Enhanced methods for Saimaa ringed seal identification
Chehrsimin, Tina (2016)
Diplomityö
Chehrsimin, Tina
2016
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2016110327145
https://urn.fi/URN:NBN:fi-fe2016110327145
Tiivistelmä
Computer vision techniques have opened doors for practical, reliable and much safer
methods for monitoring the animal populations in comparison with traditional methods.
Old methods such as capturing the animals and installing sensors on their bodies are time
consuming and may cause stress to the animals and affect their behavior. In this study,
an automatic image based identification algorithm to identify endangered Saimaa ringed
seals is proposed. The identification algorithm consists of three main steps including
image segmentation, image enhancement, and identification. The image segmentation
method contains two steps: unsupervised segmentation and classification of the superpixels.
The algorithm provides promising results by using a combination of different feature
descriptors and classifiers. In the enhancement step, morphological operations, contrast
enhancement, and color normalization are applied for segmented images to increase the
performance. In the identification step, two algorithms were tested: Wild ID and Hot
spotter. The identification algorithms were implemented on original images, segmented
images and enhanced segmented images. The best results were obtained using Hot spotter
tested with the enhanced segmented images. With a challenging data set used, 44% of the
seals were correctly identified within the first match. Meanwhile, 66% of the cases, the
correct seals were one of the 20 best matches.
methods for monitoring the animal populations in comparison with traditional methods.
Old methods such as capturing the animals and installing sensors on their bodies are time
consuming and may cause stress to the animals and affect their behavior. In this study,
an automatic image based identification algorithm to identify endangered Saimaa ringed
seals is proposed. The identification algorithm consists of three main steps including
image segmentation, image enhancement, and identification. The image segmentation
method contains two steps: unsupervised segmentation and classification of the superpixels.
The algorithm provides promising results by using a combination of different feature
descriptors and classifiers. In the enhancement step, morphological operations, contrast
enhancement, and color normalization are applied for segmented images to increase the
performance. In the identification step, two algorithms were tested: Wild ID and Hot
spotter. The identification algorithms were implemented on original images, segmented
images and enhanced segmented images. The best results were obtained using Hot spotter
tested with the enhanced segmented images. With a challenging data set used, 44% of the
seals were correctly identified within the first match. Meanwhile, 66% of the cases, the
correct seals were one of the 20 best matches.