Contour Detection from Deep Patch-level Boundary Prediction

In this paper, we present a novel approach for contour detection with Convolutional Neural Networks. A multi-scale CNN learning framework is designed to automatically learn the most relevant features for contour patch detection. Our method uses patch-level measurements to create contour maps with overlapping patches. We show the proposed CNN is able to to detect large-scale contours in an image efficienly. We further propose a guided filtering method to refine the contour maps produced from large-scale contours. Experimental results on the major contour benchmark databases demonstrate the effectiveness of the proposed technique. We show our method can achieve good detection of both fine-scale and large-scale contours.Comment: IEEE International Conference on Signal and Image Processing 201

Shadow Optimization from Structured Deep Edge Detection

Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit the local structures of shadow edges by using a structured CNN learning framework. We show that using the structured label information in the classification can improve the local consistency of the results and avoid spurious labelling. We further propose and formulate a shadow/bright measure to model the complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected in the image. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on the major shadow benchmark databases collected under various conditions.Comment: 8 pages. CVPR 201

Advances in fuzzy rule-based system for pattern classification

Ph.DDOCTOR OF PHILOSOPH

Report on professional attachment with Arthur Andersen.

The report covered the organisation of Arthur Anderson, the profession of business consulting, lesson and skills learnt, a critical evaluation of the profession and conclusion and recommendations

Competitiveness of the banking industry in Singapore.

The local banking sector is currently under major revamps. Our FYP attempts to look at these revamps that are taking place and we try to evaluate the rationals and the effects of these revamps

Analysing cycles in stock price indices using fast fourier transform.

In our research, we explore, using Fast Fourier Transform to identify the presence of cycles in stock price indices. In addition, we analyse the stability of these prices over time and hence the feasibility of this technique in the prediction of stock price indices

Edwards-Bell-Ohlson valuation model : an evaluation of Singapore stocks 1995-1999.

In this study, the EBO is tested in the Singapore stock market from 1995-1999 to evaluate its ability in estimating intrinsic value as well as its predictive power in forecasting stock returns

Energy level alignment at the methylammonium lead iodide/copper phthalocyanine interface

The energy level alignment at the CH3NH3PbI3/copper phthalocyanine (CuPc) interface is investigated by X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS). XPS reveal a 0.3 eV downward band bending in the CuPc film. UPS validate this finding and further reveal negligible interfacial dipole formation – verifying the viability of vacuum level alignment. The highest occupied molecular orbital of CuPc is found to be closer to the Fermi level than the valance band maximum of CH3NH3PbI3, facilitating hole transfer from CH3NH3PbI3 to CuPc. However, subsequent hole extraction from CuPc may be impeded by the downward band bending in the CuPc layer.Published versio

SFPQ promotes RAS-mutant cancer cell growth by modulating 5 '-UTR mediated translational control of CK1 alpha

10.1093/narcan/zcac027NAR CANCER4