Image Definition Evaluations on Denoised and Sharpened Wood Grain Images

Decorative paper and wood veneer have been widely used in the surface decoration of wood-based panels. These surface decoration methods require two-dimensional image acquisition of natural wood grain to obtain the digital grain. However, optically scanned images sometimes produce noise during the process of image acquisition and transmission. In this situation, scanned images cannot be used directly in wood grain reproductions. To reduce noise and retain or strengthen the image sharpness, studies are mostly aimed at the improvement of classic denoising algorithms and edge width-based sharpness evaluation algorithms. To enhance accessibility for common users, four kinds of wood grain images with distinct colors were chosen, and the noise filter (Dust & Scratches) and sharpen filter (Unsharp Mask, USM) were used to denoise and sharpen the images. According to the properties of the two filters, image definition in this study was considered from two aspects: detail retention and sharpness retention. To have an objective evaluation on the definition of denoised and sharpened images, two types of evaluation functions Roberts gradient function (RGF) and modulation transfer function (MTF) were introduced. The purpose of this study was to estimate the image definition by exploring the relationships between the evaluation functions and the commonly used filters in order to allow the required wood grain images to be quickly and accurately processed by common users. The results showed that RGF was only applicable to the case where the two parameters in Dust & Scratches were changed individually, while MTF was not suitable in any case. When both parameters were changed, the required denoising scheme could be obtained through PSNR and SSIM. For the images with distinct colors, even if they were acquired in the same way, denoising them with the same parameter setting was not recommended. For sharpness retention, the values of Radius and Amount in USM were given, and when the Threshold value was set to 20 (levels), the sharpness of the wood grain images barely changed. In this case, both RGF and MTF were suitable to evaluate the sharpness of the wood grain images sharpened by USM.

Pattern of the literary work (based on the “formula” of the literary work of S. I. Sukhikh)

The subject of this research is the structure of the literary work. The goal consists in the establishment of correlation between the parts of the literary work, expressed in the research of S. I. Sukhikh “The Theoretical Poetics of A. A. Potebnja”. The author meticulously examines such aspects of the theory of literary work of A. A. Potebnja as the interrelation between the external form, internal form, artistic image, and content of the literary work, as well as outlines the structure of the literary work. The article describes the parts of the literary work (external form, internal form, artistic image, content). The article offers the patter of the literary work, distinguishing the parts and interrelation between them. The author formulates the definitions of external form, internal form, artistic image, and content of the literary work; reveals the characteristics and distinctive features of each part of the literary work. This led to the formation of representation of the literary work as a structure, where each of the parts expresses its own meaning, and serves as a means of creating the meaning for the other part. The result this research consists in the creation of the pattern of literary work that gives an idea on the structure of the literary work. The acquired materials can be used in studying the general concepts of the theory of literature: the form, content, artistic image, definition of artistic merit of the literary work. The offered pattern of the literary work cab be also applied to the analysis of the literary work. The scientific novelty of this study lies in the formation of representation of the literary work as a structure, where each part expresses its own meaning and serves as a means of creating meaning for the other part, as well as in creation of the pattern that conveys this idea.

Genetic Feature Fusion for Object Skeleton Detection

Object skeleton detection requires the convolutional neural networks to recognize objects and their parts in the cluttered background, overcome the image definition degradation brought by the pooling layers, and predict the location of skeleton pixels in different scale granularity. Most existing object skeleton detection methods take great efforts into the designing of side-output networks for multiscale feature fusion. Despite the great progress achieved by them, there are still many problems that hinder the development of object skeleton detection, such as the manually designed network is labor-intensive and the network initialization depends on models pretrained on large-scale datasets. To alleviate these issues, we propose a genetic NAS method to automatically search on a newly designed architecture search space for adaptive multiscale feature fusion. Furthermore, we introduce a symmetric encoder-decoder search space based on reversing the VGG network, in which the decoder can reuse the ImageNet pretrained model of VGG. The searched networks improve the performance of the state-of-the-art methods on commonly used skeleton detection benchmarks, which proves the efficacy of our method.

The value of three narrow-band imaging model in the diagnosis of small colorectal polyps

This prospective study was aimed to evaluate the clinical value of narrow-band imaging (NBI), magnification endoscopy with narrow-band imaging (NBIME) and magnification endoscopy with acetic acid enhancement and narrow-band imaging (AA-NBIME) in the diagnosis of small colorectal polyps. We studied 261 small colorectal polyps from 122 patients with the use of above three techniques. Lesions were resected for histopathological analysis. The endoscopic images were independently reviewed by three experts and three non-experts and the diagnostic accuracy and image definition were compared among the modalities. The “experts-agreed” diagnostic accuracy was 87.7% for NBI versus 91.6% for NBIME versus 94.6% for AA-NBIME. The “non-experts-agreed” diagnostic accuracy was 80.1% for NBI versus 84.3% for NBIME versus 89.3% for AA-NBIME. All experts and non-experts diagnosed the small colorectal polyps statistically more accurately with AA-NBIME than NBI (P < 0.05). In all three modalities, the expert group’s diagnostic accuracies were statistically significantly higher compared with the non-expert group. For experts, the Kappa values for AA-NBIME, NBIME and NBI diagnosis were 0.962 (0.892–1.032), 0.577 (0.507–0.647) and 0.567 (0.497–0.637), respectively; while for nonexperts, 0.818 (0.748–0.888), 0.532 (0.462–0.602) and 0.530 (0.460–0.600). This demonstrated a good reproducibility of AA-NBIME diagnosis. The average scores (experts and non-experts) of images acquired using AA-NBIME were significantly higher than those acquired using NBIME and ME (P < 0.05). AA-NBIME is a promising tool to clearly visualize the mucosal pit pattern (PP) of colorectal polyps for better differentiating neoplastic polyps from non-neoplastic ones.

Dynamic Visual Noise Does Not Affect Memory for Fonts

Dynamic visual noise (DVN) selectively impairs memory for some types of stimuli (e.g., colors, textures, concrete words), but not for others (e.g., matrices, Chinese characters, simple shapes). According to the image definition hypothesis, the key difference is whether the stimulus leads to images that are ill-defined or well-defined. The former will be affected because the addition of noise quickly reduces the usefulness of the image in supplying information about the item's identity. The image definition hypothesis predicts that fonts should lead to ill-defined images and therefore should be affected by DVN, and although three previous studies appear to show this result, they lack a key control condition and report only proportion correct. Two experiments reassessed whether DVN affects memory for fonts, but, unlike the previous studies, both included a static visual noise condition and both were analyzed using signal detection measures. There was no evidence that DVN affected memory for font information, thus disconfirming a prediction of the original version of image definition hypothesis. We suggest a revised version that focuses on redintegration can explain the results.