JAMIE SHOTTON THESIS

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. Here are a few examples where the contour fragments used for detection are superimposed. We as humans are effortlessly capable of recognising objects from fragments of image contour. Please see my Microsoft homepage for updates since Our technique was applied to a 17 object class database from TU Graz. 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. Our technique was applied to a 17 object class database from TU Graz. A second visual cue is texture. Our visual recognition methods have proven useful for semantic photo synthesis. Green boxes represent correct detections of the horses, red boxes are false positives, and yellow boxes are false negatives. Our ECCV paper proposed TextonBoost for simultaneous automatic object recognition and segmentation, using the repeatable textural properties of objects. We demonstrated in our ICCV paper how an automatic system can exploit contour as a powerful cue for image classification and categorical object detection.

Green boxes represent correct detections of the horses, red boxes are false positives, and yellow boxes are false negatives. Other interests include class-specific segmentation, visual robotic navigation, and image search. Green boxes represent correct detections of iamie horses, red boxes are false positives, and yellow boxes are false negatives.

jamie shotton thesis

This website thesia published before I joined Microsoft and is maintained personally for the benefit of the academic community. 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. We demonstrated in our ICCV paper how an automatic system can exploit contour as a powerful cue for image classification and categorical object detection.

  THE LOST THESIS MYVIDSTER

Other interests include class-specific segmentation, visual robotic navigation, and image search. This website was published before I joined Microsoft and is maintained personally for the benefit of the academic community.

Contour and Texture for Visual Recognition of Object Categories

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.

Green boxes represent correct detections of the horses, red boxes are false positives, snotton yellow boxes are false negatives. An improved multi-scale version of this work has been accepted for publication in PAMI.

Texture thssis Visual Recognition A second visual cue is texture. The fragments of contour used for detection are visualised in the final column.

jamie shotton thesis

Our ECCV paper proposed TextonBoost for simultaneous automatic object recognition and segmentation, using the repeatable textural properties of objects.

Microsoft is in no way associated with or responsible for the content of these legacy pages. An expanded version has been accepted to IJCV.

Other interests include class-specific segmentation, shottton robotic navigation, and image search. An improved multi-scale version of this work has been accepted for publication in PAMI.

jamie shotton thesis

Our visual recognition methods have proven useful for semantic photo synthesis. The shohton of contour used for detection are visualised in the final column.

Please see my Microsoft homepage for updates since 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.

  HANAUMA BAY ESSAY

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.

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

Jamie Shotton – Publications

Based on randomized decision forests, our new system is able to run real-time, illustrated in our demo video: Our new dense-stereo algorithm can interpolate between different cameras to facilitate eye contact in one-to-one video conferencing.

We show how texture, layout, and textural context shothon be exploited to achieve accurate semantic segmentations of images, as illustrated in the results below and in the videos available here. Example object detection results on the Weizmann horse database. Jamir fragments of contour used for detection are visualised in the final column.

We demonstrated in our ICCV paper how an automatic system can exploit contour as a powerful cue for image classification and categorical object detection. Our technique was applied to a 17 object class database from TU Graz.

An improved multi-scale version of this work has been accepted for publication in PAMI.