James W. Seager

A review of techniques for the identification and measurement of fish in underwater stereo-video image sequences

Underwater stereo-video measurement systems are used widely for counting and measuring fish in aquaculture, fisheries and conservation management. To determine population counts, spatial or temporal frequencies, and age or weight distributions, snout to fork length measurements are captured from the video sequences, most commonly using a point and click process by a human operator. Current research aims to automate the measurement and counting task in order to improve the efficiency of the process and expand the use of stereo-video systems within marine science. A fully automated process will require the detection and identification of candidates for measurement, followed by the snout to fork length measurement, as well as the counting and tracking of fish. This paper presents a review of the techniques used for the detection, identification, measurement, counting and tracking of fish in underwater stereo-video image sequences, including consideration of the changing body shape. The review will analyse the most commonly used approaches, leading to an evaluation of the techniques most likely to be a general solution to the complete process of detection, identification, measurement, counting and tracking.

Modelling geoid undulations with an artificial neural network

Examines the use of a backpropagation neural network to model geoid undulations. Modelling of the Earths gravity field, and in particular the separation between ellipsoid and geoid surface, is one of the fundamental problems in the field of geodesy. Geoid undulations are important for relating heights derived from the satellite based Global Positioning System to orthometric heights, which determine the flow of water. Modelling of geoid undulations has been traditionally done using Stokes integral, least squares collocation, or by fast Fourier transforms. The paper presents the results of preliminary investigations which suggest the backpropagation neural network provides a useful tool for geoid undulation modelling.

Automatic recognition of coded targets based on a Hough transform and segment matching

This paper will present details of a coded target system that employs a Hough transform and segment matching to automatically recognise and identify the targets in digital images. The code system is based on a square surrounding the central circular target and will be described at a level of detail that would allow the system to be readily duplicated. Pre-detection processes, developed to improve the success rate under unfavourable conditions, and the tests conducted to validate a correct target match will also be described. Finally, the paper will include some examples of the use of the coded targets, drawn from calibrations of digital still cameras and underwater stereo-video systems.

Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements

Underwater stereo-video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. Length measurements are generated from stereo-video recordings by a software operator using a mouse to locate the head and tail of a fish in synchronized pairs of images. This data can be used to compare spatial and temporal changes in the mean length and biomass or frequency distributions of populations of fishes. Since the early 1990s stereo-video has also been used for measuring the lengths of fish in aquaculture for quota and farm management. However, the costs of the equipment, software, the time, and salary costs involved in post processing imagery manually and the subsequent delays in the availability of length information inhibit the adoption of this technology. We present a semi-automatic method for capturing stereo-video measurements to estimate the lengths of fish. We compare the time taken to make measurements of the same fish measured manually from stereo-video imagery to that measured semi-automatically. Using imagery recorded during transfers of Southern Bluefin Tuna (SBT) from tow cages to grow out cages, we demonstrate that the semi-automatic algorithm developed can obtain fork length measurements with an error of less than 1% of the true length and with at least a sixfold reduction in operator time in comparison to manual measurements. Of the 22 138 SBT recorded we were able to measure 52.6% (11 647) manually and 11.8% (2614) semi-automatically. For seven of the eight cage transfers recorded there were no statistical differences in the mean length, weight, or length frequency between manual and semi-automatic measurements. When the data were pooled across the eight cage transfers, there was no statistical difference in mean length or weight between the stereo-video-based manual and semi-automated measurements. Hence, the presented semi-automatic system can be deployed to significantly reduce the cost involved in ad

Influence of Bayer filters on the quality of photogrammetric measurement

Bayer colour filter arrays (CFA) are commonly used to obtain digital colour imagery from a single-chip CCD or CMOS camera. Colour information is captured via a regular array of colour filters placed over the image sensor, and the full colour image is reconstructed in a demosaicing process. Colour imagery derived in such a way is prone to visual artefacts including false colours, poor edge definition and a loss of image and colour sharpness. Such artefacts are suspected of degrading the quality of photogrammetric measurements made from demosaiced images. An approach to demosaicing based on the use of tuneable Gaussian filters is proposed. The new approach is designed to minimise image artefacts and is specifically aimed at improving the quality of photogrammetric measurements made with the demosaiced imagery. Results are given for a specific application of Bayer CFA cameras to underwater stereo length measurement of fish. The results show a reduction in visual artefacts and an improvement in the quality of stereo measurements.

Linearisation without calculus—a network adjustment example

Abstract There are many instances in the application of mathematics where linearisation, or differentiation, of complex equations is required. One such example is with survey network adjustments, where observation equations for survey measurements are linearised for a least squares solution of coordinates. The usual method of evaluating these derivatives is to linearise the observation equations using traditional calculus “rules”. The linearisation problem may be approached alternatively by approximating the differentiation numerically, avoiding any calculus derivatives. This paper shows how the principle of numerical evaluation of derivatives can be applied to network adjustment, in this case variation of coordinates on the Australian Map Grid (AMG). An investigation into the efficiencies of numerical evaluation of derivatives compared to calculus is also provided.