JAMIE SHOTTON THESIS

We demonstrated in our ICCV paper how an automatic system can exploit contour as a powerful cue for image classification and categorical object detection. Based on randomized decision forests, our new system is able to run real-time, illustrated in our demo video: Microsoft is in no way associated with or responsible for the content of these legacy pages. Contour for Visual Recognition We as humans are effortlessly capable of recognising objects from fragments of image contour. Example object detection results on the Weizmann horse database. We show how texture, layout, and textural context can be exploited to achieve accurate semantic segmentations of images, as illustrated in the results below and in the videos available here. Our visual recognition methods have proven useful for semantic photo synthesis.

Please see my Microsoft homepage for updates since An expanded version has been accepted to IJCV. Our visual recognition methods have proven useful for semantic photo synthesis. Please see my Microsoft homepage for updates since Example semantic segmentation results.

Please see my Microsoft homepage for updates since Our new dense-stereo algorithm can interpolate between different cameras to facilitate eye contact in one-to-one video conferencing. Here are a few examples where the contour fragments used for detection are superimposed. We demonstrated in our ICCV paper how an automatic system can exploit contour as a powerful cue for image classification and categorical object detection. We as humans are effortlessly capable of recognising objects from fragments of image contour.

  KEPNER-TREGOE PROBLEM ANALYSIS CASE STUDY

Contour and Texture for Visual Recognition of Object Categories

shotgon Example semantic segmentation results. Contour for Visual Recognition We as humans are effortlessly capable of recognising objects from fragments of image contour.

Based on randomized decision forests, our new system is able to run real-time, illustrated in our demo video: Our ECCV paper proposed TextonBoost for simultaneous automatic object recognition and segmentation, using the repeatable textural properties of objects. Example object detection results on the Weizmann horse database. Microsoft is in no way associated with or responsible for the content of these legacy pages. The fragments of contour used for detection are visualised in the final column.

Jamie Shotton – Research

Based on randomized decision forests, our new system is able to run real-time, illustrated in our demo video: Our visual recognition methods have proven useful for semantic photo synthesis. An expanded version has been accepted to Shottin.

jamie shotton thesis

Our ECCV paper proposed TextonBoost for simultaneous automatic object recognition and segmentation, using the repeatable textural properties of objects. This website was published before I joined Microsoft and is maintained personally for the benefit of the academic community. We have recently improved TextonBoost considerably, making it more accurate and much faster.

Our technique was applied to a 17 object class database from TU Graz.

A second visual cue is texture. Texture for Visual Recognition A second visual cue is texture.

jamie shotton thesis

Our new dense-stereo algorithm can interpolate between different cameras to facilitate eye contact in one-to-one video conferencing.

  DISSERTATION NICOLE NEUBERT

Here are a few examples where the contour fragments used for detection are superimposed. An improved multi-scale version of this work has been accepted for publication in PAMI.

Based on randomized decision forests, our new system is able to run real-time, illustrated in our shottonn video: Microsoft is in no way associated with or responsible for the content of these legacy pages.

Our ECCV paper proposed TextonBoost for simultaneous automatic object recognition and segmentation, using the repeatable textural properties of objects. We as humans are effortlessly capable of theais objects from fragments of image contour.

Our technique was applied to a 17 object class database from TU Graz. Other interests include class-specific segmentation, visual robotic navigation, and image search. We have recently improved TextonBoost considerably, making it more accurate and much faster.

Our visual recognition methods have proven useful for semantic photo synthesis. Here are a few examples where the contour fragments used for detection are superimposed. Texture for Visual Recognition A second visual cue is texture. We have recently improved TextonBoost considerably, making it more accurate and much faster.

An expanded version has been accepted to IJCV.