Age Group Estimation by Combining Texture and Fractal Analysis

Age Group Estimation by Combining Texture and Fractal Analysis

In this paper the age group estimation is presented based on combination of texture and fractal dimension features. The age of the human is used as one of the important key parameter for computer vision applications. The fractal dimension of the face image and the texture analysis is used to classify the age of the person into the three different groups such as child(10-20), young(21-50) and ol...

Texture analysis by multi-resolution fractal descriptors

This work proposes a texture descriptor based on fractal theory. The method is based on the Bouligand-Minkowski descriptors. We decompose the original image recursively into 4 equal parts. In each recursion step, we estimate the average and the deviation of the Bouligand-Minkowski descriptors computed over each part. Thus, we extract entropy features from both average and deviation. The propose...

Age Classification Combining Contour and Texture Feature

Age classification based on computer vision has widespread applications. Most of previous works only utilize texture feature or use contour and texture feature separately. In this paper, we proposed an age classification system that integrate contour and texture information. Besides, we improve the traditional Local Binary Pattern(LBP) feature extraction method and get pure texture feature. Sup...

Texture Segmentation via Haar Fractal Feature Estimation

Texture descriptor combining fractal dimension and artificial crawlers

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the environment and each other. Since this swarm system...