wits presentation 2_19052015
TRANSCRIPT
Scene Identification based onConcept LearningBeatrice van Eden
- Part time PhD Student at the University of the Witwatersrand.- Fulltime employee of the Council for Scientific and Industrial Research.
Index• Broad problem statement Give an overview of the researchI am busy with. • Identify items in a scene Current work on the project.
• Continued workHighlights of some of the reading I did.
Ivan Laptev2007
Broad Problem Statement
• How can we give a mobile robot the capability to continuously and autonomously form concepts of its environment?
Identify items in a scene
Image of scene
Identify items in scene
Determine most feasible scene
Recognise the scene / contribute
to a concept
• Ivan Laptev• Recognize and to localize objects in a still image (2D).• Histogram based image representations.• They used AdaBoost to select histogram regions optimized for the
classification of training samples.• Fisher weak learner is then applied to select a histogram feature
and an associated classifier at each round of AdaBoost
Identify items in a scene • Steve's Object Detection Toolbox 1 (Steve Branson)• Features, Object Recognition, and Object Detection.• The following features are included. They can be used in
conjunction with object recognition, sliding window detection, or deformable part models (e.g., localized versions of features are supported).• HOG: Dalal Triggs-style HOG detector, with tricks to compute them
quickly over multiple orientations• Bag of Words SIFT: Sliding window detectors over vector quantized
SIFT descriptors• Color Histograms: Sliding window detectors over RGB or CIE color
histograms• Fisher Vectors: Fisher vector encoded SIFT or color features (Perronin
et al. ECCV'2010)• Spatial Pyramids: The above features can be stacked together in
spatial pyramids, or multi-resolution pyramids
Identify items in a scene
Continued work
Searching for objects driven by context(Bogdan Alexe and Heess, Nicolas and
Yee W. Teh and Vittorio Ferrari)
• Still 2D Images• Video image detection and tracking • 3D Images• Recap face tracing algorithm • Recap tabel top detection and cluster recognition on table top
Continued work
Searching for objects driven by context(Bogdan Alexe and Heess, Nicolas and
Yee W. Teh and Vittorio Ferrari)
Scemantic Mapping Using Object-Class Segmentation of RGB-D Images(Stuckler, J.; Biresev, N.; Behnke, S)
Unsupervised feature learning for 3D scene labeling(Lai, K.; Liefeng Bo; Fox, D)
3-Sweep: extracting editable objects from a single photo(Tao Chen, Zhe Zhu, Ariel Shamir, Shi-Min
Hu, Daniel Cohen)
Thank you