Automatic In Situ Identification of Plankton

Earth's oceans are a soup of living micro-organisms known as plankton. As the foundation of the food chain for marine life, plankton are also an integral component of the global carbon cycle which regulates the planet's temperature. In this paper, we present a technique for automatic identification of plankton using a variety of features and classification methods including ensembles. The images were obtained in situ by an instrument known as the flow cytometer and microscope (FlowCAM), that detects particles from a stream of water siphoned directly from the ocean. The images are of necessity of limited resolution, making their identification a rather difficult challenge. We expect that upon completion, our system will become a useful tool for marine biologists to assess the health of the world's oceans.

[1]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[2]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[3]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[5]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Francesca Odone,et al.  Image Kernels , 2002, SVM.

[7]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Paul L. Rosin,et al.  A new convexity measure for polygons , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  P. Culverhouse,et al.  Do experts make mistakes? A comparison of human and machine identification of dinoflagellates , 2003 .

[11]  Allen R. Hanson,et al.  On multi-scale differential features and their representations for image retrieval and recognition , 2003 .

[12]  Charles S. Yentsch,et al.  9---p An imaging-inflow system for automated analysis of marine microplankton , 2006 .

[13]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[14]  Thomas G. Dietterich Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[15]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  H. D. Buf,et al.  Automatic diatom identification , 2002 .

[17]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[18]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[19]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[20]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[21]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[22]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[23]  Roderic A. Grupen,et al.  Visual feature learning , 2001 .

[24]  Thomas G. Dietterich The Handbook of Brain Theory and Neural Networks , 2002 .

[25]  Phil F. Culverhouse,et al.  Expert and machine discrimination of marine flora: a comparison of recognition accuracy of field-collected phytoplankton , 2003 .

[26]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[27]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[28]  Bernd Jähne,et al.  BOOK REVIEW: Digital Image Processing, 5th revised and extended edition , 2002 .

[29]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[30]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[31]  A. D. Poularikas,et al.  Automated sizing, counting and identification of zooplankton by pattern recognition , 1984 .

[32]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[33]  Fabio Roli,et al.  Methods for Designing Multiple Classifier Systems , 2001, Multiple Classifier Systems.

[34]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[35]  David G. Stork,et al.  Pattern Classification , 1973 .

[36]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[37]  Timothy F. Cootes,et al.  Locating salient facial features using image invariants , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.