Skin colour classification using linear discriminant analysis and colour mapping co-occurrence matrix

Osman, Ghazali; Hitam, Muhammad Suzuri

doi:10.1109/iccat.2013.6522047

Cited by 6 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, Haralick features were primarily based on a Color Mapping Co-occurrence Matrix (CMCM). CMCM is a statistical feature used to describe texture characteristics in color images by examining the correlations and distributions between colors [23] Here, i and j represent the quantized color values for channel 1 and channel 2, channel 1 and channel 3, and channel 2 and channel 3, respectively. c. Normalize the joint histogram by dividing each element by the total number of color pairs in the patch to obtain a probability matrix.…”

Section: B Feature Extraction 1) Haralick Featurementioning

confidence: 99%

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

Liyuan,

Osman,

Abdul Rahman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Due to prolonged exposure to heavy train loads, various anomalies can emerge on the surface of railway tracks, posing a direct threat to safe train operation. The accurate and timely detection of these anomalies is important to ensure safe transportation and advancing intelligent maintenance. However, in the domain of anomaly detection, several challenges have arisen owing to the variability in illumination conditions, imaging blur inherent to the capture devices, and the introduction of noise from environmental factors such as dust particles. These interferences have significantly undermined the accuracy of rail anomaly detection based on target detection techniques. In response to these challenges, this study introduced a novel approach for rail anomaly detection in the presence of image artifacts by utilizing a multi-modal multi-task framework. The objective of this study is to enhance the performance of rail anomaly detection under interference-prone conditions. This study integrated color moment features, HU invariant moment features, and Haralick features to construct a fuzzy detection model for rail anomalies using a multi-task learning (MTL) strategy. The model prioritized the primary task of classifying rail anomalies, with interference level classification and fuzzy logic interference judgment serving as auxiliary tasks within the network. Finally, based on the results of fuzzy logic and interference level detection, a fuzzy judgment was made distinguishing between "with interference" and "without interference." Experimental findings consistently demonstrated that the integration of multi-modal features and multi-task learning methodologies significantly improves the accuracy of rail anomaly recognition in the presence of interference, thus establishing an effective approach for rail anomaly identification in challenging scenarios.

show abstract

Section: B Feature Extraction 1) Haralick Featurementioning

confidence: 99%

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

Liyuan,

Osman,

Abdul Rahman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…2 represents a set of images from Compaq dataset with GTs. This database is no longer available for public use [54,55].…”

Section: Standard Datasets and Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Survey on Human Skin Detection

Mahmoodi

Sayedi

2016

IJIGSP

View full text Add to dashboard Cite

Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

show abstract

“…The machine learning method detects "skin" pixels by building a predictive model from the input data. Such models, like Bayesian classifier, linear discriminant analysis, binary logistic regression, adaptive neurofuzzy inference system, etc., were successfully applied to skin detection [7,[22][23][24][25][26]. Among them, the Bayesian classifier is especially noteworthy not only in the field of skin detection but also in other disciplines because it provides the information concerning the probability that an observation belongs to a class, thereby evaluating the reliability of the result [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Nguyen‐Trang

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Skin detection is an interesting problem in image processing and is an important preprocessing step for further techniques like face detection, objectionable image detection, etc. However, its performance has not really been high because of the high overlapped degree between "skin" and "nonskin" pixels. This paper proposes a new approach to improve the skin detection performance using the Bayesian classifier and connected component algorithm. Specifically, the Bayesian classifier is utilized to identify "true skin" pixels using the first posterior probability threshold, which is approximate to 1, and to identify "skin candidate" pixels using the second posterior probability threshold. Subsequently, the connected component algorithm is used to find all the connected components containing the "skin candidate" pixels. According to the fact that a skin pixel often connects with other skin pixels in an image, all pixels in a connected component are classified as "skin" if there is at least one "true skin" pixel in that connected component. It means that the "nonskin" pixels whose color is similar to skin are classified as "nonskin" when they have the posterior probabilities lower than the first posterior probability threshold and do not connect with any "true skin" pixel. This idea can help us to improve the skin classification performance, especially the false positive rate.

show abstract

Skin colour classification using linear discriminant analysis and colour mapping co-occurrence matrix

Cited by 6 publications

References 24 publications

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

A Comprehensive Survey on Human Skin Detection

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Contact Info

Product

Resources

About