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Pixel-level labelling tasks, such as semantic segmenta-tion, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to de-lineate(More)
Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elements for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial distribution of(More)
Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust(More)
The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. 'I see a shiny red chair'). In this paper, we formulate the problem of joint visual attribute and object class image seg-mentation as a dense multi-labelling problem, where each pixel in an image(More)
Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixels. In this article we propose treating nouns(More)
We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consistent with the image's visual features. Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF(More)
Pedestrian tracking in multi-camera is an important task in intelligent visual surveillance system, but it suffers from the problem of large appearance variations of the same person under different cameras. Inspired by the success of existing view transformation model in multi-view gait recognition, we present a novel view transformation model based(More)
— Given a face detection, facial feature detection involves localizing the facial landmarks such as eyes, nose, mouth. Within this paper we examine the learning of the appearance model in Constrained Local Models (CLM) technique. We have two contributions: firstly we examine an approximate method for doing structured learning, which jointly learns all the(More)
Ground reaction force can be used to distinguish different human gait like limb movement. Cumulative foot pressure image is a 2-D data that recorded the spatial and temporal change of ground reaction force during one gait cycle. However , when putting it into practice as a new biometric for gait recognition, it suffers from the problem of large translation(More)