‐conditional random fields

Parsing fashion image into mid‐level semantic parts based on chain‐conditional random fields

Study of Techniques Used For Segmentation and Classification of Skin Lesion

Design and evaluation of a parallel and optimized light–tissue interaction-based method for fast skin lesion assessment

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