'Hyperspectral image' presentation slideshows

Regularized Double Nearest Neighbor Feature Extraction for Hyperspectral Image Classification

Regularized Double Nearest Neighbor Feature Extraction for Hyperspectral Image Classification. Hsiao-Yun Huang Department of Statistics and Information Science, Fu-Jen University. Hyperspectral Image Introduction 1. (image credit: AFRL). Hyperspectral Image Introduction 2.

800 views • 53 slides

Displaying Hyperspectral Images on RGB Displays

Displaying Hyperspectral Images on RGB Displays. Victoria Kogan November 2012 This presentation was prepared for a seminar on hyperspectral images, Computer Science Department, University of Haifa, Israel. Agenda. Introduction Comprehensive Representation Algorithms

519 views • 35 slides

High-resolution Hyperspectral Imaging via Matrix Factorization

High-resolution Hyperspectral Imaging via Matrix Factorization. Rei Kawakami 1 John Wright 2 Yu-Wing Tai 3 Yasuyuki Matsushita 2 Moshe Ben-Ezra 2 Katsushi Ikeuchi 3 1 University of Tokyo, 2 Microsoft Research Asia (MSRA), 3 Korea Advanced Institute of Science and Technology (KAIST)

392 views • 24 slides

Manifold learning: Locally Linear Embedding

Manifold learning: Locally Linear Embedding. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Review: MDS. Metric scaling (classical MDS) assuming D is the squared distance matrix. Non-metric scaling

774 views • 23 slides

Road Map

327 views • 21 slides

Sparse Matrix Factorizations for Hyperspectral Unmixing

Sparse Matrix Factorizations for Hyperspectral Unmixing. John Wright Visual Computing Group Microsoft Research Asia. Sept . 30 , 2010. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A. Problem setting.

209 views • 7 slides

Hyperspectral Image

Hyperspectral Image. y. x. wavelength. DC Mall. Spectra of some pixels. 200 x 200 x 191 image. Images for some wavelengths. DC Mall Segmentation. 200 x 200 x 191 image. Cluster analysis in R 191. Segmentation result (different colors). Plastics. 250 x 320 x 240 image.

297 views • 7 slides

Challenge and Significance

The ISRA algorithm will be used for coral (SeaBED) image understanding (R2) and its hardware implementation will allow faster information management (R3).

119 views • 1 slides