CenSSIS is a National Science Foundation Engineering Research Center supported in part by the

1. Change Detection and Visualization Eleonora Z. Vidolova, Karin Griffis, Maja Bystrom Electrical and Computer Engineering, Boston University Bio-Med Enviro-Civil L3 S4 S5 S3 S2 S1 ValidatingTestBEDs FundamentalScience R2 L1 R1 R3 Multispectral images acquired at different times Abstract Change categorization to highlight important changes Automated detection of changes between sequential images (images taken of the same object at different times) is vital for applications such as assisted medical diagnosis, surveillance, and environmental monitoring. Traditional change detection programs have binary outputs, that is, each region in the image is marked with a "yes" or a "no" to indicate whether change has occurred. Newer high-level analysis approaches express changes in terms of known objects and behaviors, these systems are known as IUSs (Image Understanding Systems). The work on this REU project is to aid the research thrust of creation of an IUS for multi-spectral remote sensing imagery. The main goals of this project were to implement the occlusion-adaptive, content-based mesh motion estimation method of [1] to represent and interpret motion, and then adapt it for the multi-spectral data. To date the occlusion-based part of the algorithm has been completed and the work on the content-based part and the adaptations is underway. SWIR Band 4 Time 1 Change Understanding via Motion Detection and Interpretation Time 2 crop change clouds ASTER images Future Work Motion Detection via Optical Flow Primary Project Goals • Complete the content-based mesh design • Adapt Altunbasak's algorithm for multi-spectral data as opposed to single band data • Make code modular so it can be utilized as the motion model in the image understanding system • Use multi-spectral, occlusion-adaptive, content-based mesh to model motion between two co-registered, multi-spectral images taken at different times. Complete mesh interpretation algorithm. Test image 1 Test image 2 Optical Flow Field • Implement the occlusion-adaptive and content- based meshes from Altunbasak and Tekalp [1]. • Adapt the mesh algorithm to change detection problem. • Interpret mesh changes and model failures to • categorize image change. Approximation of the local image motion based upon local derivatives in a sequence of images. Disadvantage: This does not work for occlusions, new objects, or large motion. Difficult to interpret motion field. Mesh Algorithm Overview • Identify Occlusion/Disocclusion Regions • Find optical flow field (used Lucas-Kanade method) • Compensate frame k from frame k+1 • Where the compensated image M(xk) and the original image xk are different we label the pixels as (dis)occlusion pixels References Original Disocclusion Occlusion [1] Y. Altunbasak, A. M. Tekalp, “Occlusion-Adaptive, Content-Based Mesh Design and Forward Tracking,” IEEE Trans. Image Processing,vol. 6, pp.1270-1280, Sept. 1997 [2] Y. Nakaya, H. Harashima, "Motion Compensation Based on Spatial Transformations," IEEE Trans. Circuits and Systems for Video Technology, vol. 4, no. 3, pp.339-356, June 1994 [3] J. L. Barron, N. A. Thacker, "Tutorial: Computing 2D and 3D Optical Flow," Imaging Science and Biomedical Engineering Division, Medical School, University of Manchester, Jan. 2005 Alternative to Optical Flow: Meshes and Hexagonal Matching • Polygon Approximation of Occlusion Shapes • Find the pixels on the (dis)occlusion region edges that are furthest apart (main axis) • Find the initial vertices, the two point on each side of the main axis that are furthest away from it • Connect neighboring nodes and check if more nodes need to be added Frame k+1 Frame k Place a uniform mesh over image at Time 1 then deform the mesh via hexagonal matching to fit the image at Time 2 Relation to CenSSIS R2, Physics-Based Signal Processing and Image Understanding Image from Altunbasak and Tekalp [1] Challenges and Accomplishments • Organization of the data structures from one step of the algorithm to the next • Finding and modifying a Lucas-Kanade optical flow algorithm implementation that fits our purposes • Developing a simple implementation for assigning nodes • on the edges of occlusion regions L2 Images from Altunbasak and Tekalp [1] Advantage: Motion constrained by the size of the triangles. Less calculation time. Disadvantage: Problems with motion detection on object boundaries. L2 Eleonora Z. Vidolova eleonora@bu.edu Contact Information - CenSSIS is a National Science Foundation Engineering Research Center supported in part by the Engineering Research Centers Program of the National Science Foundation (Award # EEC-9986821)