Sea Turtle Identification by Matching Their Scale Patterns

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1 Sea Turtle Identification by Matching Their Scale Patterns Technical Report Rajmadhan Ekambaram and Rangachar Kasturi Department of Computer Science and Engineering, University of South Florida Abstract All sea turtles occurring in U.S. Atlantic waters are protected under the Endangered Species Act of Accurate assessments of sea turtle populations, such as abundance estimates, require the ability to identify individuals within a population. Naturally occurring variations of the head scales of sea turtles allow for individuals to be identified using photo-identification (photo-id). However, the process of visually examining photographs within a large catalog is labor intensive, and is subject to human error. Computer-assisted photo-id programs have been developed to aid with the identification of individuals for some species, but such programs are not currently available for sea turtles. Our goal was to develop a computer-assisted program that will identify recaptured turtles using the unique scale patterns on their head. As a first step, we have designed a system that rank orders images using a match score between the probe image (new image) and each archived image in the database. The system was tested on a sample set of 780 images that included 114 duplicate images of 52 recaptured sea turtles. Our algorithm placed the duplicate images within the top 20% of the ranked set for 80% of the probe images that had a duplicate in the database, thus substantially reducing the amount of time required to identify recaptured turtles. 1. Introduction The use of photo-identification (photo-id) to identify individuals from their natural markings has been wellestablished for a number of cetacean species, and is growing in popularity among other species, including sea turtles ([1,2]). Variations of the size, arrangement, and coloration of the head scales of individual sea turtles allow for their identification ([3,4]) over time using photo-id (Fig. 1). Researchers at the Southeast Fisheries Science Center Beaufort Lab in Beaufort, NC are using photo-id to obtain abundance estimates of juvenile sea turtles within Pamlico and Core Sounds, NC. Figure 1 Images of two different sea turtles from our dataset Upon capture, photographs are taken of the top portion of the turtle s head. Photographers have limited control over the pose and lighting conditions. If the animal is too large to bring aboard the vessel it may be necessary to capture the image while the turtle is in the water. In such cases, photos are usually not taken directly overhead of the turtle, making it difficult to segment the scales accurately and use them for proper matching. To use the number and arrangement of scales, the scales should be segmented properly; i.e., no two scales should be combined as a single one and a single scale should not be divided into two or more scales. The difficulty in detecting the scale boundary is due to their variations among turtles and the pose of the captured image. The scale boundary is often discontinuous due to the sloughing of scales as the turtle grows, or growth of barnacles, and anomalies particular to the turtle.

2 The above mentioned physical attributes used for identification are shared by all species of hard-shelled sea turtles, but vary slightly among individuals. The local characteristics such as those due to anomalies, the shape of the scales junctions, and the surface texture can be used to identify individual turtles. Interestingly, this application is similar to that of human face recognition which has been studied extensively by computer vision researchers [5]. The difficulty in capturing the characteristics of a human face in any fixed mathematical model is well known. Instead of focusing on extracting specific local characteristics and designing a customized feature-based matching algorithm, we used the general purpose SIFT (Scale Invariant Feature Transform [6]) feature detector for this proof of concept study (Fig. 2). The expectation is that the SIFT feature detector will capture some of these local properties from the turtle images to adequately demonstrate the feasibility of using computer vision based methods in this novel application domain. As in any typical object recognition system, our method involves finding robust features which are invariant under transformations, matching the features with the target image, and performing geometric alignment to verify the matching of feature points. We used SIFT for feature extraction, k-nearest neighbor for matching, and Homography to verify geometric alignment. 2. Identification Process 2.1. SIFT SIFT [6] is one of the image features used extensively in 3D reconstruction, object detection, and robot navigation applications for matching image points across images taken under different illumination conditions and from varying angles. It is well known that the extreme points in a region of the image will remain the same even when there is a substantial change in viewpoint, illumination, and noise. The extreme points are the maxima and minima of intensity values of small patches of the image densely sampled at different scales (spatial resolutions). The SIFT feature descriptor is computed only for the extreme points. The descriptor is the histogram of the threshold magnitude of the gradient orientation. This descriptor is invariant to spatial resolution because it is computed at different scales. It is also invariant to orientation because it is aligned with respect to the peaks in the orientation histogram. Figure 2 shows the SIFT feature locations for two different turtle images. Figure 2 SIFT features are shown as arrows. The location, size, and orientation of features corresponds to the starting point, length, and direction of the features Feature matching For each SIFT feature point in one image, the corresponding feature points in the comparison image are sorted according to their Euclidean distance. The first two nearest neighbors are selected and if the distance of the first nearest neighbor is less than 0.8 times the distance of the second nearest neighbor then the first nearest neighbor is considered to be a correct match. Figure 3 shows the SIFT feature point matches between two images of the same turtle.

3 Figure 3 All SIFT feature matches between two images. The end points of the lines are the feature locations in the respective image Homography Homography is the linear transformation from a projective plane to itself. If we have four corresponding points between two images, then a homography matrix can be calculated up to a scale factor. Here, we randomly select the coordinates of four matching SIFT points, compute the homography matrix, and verify how many other points satisfy the transformation within a threshold difference. The transformation matrix that satisfies the most points is assumed to be the correct transformation between the two images. If the matching points are from different images, the number of points that satisfy the transformation will be much less. The feature matches shown in Figure 3 are filtered using homography transformation, and only the matches that satisfy the homography constraint are shown in Figure 4. Figure 4 SIFT feature matches after filtering by homography 2.4. Ranking system Assume that we have n labeled turtle images. We refer to these images as a gallery. New turtle images (probe images) are compared to n labeled turtles in the gallery to search for recaptured turtles. In a typical operational environment with large galleries, it is not unusual for a researcher to spend a significant amount of time performing this matching task; the process is tedious and could potentially leads to human error. The goal of our system is to automate this process, therefore reducing the amount of time it takes to identify recaptured turtles, and to standardize the identification process. However, since two images of the same turtle are seldom identical, one must decide if there is a match based on a similarity measure and a preset threshold of acceptable limit on this measured value. In our experimental system, in the absence of a preset

4 threshold, we present the user with a rank ordered list of gallery images based on the computed matching score of geometrically aligned SIFT feature matches. The user will then go through the gallery images in the ranked order rather than an arbitrary order. Our expectation is that if there is a matching image in the gallery it would appear within the top p% of the ordered set. The user selects p depending upon their requirement and would limit search to the top p% of gallery images. This will significantly reduce the time for manual search for match and would improve accuracy by reducing operator fatigue. Figure 5 shows an example ranking produced for a probe image by our system Figure 5 Rank ordered gallery images. The top left image is the probe. The top middle, top right, bottom left, bottom middle and bottom right images are ranked 1, 2, 3, 4 and 5 in the gallery respectively. The image ranked 2 is the correct match. 3. Results We tested the method on a database of 780 turtle images that had previously been examined to determine if any turtles had been captured more than once. Of these, 52 turtles were identified as recaptures, resulting in a total of 114 images with a matchable pair in the database (Some turtles were recaptured more than once). We used each of the 114 images of recaptured turtles as a probe, and the remaining 779 images were used as gallery to test the algorithm. The result of this experiment is captured using the Cumulative Match Characteristics (CMC) curve (Fig. 6). The CMC curve captures the probability of identification for various ranks. In Figure 6, the rank is shown as a percentage of the data set size instead of the actual rank to aid with the interpretation of the performance of the algorithm. Figure 6 shows that a matching image appears within the top 10% (78 images) of the gallery images for 70% of the probe images. If we examine the top 20%, the coverage increases to 80% of the test cases. The matching process in this method can be replaced by method suggested in [7] to improve the results further. Instead of directly finding the distance between each feature points, the method in [7] clusters the SIFT feature points, and determines the distance based on the clusters.

5 Figure 6 The CMC curve shows the performance of the algorithm. 4. Extension of work to other animal species Manatees are also carefully monitored, and researchers are working to prevent their extinction. In 2010, the estimated minimum population was approximately 5,000 animals in Florida [8]. Manatees live close to river shores, and are often injured by boat propellers, resulting in permanent scarring. While they lack any observable patterns such as those on turtles, scientists are using these wound marks to identify individual manatees. However, minor scars may disappear as the animal ages, and the accumulation of new scars can make identification a much harder problem. Since our method relies on local features, examination of both of these features simultaneously may allow for new insight about other properties that can be used for addressing both issues. 5. Conclusions We have designed an experimental system for detecting matching pairs of sea turtle images within a database. The objective is to determine if a recently captured turtle is a new animal or a turtle that was previously captured and cataloged. The method used in our experiment is rather simple, and yet it generated very promising results, Hence the method demonstrated the potential for opening a new research application domain for vision researchers for extending the work to other species. References [1] J Reisser, M Proietti, P Kinas, and I Sazima. Photographic identification of sea turtles: method description and validation, with an estimation of tag loss. Endangered species research. Volume 5:73-82, [2] G Schofield, K Katselidis, P Dimopoulos, and JD Pantis. Investigating the viability of photo-identification as an objective tool to study endangered sea turtle populations. Journal of experimental marine biology and ecology. Volume 360, Issue 2: , [3] J. Wyneken. The Anatomy of Sea Turtles, NOAA Technical Memorandum NMFS-SEFSC-470, [4] [5] X. Zhang, Y. Gao. Face recognition across pose: A review. Pattern Recognition, Volume 42: , [6] DG. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, Volume 60:91-110, [7] J. Zhao, HJ. Zhou, GZ. Men. A Method of SIFT feature points matching for image mosaic. International Conference on Machine Learning and Cybernetics, Volume 4: , [8] [9] R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision, 2000.

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