Combination of Canny Edge Detection and Blob Processing Techniques for Shrimp Larvae Counting This paper presents the proposed method to count the number of the shrimp larvae using computer technology based approach. The high demand of computer aids in counting Shrimp larvae are required in calculating survival rate of Shrimp larvae. Currently, the process of handling shrimp from post to adult larvae stage is still conducted with a tedious and laborintensive task especially in calculating the number of shrimp larvae. However, the use of the manual counting approach has suffered with some drawbacks such as inaccurate, time consuming, laborious, human intervention, sequential processes and overestimation. Since that, many researchers are interested into this the fundamental research and development challenges. In this study, we present an image processing technique and blob analysis for counting the number of shrimp larvae. The use of image processing technique helps for object counting specifically on image enhancement and image segmentation. Meanwhile, a Blob analysis algorithm is used as connected component to find and count shrimp larvae in the image through the experiment. The performance of the proposed method is evaluated by comparing the results with the manual counting system using all ninety samples dataset. All ninety samples of shrimp larvae (Penaeus vannamei) were obtained from the Freshwater Hatchery of University Malaysia Terengganu. The experimental results demonstrate that the proposed method is outperformed compared to the manual counting approach through the experiment by reducing the analysis time with minimizing the sequential process task.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates videoimage annotation tools for defining, training, and validation of datasets with videoimage enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deepsea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.