Pranam Janney

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Large amounts of available training data and increasing computing power have led to the recent success of deep con-volutional neural networks (CNN) on a large number of applications. In this paper, we propose an effective semantic pixel labelling using CNN features, hand-crafted features and Conditional Random Fields (CRFs). Both CNN and hand-crafted(More)
In this paper, we present a new rotation-invariant texture descriptor algorithm called Invariant Features of Local Textures (IFLT). The proposed algorithm extracts rotation invariant features from a small neighbourhood of pix-els around a centre pixel or a texture patch. Intensity vector which is derived from a texture patch is normalized and Haar wavelet(More)
In digital airborne electro-optical imagery, the identification of objects, particularly vehicles, has an important role in wide-area search and surveillance applications. We propose an identification and pose estimation approach based on maximising the correlation of features in an image with projections of 3D models. It has been applied to imagery(More)
Intelligent machines require basic information such as moving-object detection from videos in order to deduce higher-level semantic information. In this paper, we propose a methodology that uses a texture measure to detect moving objects in video. The methodology is computationally inexpensive, requires minimal parameter fine-tuning and also is resilient to(More)