An experimental evaluation of visual similarity for HDR images

In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control factors include choice of tone mapping operator (TMO), choice of distance metric, and choice of image feature. The image features that we experiment with are chosen from the features that are commonly used in the usual low dynamic range setting including features learned via Convolutional Neural Networks. The set of image features also includes combined features where the combination coefficients are estimated using logistic regression. We compute correlations between human judgments and quantitative features to understand how much each feature contributes to visual similarity. Combined features yield nearly 84% agreement with human judgments when applied on tone mapped images. Though we observed that using common features directly on raw or linearly scaled HDR images yield subpar correlation estimates compared to using them on tone mapped HDR images, we did not observe significant effect due to the choice of TMO on the estimates. As an application, we propose an improvement to style-based tone mapping for more correctly imparting desired styles to HDR images with different characteristics

Style based tone mapping for HDR images

In this paper we propose a different approach to high dynamic range (HDR) image tone mapping. We put away the assumption that there is a single optimal solution to tone mapping. We argue that tone mapping is inherently a personal process that is guided by the taste and preferences of the artist; different artists can produce different depictions of the same scene. However, most existing tone mapping operators (TMOs) compel the artists to produce similar renderings. Operators that give more freedom to artists require adjustment of many parameters which turns tone mapping into a laborious process. In contrast to these, we propose an algorithm which learns the taste and preferences of an artist from a small set of calibration images. Any new image is then tone mapped to convey the appearance that would be desired by the artist

Is ultrasonography useful in the diagnosis of the polyneuropathy in diabetic patients?

[Purpose] The aim of this study was to investigate the usefulness of ultrasonography for the diagnosis of polyneuropathy in diabetic patients by examination of the median and ulnar nerves. [Subjects and Methods] Sixty-three diabetic patients and fourteen controls were enrolled in the study. Nerve conduction studies were performed on both upper and lower limbs. Median and ulnar nerve cross-sectional areas were measured at the wrist and forearm levels in 140 hands by ultrasound. [Results] The median nerve cross-sectional area was increased at the hook of hamatum, pisiform bone, and radioulnar joint levels in patients with carpal tunnel syndrome. The ulnar nerve area at the medial epicondyle was significantly increased in the diabetic polyneuropathy (9.2 ± 1.6), diabetic polyneuropathy plus carpal tunnel syndrome (9.3 ± 1.4), and carpal tunnel syndrome (9.2 ± 1.9) groups compared with the control group (7.7 ± 1.1). In receiver operating characteristics analysis, the cutoff value of the ulnar nerve was 8.5 mm2 at ulnar epicondyle with 71.4% specificity and 70.4% sensitivity, corresponding to the highest diagnostic accuracy for diabetic polyneuropathy. [Conclusion] Ultrasonographic examination of the median and ulnar nerves can be an alternative or additional diagnostic modality for the evaluation of neuropathies in diabetic patients