Person re-identification has recently attracted increasing interest in the computer vision and safety-critical applications. In practice, due to poor quality of cameras or long distance away from person, the captured pedestrian videos usually suffer from low resolution, which will result in the loss of useful information contained in videos and make person re-identification between low-resolution (LR) and high-resolution (HR) videos (PRLHV) be a challenging task. However, the problem of PRLHV has not been well studied. In this paper, we propose a semi-coupled mapping based set-to-set distance learning (SMDL) approach for PRLHV. Specifically, by regarding each video as a set of features extracted from several walking cycles, we learn a discriminative set-to-set distance metric to enhance the separability between videos from different persons. To decrease the influence of low resolution on the distance learning, we design a clustering-based semi-coupled mapping term for our approach, which can reduce the variation between features of low-resolution and high-resolution videos by a semi-coupled mapping matrix. Since there exists no low-resolution video pedestrian re-identification dataset under real-world scenario up to now, we contribute a benchmark dataset for PRLHV, named high-resolution and low-resolution video person reidentification dataset (HLVID). Although this dataset is challenging and difficult for person re-identification, it is a useful attempt for further studies on low-resolution video-based pedestrian re-identification under the real-world scene. The extensive experiments on the newly collected video dataset demonstrate that our approach performs better than the state-of-the-art person re-identification methods in the PRLHV task. INDEX TERMS Distance learning, low-resolution video-based pedestrian dataset, semi-coupled mapping, person re-identification.
Optical coherence tomography (OCT) is an imaging modality that acquires high‐resolution cross‐sectional images of living tissues and it has become the standard in ophthalmological diagnoses. However, most quantitative morphological measurements are based on the raw OCT images which are distorted by several mechanisms such as the refraction of probe light in the sample and the scan geometries and thus the analysis of the raw OCT images inevitably induced calculation errors. In this paper, based on Fermat's principle and the concept of inverse light tracing, image distortions due to refraction occurred at tissue boundaries in the whole‐eye OCT imaging of mouse by telecentric scanning were corrected. Specially, the mathematical correction models were deducted for each interface, and the high‐precision whole‐eye image was recovered segment by segment. We conducted phantom and in vivo experiments on mouse and human eyes to verify the distortion correction algorithm, and several parameters of the radius of curvature, thickness of tissues and error, were calculated to quantitatively evaluate the images. Experimental results demonstrated that the method can provide accurate and reliable measurements of whole‐eye parameters and thus be a valuable tool for the research and clinical diagnosis.
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