An automated fish species classification and migration monitoring system

Lee, D.J.; Redd, S.; Schoenberger, Robert B.; Xu, Xiaoqian; Zhan, Peng

doi:10.1109/iecon.2003.1280195

Cited by 40 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system has reported classification accuracy around 78%. D. J. Lee et al [22], removed edge noise and redundant data points through the development of a shape analysis algorithm. Critical landmark points were located using an algorithm of curvature function analysis.…”

Section: Related Workmentioning

confidence: 99%

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Rathi

Jain

Indu

2017

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)

132

View full text Add to dashboard Cite

The target of this paper is to recommend a way for Automated classification of Fish species. A high accuracy fish classification is required for greater understanding of fish behavior in Ichthyology and by marine biologists. Maintaining a ledger of the number of fishes per species and marking the endangered species in large and small water bodies is required by concerned institutions. Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other water bodies in images, image quality and occlusion. This method uses a novel technique based on Convolutional Neural Networks, DeepLearning and Image Processing to achieve an accuracy of 96.29%. This method ensures considerably discrimination accuracy improvements than the previously proposed methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Rathi

Jain

Indu

2017

2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)

132

View full text Add to dashboard Cite

show abstract

“…Cullinan et al defined four classes (bat, gull, tern, swallow), and using flight tracks reported 82% correct classification; however, this does not consider fine-grained differentiation. Lee et al (2003) used shape contour features to discriminate between nine fish species, and achieved a classification rate of between 13% and 80%. Spampinato et al (2010) also used texture and shape features, achieving a 92% correct rate.…”

Section: Studies Related To Other Speciesmentioning

confidence: 99%

Classification of bird species from video using appearance and motion features

Atanbori

Duan

Shaw³

et al. 2018

Ecological Informatics

View full text Add to dashboard Cite

The monitoring of bird populations can provide important information on the state of sensitive ecosystems; however, the manual collection of reliable population data is labour-intensive, time-consuming, and potentially error prone. Automated monitoring using computer vision is therefore an attractive proposition, which could facilitate the collection of detailed data on a much larger scale than is currently possible.A number of existing algorithms are able to classify bird species from individual high quality detailed images often using manual inputs (such as a priori parts labelling). However, deployment in the field necessitates fully automated in-flight classification, which remains an open challenge due to poor image quality, high and rapid variation in pose, and similar appearance of some species. We address this as a fine-grained classification problem, and have collected a video dataset of thirteen bird classes (ten species and another with three colour variants) for training and evaluation. We present our proposed algorithm, which selects effective features from a large pool of appearance and motion features. We compare our method to others which use appearance features only, including image classification using state-ofthe-art Deep Convolutional Neural Networks (CNNs). Using our algorithm we achieved an 90% correct classification rate, and we also show that using effectively selected motion and appearance features together can produce results which outperform state-of-the-art single image classifiers. We also show that the most significant motion features improve correct classification rates by 7% compared to using appearance features alone.

show abstract

“…We modified the relevance measure, K, for the curve evolution method in [28][29][30] to remove redundant points while maintaining the significance of the contours. The new relevance measure is shown in (1) where β is the turn angle on the vertex between line segments s 1 and s 2 and l(s 1 ) and l(s 2 ) are the normalized length from the vertex to the two adjacent vertices [33][34][35]. This modified curve evolution method reduces short, straight line segments that provide little information about the overall shape of the object.…”

Section: Data Point Reductionmentioning

confidence: 99%

<title>Shape-based human detection for threat assessment</title>

et al. 2004

View full text Add to dashboard Cite

Detection of intrusions for early threat assessment requires the capability of distinguishing whether the intrusion is a human, an animal, or other objects. Most low-cost security systems use simple electronic motion detection sensors to monitor motion or the location of objects within the perimeter. Although cost effective, these systems suffer from high rates of false alarm, especially when monitoring open environments. Any moving objects including animals can falsely trigger the security system. Other security systems that utilize video equipment require human interpretation of the scene in order to make real-time threat assessment. Shape-based human detection technique has been developed for accurate early threat assessments for open and remote environment. Potential threats are isolated from the static background scene using differential motion analysis and contours of the intruding objects are extracted for shape analysis. Contour points are simplified by removing redundant points connecting short and straight line segments and preserving only those with shape significance. Contours are represented in tangent space for comparison with shapes stored in database. Power cepstrum technique has been developed to search for the best matched contour in database and to distinguish a human from other objects from different viewing angles and distances.

show abstract

An automated fish species classification and migration monitoring system

Cited by 40 publications

References 11 publications

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Classification of bird species from video using appearance and motion features

<title>Shape-based human detection for threat assessment</title>

Contact Info

Product

Resources

About