The technique of skin detection consists of three basic operations: initial segmentation, skin pixel prediction, and k-means clustering. The threshold value is used for initial segmentation, which is the average of TOTSU and TMAX.

face recognition errand by using k-means with radial basis function network technique. The facial recognition system has proposed to recognize t. e faces which are enrolled in the database and which are not the parts of t.

Aug 10, 2023 · Here’s a step-by-step guide to implementing K-Means for anomaly detection: Data Preprocessing: Start by preparing your dataset. Ensure that the features are appropriately scaled, and any...

Mar 26, 2018 · We proposed the extraction of the texture and shape characteristics of facial and iris modalities using a multi-scale local–global technique to collect the microtexton information of local primitives efficiently along with the global features with makeup and texture contact lenses.

Apr 1, 2023 · Many research efforts have been conducted and reported in literature with regard to improving the K-means algorithm’s performance and robustness. The current work presents an overview and taxonomy of the K-means clustering algorithm and its variants.

Dec 1, 2010 · The scheme is used to solve two cosmetic subjective measurements tasks, classification of cosmetic defects and detection of non-uniform color regions in a translucent film. The first problem is solved with two approaches supervised and …

In this paper we propose a new edge detector based on anisotropic linear filtering, local maximization and gamma correction. The novelty of this approach resides in the mixing of ideas coming...

Aug 18, 2017 · In this paper, we present a novel algorithm for k -mean clustering which exploits the data redundancy occurring in the dataset. The proposed algorithm performs computations for the available unique items in the dataset and uses its frequency to finalize the results.

Nov 1, 2021 · Various machine learning supervised classification methods such as Logistic Regression, Linear Support Vector Machine, Adaptive k-Nearest Neighbor, Artificial Neural Network and Decision...

K-means clustering is a clustering method where we define k clusters on the basis of the feature value of the objects to be grouped. We applied K-means 1-tier and 2-tier algorithm for creating clustering and detecting anomalies in dataset.