Mr. Chi Huang Profile

Chi Huang

at Texas A&M Univ Corpus Christi

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Publications (2)

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Proceedings Article | 7 June 2024 Presentation + Paper

Age-based clustering of seagrass blades using AI models

Omer Sevinc, Mehrube Mehrubeoglu, Kirk Cammarata, Chi Huang, Lifford McLauchlan

Proceedings Volume 13034, 130340F (2024) https://doi.org/10.1117/12.3014915

KEYWORDS: Machine learning, Artificial intelligence, Deep learning, Feature extraction, Image classification, Evolutionary algorithms, Ecosystems, Biological samples, Environmental monitoring

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Seagrass ecosystems play a vital role in maintaining marine biodiversity and ecological balance, making their monitoring and management essential. This study proposes a novel approach for clustering of seagrass images into three distinct age categories: young, medium, and old, using deep learning and unsupervised machine learning techniques. VGG-16 convolutional neural networks (CNN) are employed for feature extraction from the seagrass images, followed by K-means clustering to categorize the image samples into the specified age groups. The implemented methodology begins with the collection and annotation of a diverse seagrass image dataset, including samples from various locations and conditions. Images are first pre-processed to ensure consistent size and quality. To enable real-time capabilities, an optimized VGG-16 CNN is then fine-tuned on the annotated dataset to learn discriminative features that capture age-related characteristics of the seagrass leaves within the constraints of real-time image processing. After feature extraction, the Kmeans clustering algorithm is applied to group the images into young, medium, and old categories based on the learned features. The clustering results are evaluated using quantitative metrics such as the silhouette score and Davies-Bouldin index, demonstrating the effectiveness of the proposed method in capturing age-related patterns in seagrass imagery. This research contributes to the field of seagrass monitoring by providing an automated and real-time approach to classifying seagrass images into age categories which can facilitate more accurate assessments of seagrass health and growth dynamics. A real-time capability would equip decision-makers with a valuable tool for immediate responses and support the sustainable management of seagrass ecosystems in various marine environments.

Proceedings Article | 16 April 2021 Presentation + Paper

Segmentation of seagrass blade images using deep learning

Mehrube Mehrubeoglu, Isaac Vargas, Chi Huang, Kirk Cammarata

Proceedings Volume 11736, 1173605 (2021) https://doi.org/10.1117/12.2587057

KEYWORDS: Image segmentation, Neural networks, Image processing, Classification systems

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Segmentation of individual seagrass images is of importance to biologists who are investigating individual seagrass blade cover to correlate the surface cover information to benthic environmental factors. Seagrasses may be covered with epiphytes like crustose and filamentous algae and tubeworms, all bioindicators of nutrient and turbidity conditions of the seagrass environment. Classical image processing techniques to segment seagrasses have been successful; however, such techniques are relatively time consuming. We introduce deep learning as a computationally efficient approach to perform semantic segmentation in multiple seagrass images to determine each blade’s percent cover and surface composition. Pre-trained ResNet-18 and ResNet-50 convolutional neural networks have been adapted using transfer learning to classify seagrass blade surface composition. Seagrass surface semantic segmentation and mapping is achieved for five classes including the bare seagrass blade (no cover), general epiphyte, tubeworm, filamentous algae, and background. We present the application of deep learning in two convolutional neural networks to achieve semantic segmentation of seagrass blades as a fast tool for seagrass surface classification. Classification accuracy and computational performance of the two deep CNN are presented.

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