Extraction method of scallop areas considering bottom sediment of seabed

Enomoto, Koichiro; Toda, Masashi; Kuwahara, Yasuhiro

doi:10.1109/ut.2011.5774152

Cited by 2 publications

(10 citation statements)

References 4 publications

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“…First, the object image is performed the preprocessing [8]. Next, the seabed environment of the object image is identified by the bottom sediment classification.…”

Section: B Proposed Methodsmentioning

confidence: 99%

“…K. Enomoto have already proposed the algorithms to extract the scallop areas from the seabed images of gravel and sand, and shown the method's effectiveness [7], [8], because the scallop is looked different from each environments. These extraction rate accuracy are 95.0% [7] and 89.0% [8] in the scallop extraction algorithm for the fields of gravel and sand. Therefore, it is need to select a suitable scallop extraction algorithm on the basis of each field, and develop the bottom sediment classification method from the seabed image.…”

Section: A Seabed Environments and Scallopmentioning

confidence: 99%

“…We used 240 gravel images and 149 sand images in this experiment. These texture images were selected the upper left of the middle of the frame that have a margin 64 x 64 pixels, and its' size is 128 x 128 pixels [8]. Figure 6 shows the original samples in the fields of gravel and sand.…”

Section: Experi Mentsmentioning

confidence: 99%

“…In the sand field, the scallop areas are extracted by using following processes [8]. The candidate shelly rim pixels are extracted from the obtained image by dynamic threshold processing.…”

Section: Experi Mentsmentioning

confidence: 99%

“…K. Enomoto, et al have already proposed the algorithms to count the scallops from the seabed image of gravel and sand, because the scallop is looked different from each environments [7], [8]. They have already proposed the algorithms to extract the scallop areas from the seabed images of gravel and sand, because the scallop is looked different from each environments.…”

Section: Introductionmentioning

confidence: 99%

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

Enomoto

Toda

Kuwahara

2012

2012 International Symposium on Optomechatronic Technologies (ISOT 2012)

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Each related organization conducts various fishery investigations and collects data required for estimation of re source state. In the scallop culture industry in To koro, 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 method's effectiveness.

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“…First, the object image is performed the preprocessing [8]. Next, the seabed environment of the object image is identified by the bottom sediment classification.…”

Section: B Proposed Methodsmentioning

confidence: 99%

Section: A Seabed Environments and Scallopmentioning

confidence: 99%

Section: Experi Mentsmentioning

confidence: 99%

“…In the sand field, the scallop areas are extracted by using following processes [8]. The candidate shelly rim pixels are extracted from the obtained image by dynamic threshold processing.…”

Section: Experi Mentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Enomoto

Toda

Kuwahara

2012

2012 International Symposium on Optomechatronic Technologies (ISOT 2012)

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An Anchoring Capacity Study Focused on a Wheel’s Curvature Geometry for an Autonomous Underwater Vehicle with a Traveling Function during Contact with Loose Ground Containing Water

Ofuchi,

Fujiwara,

Iizuka

2024

Geotechnics

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The current scallop fishery sector allows many scallops to remain in specified fishing zones, and this process leads to heavy losses in the sector. Scallop fishermen aim to harvest the remaining scallops to reduce their losses. To achieve this, a fisherman must understand the scallop ecology on the seafloor. In our previous study, we proposed a method for measuring scallops using wheeled robots. However, a wheeled robot must be able to resist disturbance from the sea to achieve high measurement accuracy. Strong anchoring of wheels against the seafloor is necessary to resist disturbance. To better understand anchoring performance, we confirmed the wheel anchoring capacity in water-containing sand in an experiment. In this experiment, we towed fixed wheels on water-containing sand and measured the resistance force acting between the wheel and the sand. Afterward, we considered the resistance force as the wheel anchoring capacity on the water-containing sand. The experimental results capture the tendency for the anchoring capacity of sand with/without water to increase with sinkage. The results also demonstrate that the anchoring capacity of water-containing sand is lower than that of non-water-containing sand. However, the results indicate that when the wheels possess lugs, their presence tends to increase the wheels’ anchoring capacity in water.

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Extraction method of scallop areas considering bottom sediment of seabed

Cited by 2 publications

References 4 publications

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

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

An Anchoring Capacity Study Focused on a Wheel’s Curvature Geometry for an Autonomous Underwater Vehicle with a Traveling Function during Contact with Loose Ground Containing Water

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