As a multi-billion dollar industry, scallop fisheries world-wide rely on maintaining healthy off-shore populations. Recent developments in the collection of optical images from extended areas of the ocean floor has opened the possibility of assessing scallop populations from imagery. The shear volume of data - upwards of 20,000 images per hour - implies that automatic image analysis is necessary. This paper presents a computer vision software system to identify and count scallops. For each image, the system generates initial candidate regions of potential scallops, extracts image features in the candidate regions, and then applies one of several different trained Adaboost classifiers to determine the strength of each region as a scallop. In making the final classification decision, the strength of the scallop classifier output is compared to the output of other classifiers trained to detect sand dollars, clams and other “distractors”.

We describe a novel application of support vector machines and multiscale texture and color invariants to a problem in biological oceanography: the identification of 6 species of bivalve larvae. Our data consists of polarized color images of scallop and other bivalve larvae (between 2 and 17 days old) collected from the ocean by a shipboard optical imaging system of our design. Larvae of scallops, clams, and oysters are small (100 microns) with few distinguishing features when observed under standard light microscopy. However, the use of polarized light with a full wave retardation plate produces a vivid color, birefringence pattern. The patterns display very subtle differences between species, often not discernable to human observers. We show that a soft-margin support vector machine with Gaussian RBF kernel is a good discriminator on a feature set extracted from Gabor wavelet transforms and color distribution angles of each image. By constraining the Gabor center frequencies to be low, the resulting system can attain classification accuracy in excess of 90% for vertically oriented images, and in excess of 80% for randomly oriented images.

This paper describes a novel application of multiscale texture invariants and statistical learning theory to the identification of 6 species of bivalve larvae in biological oceanography. Our data consists of polarized color images of scallop and other bivalve larvae (between 2 and 17 days old) collected from the ocean by a shipboard optical imaging system of our design. Larvae of scallops, clams, and oysters are small (100 microns) with few distinguishing features when observed under standard light microscopy. However, the use of polarized light with a full wave retardation plate produces a vivid color, bi-refringence pattern. The patterns display very subtle differences between species, often not discernable to human observers. Our texture invariants are extracted from Gabor wavelet transforms of each image. We show that by constraining the Gabor center frequencies to be low, the invariants can be optimized to capture subtle inter-species colored-texture differences. When used as input to a support vector machine classifier, the invariants provide classification of larvae to species with accuracy of 85%.