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We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations...
It was proved that the fusion of information from multi-modality images increases the accuracy of pedestrian recognition systems. One of the best approache so far is to concatenate the features from multi-modality images into a large feature vector, but it requires strong camera calibration settings and non-discriminative modalities could lead to missclassification of some particular images. We present...
Once the human vision system has seen a 3D object from a few different viewpoints, depending on the nature of the object, it can generally recognize that object from new arbitrary viewpoints. This useful interpolative skill relies on the highly complex pattern matching systems in the human brain, but the general idea can be applied to a computer vision recognition system using comparatively simple...
Blur is often present in real-world images and significantly affects the performance of face recognition systems. To improve the recognition of blurred faces, we propose a new approach which inherits the advantages of two recent methods. The idea consists of first reducing the amount of blur in the images via deblurring and then extracting blur-tolerant descriptors for recognition. We assess our analysis...
This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions and non-Lambertian effects. The ability to model translation and scale...
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