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Feature Detection and Outline Registration in Dorsal Fin Images A. S. Russell, K. R. Debure, Eckerd College, St. Petersburg, FL Most Prominent Notch analyze.

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Presentation on theme: "Feature Detection and Outline Registration in Dorsal Fin Images A. S. Russell, K. R. Debure, Eckerd College, St. Petersburg, FL Most Prominent Notch analyze."— Presentation transcript:

1 Feature Detection and Outline Registration in Dorsal Fin Images A. S. Russell, K. R. Debure, Eckerd College, St. Petersburg, FL Most Prominent Notch analyze wavelet decomposition of angles comprising the dorsal fin outline, localizing search to the trailing edge of the dorsal fin identify candidate notches as local minima with large magnitudes at an intermediate transform level track candidate notches to coarser level. Identify most prominent notch as the minimum that decreases most slowly in magnitude back-track to the finest level of detail to accurately identify position of notch Marine mammologists studying the behavior and ecology of wild dolphins often employ photo-identification as a means of associating observational data with individual dolphins. DARWIN is a computer program that addresses the difficulties of manual photo-identification by applying computer vision and signal processing techniques to automate much of the process. The DARWIN system allows a researcher to query a database of previously identified dolphin dorsal fin images with an image of an unidentified dolphin's fin. The researcher may then browse a rank ordered list of database fin images that most closely resemble the query image to identify the dorsal fin. Since the examination begins with fins most similar to the unidentified fin, the time required of the researcher to identify the correct match is potentially reduced. A major challenge in the automated process arises from the presence of perspective distortions, which can cause different views of the same dorsal fin to differ significantly in appearance, making direct comparison extremely problematic. Current research has focused on methods of distortion correction to quickly transform images so that it appears fins were photographed from the same angle. Locating salient fin feature points provides a basis for this transformation by allowing a direct determination of any rotational, translational, and scaling factors necessary to compensate for perspective distortions. Preliminary results show that this approach produces appropriate transformations when the feature points are accurately identified. The resulting distortion correction produces a marked improvement in the similarity of the dorsal fin outlines. This new approach promises improved accuracy and a compelling alternative to manual photo-identification. Figure 4 : Finding the tip from the wavelet transform. The original chain code is on top, with increasingly coarse details following. The position of the tip is found on the coarsest (bottom) level, and tracked to the finer levels. The tracking of the tip is marked at each level. Automatic Feature Selection Starting Point of the Leading Edge compute absolute angles between successive points along the edge identify threshold angle which maximizes between class variance [3] of the angles which comprise the proper leading edge of the fin and the angles which are associated with the body of the dolphin. discard line segments at leftmost end of outline if their angles diverge significantly from predominant edge orientation select the initial point of the leading edge if no such divergence exists Tip of the Dorsal Fin compute a wavelet decomposition of the chain of angles comprising the fin outline identify largest positive local maximum in a coarse level representation of the outline (roughly indicates the position of the fin tip) track position back through the finer scale representations to more accurately identify the position. Figure 5 : Two real- world results of the registration process. In each set, the originally extracted outlines are pictured on the top. The automatically registered outlines are pictured on the bottom. Figure 3 : Left: Dorsal fin image with characteristic feature points indicated in red. Center: Extracted outline with feature points marked. Right: Chain code of angles comprising fin outline. Abstract Introduction Dorsal Fin Identification Ideally, we would like to transform the outlines so that it appears the original fins were photographed from the same angle. The transformation process is as follows: locate sets of common feature points on pairs of fin outlines compute transformation matrix which maps one set of points onto the other apply transformation to entire outline Outline Registration Preliminary results suggest that the registration method presented herein shows significant promise in perspective correction of dolphin dorsal fin images. The effectiveness of the method is, of course, entirely dependent upon the accuracy of extracted features. Fortunately, features are well extracted in many cases: The identification of the fin tip is quite stable even when part of the tip is absent. Notch identification is reasonably successful even in outlines where multiple notches are present or the most prominent notch is relatively small. The starting point of the leading edge presents more difficulty when the area where the dorsal fin meets the dolphin’s body is not contained in the outline. A possible solution for this fault is to detect when an outline may lack part of the fin's leading edge, and alternatively use a different feature point, such as the end of the leading edge. In any case, the process has the ability to improve the registration of outlines considerably. The improved registration suitably corrects for perspective distortion and makes fin outline comparison and subsequent retrieval of appropriate images far less problematic.Conclusion [1] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, pages , [2] S.Mallat. Characterization of signals from multiscale edges. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(7): , July [3] N. Otsu. A threshold selection method from gray level histograms. IEEE Trans. on Systems, Man, and Cybernetics, SMC-9:62--66, January 1992.References The authors would like to thank the the following organizations for support of this research: National Science Foundation National Marine Fisheries Services Eckerd College Dorsal fin images courtesy of Eckerd College Dolphin Project. Acknowledgements DARWIN is a computer program which aids marine mammalogists in the identification of dolphins and in the management of a database of observational information. DARWIN graphically presents the digitized dorsal fin images and associated textual sighting information. The database of information can be: queried for images which are similar to the dorsal fin image of an unknown individual queried with the name of a specific individual organized and viewed by particular sighting information such as location, date, or fin damage category Figure 1 : Following a query of the dorsal fin database, DARWIN presents icons representing fin images which most closely resemble the unknown fin image. Figure 2 : The tracing window allows the user to perform a sketch based query of the database of dorsal fins. This window is also used to add new dorsal fin images and associated sighting data to the database. Direct comparison of chain code representations is problematic since perspective distortions can cause outlines extracted from multiple photographs of the same dolphin’s dorsal fin to differ significantly. We present a technique to transform the outlines so that it appears the original fins were photographed from the same angle. Locating common feature points on fin outlines provides a basis for this transformation. 2.Active contours [1] are employed to accurately position the points comprising the outline onto the actual edge of the fin. The fin outline is extracted as a series of two-dimensional x and y coordinates. 3. The extracted outline is reduced to a one-dimensional series of angular changes between points called a chain code. 4. The chain-coded outline is compared against fin outlines in the database. In order to query the database for fins resembling the unknown dolphin: 1. The user traces a rough outline of the dorsal fin.


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