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Part 4: Combined segmentation and recognition

Part 4: Combined segmentation and recognition

Part 4: Combined segmentation and recognition. by Rob Fergus (MIT). Aim. Given an image and object category, to segment the object. Object Category Model. Segmentation. Cow Image. Segmented Cow. Segmentation should (ideally) be shaped like the object e.g. cow-like

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618 views • 43 slides
Beyond bags of features: Part-based models

Beyond bags of features: Part-based models

Beyond bags of features: Part-based models. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. visual codeword with displacement vectors. Implicit shape models. Visual codebook is used to index votes for object position.

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403 views • 32 slides
Agenda

Agenda

Agenda. Introduction Bag-of-words models Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based image retrieval Datasets & Conclusions. Aim. Given an image and object category, to segment the object. Object Category

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325 views • 17 slides
Our Framework 2.1. Enforcing Temporal Consistency by Post Processing

Our Framework 2.1. Enforcing Temporal Consistency by Post Processing

Human Detection in Videos using Spatio -Temporal Pictorial Structures Amir Roshan Zamir , Afshin Dehghan , Ruben Villegas University of Central Florida. Results Videos Taken From TRECVID MED11 Dataset. 2.2. Enforcing Temporal Consistency by Embedding them into the Detection Process

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145 views • 1 slides

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