DeepAI AI Chat
Log In Sign Up

Automatic segmentation of lizard spots using an active contour model

by   Jhony Giraldo, et al.

Animal biometrics is a challenging task. In the literature, many algorithms have been used, e.g. penguin chest recognition, elephant ears recognition and leopard stripes pattern recognition, but to use technology to a large extent in this area of research, still a lot of work has to be done. One important target in animal biometrics is to automate the segmentation process, so in this paper we propose a segmentation algorithm for extracting the spots of Diploglossus millepunctatus, an endangered lizard species. The automatic segmentation is achieved with a combination of preprocessing, active contours and morphology. The parameters of each stage of the segmentation algorithm are found using an optimization procedure, which is guided by the ground truth. The results show that automatic segmentation of spots is possible. A 78.37 segmentation in average is reached.


page 5

page 8

page 9

page 11

page 13

page 14


Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images

Automatic segmentation of head and neck tumors plays an important role i...

VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic...

Mushroom image recognition and distance generation based on attention-mechanism model and genetic information

The species identification of Macrofungi, i.e. mushrooms, has always bee...

A Pyramid CNN for Dense-Leaves Segmentation

Automatic detection and segmentation of overlapping leaves in dense foli...

Textural Approach for Mass Abnormality Segmentation in Mammographic Images

Mass abnormality segmentation is a vital step for the medical diagnostic...