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FYP Topics for 2013-2014

FYP Topics for 2013-2014. Cavan Loy. Can you count how many women and how many man are there?. Gender Classification At-a-Distance. Girls like this product!. Guys like that product!. CCL1: Gender Classification: Machine Intelligence vs. Human Perception.

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FYP Topics for 2013-2014

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  1. FYP Topics for 2013-2014 Cavan Loy

  2. Can you count how many women and how many man are there?

  3. Gender Classification At-a-Distance Girls like this product! Guys like that product!

  4. CCL1: Gender Classification: Machine Intelligence vs. Human Perception • Joint project with Psychology Department • Investigate and compare the capability of human and computational recognition methods, in identifying the true gender given images captured at-a-distance • Students will • Design experiments to examine how good human subjects in classifying genders • Investigate which machine learning methods exhibit the closest thinking pattern/intelligence as in human subjects • Investigate how the participant's background will affect their perception, and which kinds of machine learning approach best model which group of users' perception • Math: 30%, Programming: 30%, Experiment: 40%

  5. Cluster 1 Cluster 2 Your Photo More similar Less similar . . .

  6. CCL2: Image Clustering and Ranking • Industry-partnered research project with Lenovo • Compare and investigate methods for image clustering and ranking in low-dimensional manifold • Student will • Implement existing manifold-based clustering and ranking algorithms • Design experiments to compare the methods • Math: 50%, Programming: 50%

  7. Sketch Recognition Source:http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/

  8. CCL3: Sketch Recognition • Industry-partnered research project with Lenovo • Human is good in recognizing object category of real-world hand drawings. How about machine? • Student will • Implement existing methods or investigate a new approach for sketch recognition • Extract visual features to represent the sketches • Use a machine classifier to automatically assign a sketch to the correct category • Math: 40%, Programming: 60%

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