Topic
Naturalness
About: Naturalness is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 31737 citations.
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TL;DR: Zhang et al. as discussed by the authors proposed an effective no-reference Enhanced Colonoscopy Image Quality (ECIQ) method to automatically evaluate the perceptual quality of ECIs via analysis of brightness, contrast, colorfulness, naturalness, and noise.
Abstract: In colonoscopy, the captured images are usually with low-quality appearance, such as non-uniform illumination, low contrast, etc., due to the specialized imaging environment, which may provide poor visual feedback and bring challenges to subsequent disease analysis. Many low-light image enhancement (LIE) algorithms have recently proposed to improve the perceptual quality. However, how to fairly evaluate the quality of enhanced colonoscopy images (ECIs) generated by different LIE algorithms remains a rarely-mentioned and challenging problem. In this study, we carry out a pioneering investigation on perceptual quality assessment of ECIs. Firstly, considering the lack of specific datasets, we collect 300 low-light images with diverse contents during the real-world colonoscopy and conduct rigorous subjective studies to compare the performance of 8 popular LIE methods, resulting in a benchmark dataset (named ECIQAD) for ECIs. Secondly, in view of the distinctive distortion characteristics of ECIs, we propose an effective no-reference Enhanced Colonoscopy Image Quality (ECIQ) method to automatically evaluate the perceptual quality of ECIs via analysis of brightness, contrast, colorfulness, naturalness, and noise. Extensive experiments on ECIQAD demonstrate the superiority of our proposed ECIQ method over 14 mainstream no-reference image quality assessment methods.
3 citations
TL;DR: MarkCRQA as discussed by the authors attaches the marks to the entities in questions, answers, and knowledge base, which avoids the additional recognition process for entity types and reduces the training difficulty.
Abstract: Knowledge-based natural answer generation systems are generally difficult to train because numerous entities rarely appear. One way is to replace the entities with their respective types. However, the entity type requires additional recognition with limited accuracy and ignores semantic meanings. Consequently, we propose a question answering system with flexible marks, copying, and retrieving mechanisms (MarkCRQA) to generate natural and accurate answers. By requiring random marks to be shared among all entity types, MarkCRQA attaches the marks to the entities in questions, answers, and knowledge base, which avoids the additional recognition process for entity types and reduces the training difficulty. In addition, we propose to finely control the naturalness and knowledge level of each answer for different real-world scenarios and user needs. Experiments show that MarkCRQA achieves state-of-the-art performance on two open-domain question answering datasets.
3 citations
TL;DR: In this article , the authors estimate the naturalness of Swiss forests by comparing the tree species composition (i.e., dominance and presence/absence of tree species) recorded by the national forest inventory with the idealised species composition of the potential natural forests.
3 citations
01 Sep 1969
TL;DR: A recently proposed level-oriented model for machine analysis and synthesis of natural languages is investigated and claims concerning the preservation of context-free languages in such a system are examined and shown to be unjustified.
Abstract: In this paper, a recently proposed level-oriented model for machine analysis and synthesis of natural languages is investigated. Claims concerning the preservation of context-free (CF) languages in such a system are examined and shown to be unjustified. Furthermore, it is shown that even a revised version of the model (incorporating some recent discoveries) will not be CF-preserving. Finally, some theoretical implications of these findings are explored: in particular, claims of greater naturalness and the question of recursivity.
3 citations