Sibt ul Hussain

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This paper proposes a novel face representation based on Local Quantized Patterns (LQP). LQP is a generalization of local pattern features that makes use of vector quantization and lookup table to let local pattern features have many more pixels and/or quantization levels without sacrificing simplicity and computational efficiency. Our new LQP face(More)
This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been shown recently that small local pixel pattern distributions can be highly discriminative.(More)
We describe a family of object detectors that provides state-of-the-art error rates on several important datasets including INRIA people and PASCAL VOC’06 and VOC’07. The method builds on a number of recent advances. It uses the Latent SVM learning framework and a rich visual feature set that incorporates Histogram of Oriented Gradient, Local Binary Pattern(More)
Removing perspective distortion from hand held camera captured document images is one of the primitive tasks in document analysis, but unfortunately no such method exists that can reliably remove the perspective distortion from document images automatically. In this paper, we propose a convolutional neural network based method for recovering homography from(More)
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