A 3D machine vision system for quality grading of Atlantic salmon

No abstract is available for this paper.

• Publication year

  2016

• Venue

  Computers and Electronics in Agriculture

• Publication date

  2016-04-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/j.compag.2016.02.020
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Transformation for Extracting New De scriptors of Shape ' , in

  2017cited by this paper
• Skeletal Anomaly Monitoring in Rainbow Trout (Oncorhynchus mykiss, Walbaum 1792) Reared under Different Conditions

  2014cited by this paper
• Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis

  2013cited by this paper
• Vertebral deformities in farmed Atlantic salmon (Salmo salar L.) – etiology and pathology

  2012cited by this paper
• LIBSVM: A library for support vector machines

  2011cited by this paper
• Quantification of gaping, bruising, and blood spots in salmon fillets using image analysis.

  2011cited by this paper
• Atlantic salmon bath challenged with Moritella viscosa--pathogen invasion and host response.

  2009cited by this paper
• Towards a classification and an understanding of developmental relationships of vertebral body malformations in Atlantic salmon (Salmo salar L.).

  2009cited by this paper
• Quality grading of Atlantic salmon (Salmo salar) by computer vision.

  2008cited by this paper
• A Practical Guide to Support Vector Classication

  2008cited by this paper
• A radiological study on the development of vertebral deformities in cultured Atlantic salmon (Salmo salar L.)

  2007cited by this paper
• AUTOMATIC WEIGHT AND QUALITY GRADING OF WHOLE PELAGIC FISH

  2006cited by this paper
• Computer vision based sorting of atlantic salmon (SALMO SALAR) according to size and shape

  2006cited by this paper
• Compressed vertebrae in Atlantic salmon Salmo salar: evidence for metaplastic chondrogenesis as a skeletogenic response late in ontogeny.

  2005cited by this paper
• Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

  2003cited by this paper
• Demosaicking methods for Bayer color arrays

  2002cited by this paper
• Multiple View Geometry in Computer Vision

  2001cited by this paper
• Risk factors for spinal deformities in Atlantic salmon, Salmo salar L.

  1998cited by this paper
• The quickhull algorithm for convex hulls

  1996cited by this paper
• Voronoi diagrams—a survey of a fundamental geometric data structure

  1991cited by this paper
• Marching cubes: A high resolution 3D surface construction algorithm

  1987cited by this paper

• Computer Vision-Based Research Progress on Biomass Statistics and Health Status Assessment of Fish and Shrimp

  2025cites this paper
• Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach

  2024cites this paper
• Machine Vision and Robotics for Primary Food Manipulation and Packaging: A Survey

  2024cites this paper
• Framework for Automated Wound Detection and Tracking in Industrial Scale Fish Farms

  2024cites this paper
• TECHNICAL SOLUTIONS FOR BIOMASS ESTIMATION ACCORDING TO THE CONCEPT OF AQUACULTURE 4.0

  2024cites this paper
• The impact of varying EPA:DHA ratio on Atlantic salmon health and welfare

  2023cites this paper
• In-Water Fish Body-Length Measurement System Based on Stereo Vision

  2023cites this paper
• Digital scoring of welfare traits in Atlantic salmon (Salmo salar L.) - a proof of concept study quantifying dorsal fin haemorrhaging via hyperspectral imaging

  2023cites this paper
• The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

  2023cites this paper
• Genomic selection and its research progress in aquaculture breeding

  2022cites this paper
• Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network

  2022cites this paper
• Cutting Techniques in the Fish Industry: A Critical Review

  2022cites this paper
• Automated metal sorting system based on weight and colour of the specimen

  2022cites this paper
• Counting assistant through multi-sensor fusion for inventory monitoring

  2022cites this paper
• Novel Techniques for Quality Evaluation of Fish: A Review

  2021cites this paper
• Measuring internal quality traits in egg by 3D laser imaging

  2021cites this paper
• Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

  2020cites this paper
• Food and feed production

  2020cites this paper
• Machine Vision Based Macro Measurement System Detection

  2020cites this paper
• Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features

  2020cites this paper
• Design and in-house validation of a portable system for the determination of free acidity in virgin olive oil

  2019cites this paper
• Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

  2019influential citation
• Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence

  2019cites this paper
• Machine Vision System: Food Industry Applications and Practices

  2019cites this paper
• Automatic sea squirt sorting algorithm based on the HSV color model and weight estimation

  2018cites this paper
• Automatic Bluefin Tuna sizing using a stereoscopic vision system

  2018cites this paper
• Enhanced Vineyard Descriptors combining UAV 2D and 3D Crop Models

  2018cites this paper
• The use of 3D scanning to determine the weight of the chicken breast

  2018cites this paper
• Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture

  2018cites this paper
• A system for classifying vegetative structures on coffee branches based on videos recorded in the field by a mobile device

  2017cites this paper