PPT-Local features: detection and description

Monday March 7 Prof Kristen Grauman UTAustin Midterm Wed Covers material up until 31 Solutions to practice exam handed out today Bring a 85x11 sheet of notes if you want Review the outlines and notes on course website accompanying reading in textbook. ABQ Leak Locator brings years of systems engineering and in-depth technical problem solving methodology to the table to apply toward benefiting its clients and customers. 02nT Faster cycle rates Up to 10Hz Longer range detection Pros brPage 5br Magnetometers Magnetometers Large distant targets mask small local targets Difficult to pick out small target due to background noise No sense of direction of target on single CSE 576. Face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Discriminative part-based models. Many slides based on . P. . . Felzenszwalb. Challenge: Generic object detection. Pedestrian detection. Features: Histograms of oriented gradients (HOG). Partition image into 8x8 pixel blocks and compute histogram of gradient orientations in each block. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. . Each . feature set increases accuracy over the 69% baseline accuracy. .. Word Prominence Detection using Robust yet Simple Prosodic Features. Prosodic Features . ( . ** denotes novel features. ). Twitter . A Behavioral Modeling . Approach. Ashwin. . Rajadesingan. , Reza Zafarani, and . Huan. . Liu. Sarcasm. . . a . nuanced form of language where usually, the user explicitly states the opposite of what she implies. . Source: D. Lowe, L. Fei-Fei. Canny edge detector. Filter image with x, y derivatives of Gaussian . Find magnitude and orientation of gradient. Non-maximum suppression:. Thin multi-pixel wide “ridges” down to single pixel width. on Online Social Networking. Group Members. :. Sunghun Park. Venkat Kotha. Li Wang . Wenzhi Cai. Outline. Problem Overview. Current Solutions. Limitations of Current Solutions. Conclusion . Our Solution. Flavio, Jon, Ravi, Mohammad, and Sandeep. Presented By:. Muthu. . Chandrasekaran. Published in . AAAI 2014. The . Outline. Big Picture. Contributions. Approach. Results. Discussion. 2. Event Detection Via Communication Pattern Analysis. Devi Parikh. Slide credit: Kristen . Grauman. 1. Disclaimer: Most slides have been borrowed from Kristen . Grauman. , who may have borrowed some of them from others. Any time a slide did not already have a credit on it, I have credited it to Kristen. So there is a chance some of these credits are inaccurate.. College Interim 1966 Instructor x0000rescription Enrollment Prerequisite Title Instructor Description Enrollment Prerequisite Title Instructor Description Prerequisite Title Instructor Description Pag Limit of Detection (LOD). The detection limit is the concentration that is obtained when the measured signal differs significantly from the background.. Calculated by this equation for the ARCOS.. C.