Koichiro Enomoto

Scallop Detection from Sand-Seabed Images for Fishery Investigation

We propose a method of extracting scallop areas from sand seabed images to assess fish resources, and developed an automatic system that measures their quantities, sizes, and states for fisheries management. In the gravel field, we can see scallops feature, such as color, fluted patterns and shaped like fans. But, it is difficult for the scallop is coverd with sand in the sand field. The present study is described our extracting method using limited features, and presented the result and its effectiveness.

Bottom sediment classification method from seabed image for automatic counting system of scallop

Each related organization conducts various fishery investigations and collects data required for estimation of resource state. In the scallop culture industry in Tokoro, Japan, the fish resources are investigated by analyzing seabed images. The seabed images are now obtainable from catamaran technology. However, there is no automatic technology to measure data from these images, and so the current investigation technique is the manual measurement by experts. The scallop is looked different from each environment. Therefore, a suitable algorithm to extract the scallop area depends on the bottom sediments of the seabed image. In this paper, we propose a method to classify the bottom sediments of the seabed image. For bottom sediment classification, we forge a strong classifier from weak classifiers using AdaBoost using the various texture features. This paper describes a method to classify the bottom sediments, presents the comparison of the effectiveness of the texture features and the results. Moreover, we presents the experiments results of the scallop counting based on the proposed method, and evaluate the methods effectiveness.

Extraction method of scallop areas considering bottom sediment of seabed

Each related organization conducts various fishery investigations and collects data required for estimation of resource state. In the scallop culture industry in Abashiri, Japan, the fisheries are investigated by analyzing seabed images. Seabed images are now obtainable from catamaran technology. However, there is no automatic technology to measure data from these images, and so the current investigation technique is the manual measurement by experts. The scallop lives on seabed of gravel, granule, ballas, coarse sand, and fine sand. The scallop is looked different from each environments. In fine sand fields, the scallop areas have only the shelly rim features in sand field, because the scallop is covered with sand. It is needed to extract the scallop area that the field of seabed image is identified because the method is selected by the fields. In this paper, we propose a method to classify the bottom sediments of the seabed image using the gray level co-occurrence matrix (GLCM). Moreover, we describe a method to extract scallop areas in fine sand field using the result of the bottom sediments, present the results, and evaluate the methods effectiveness.

The amount of Alaria praelonga Kjellmans analysis method from laminaria bed images for fishery investigation

The purpose of this research is to develop automated system for measuring the amount of target fishery resources from images which are taken with underwater camera for fishery investigation in laminaria beds in Nemuro, Japan. The image analysis technology for measuring the amount of target in laminaria beds in Nemuro is needed to develop it. In laminaria beds in Nemuro, there is no efficient method to mesuring “Needless Seaweed” which prevent the growth of laminaria. Alaria praelonga Kjellmen is one of Needless Seaweeds which has a vein on the surface. In this paper, we explain about extraction method of this vein to mesure Alaria praelonga Kjellmen from the image.

Extraction method of scallop area in seabed images for fishery resources investigation

In this research, we propose a method to extract scallop areas in seabed images in order to construct a system that can measure automatically the number, size, and state of fishery resources, especially scallops, by analyzing seabed images. Our algorithm is based on information on the hue and characteristic pattern scallop shells. The effectiveness of the proposed method is illustrated through an experiment.