Changes

| October 11

| October 11

| Kotaro Hara

| Kotaro Hara

| TBA

| Using Google Street View to Identify Street-level Accessibility Problems

|-

|-

| October 18

| October 18

In this detection problem, the anomalies have very well defined structure and therefore, they can be represented sparsely in some subspace.  In addition, the image background has very structured texture, so it is sparse with respect to a different frame.  Theoretical results in mathematical geometric separation show that it is possible to separate these two image components (regular texture from contours) by minimizing the L1 norm the coefficients in geometrically complementary frames.  More recently, it has been shown that this problem can be solved efficiently using thresholding and total variation regularization.  Our experiments show that the sparse coefficients extracted from the contour component can be converted into feature vectors that can be used to cluster and detect these anomalies.

In this detection problem, the anomalies have very well defined structure and therefore, they can be represented sparsely in some subspace.  In addition, the image background has very structured texture, so it is sparse with respect to a different frame.  Theoretical results in mathematical geometric separation show that it is possible to separate these two image components (regular texture from contours) by minimizing the L1 norm the coefficients in geometrically complementary frames.  More recently, it has been shown that this problem can be solved efficiently using thresholding and total variation regularization.  Our experiments show that the sparse coefficients extracted from the contour component can be converted into feature vectors that can be used to cluster and detect these anomalies.

===TBA===

===Using Google Street View to Identify Street-level Accessibility Problems===

Speaker: [http://kotarohara.com/ Kotaro Hara] -- Date: October 11, 2012

Speaker: [http://kotarohara.com/ Kotaro Hara] -- Date: October 11, 2012