Color Image Landscape Photo Hand-Painted Effect Evaluation with PFA Algorithm

With the continuous development of society and economy, diversified presentation and publicity of landscape are becoming more and more popular among the public, such as using color pictures to display multimedia publicity videos or APP, WeChat public accounts, and other new media. There are various ways of expression. Against the limitations of color image landscape, this article introduced the PFA algorithm, during the experiment of hand-painted landscape pictures, the effect of color hand-painted images is evaluated by using the coordination degree of natural target detection scene, and the corresponding image evaluation index values are obtained for effective analysis, and the prediction model is established. The simulation results show that the PFA algorithm is effective and can support the evaluation of the hand-painted effect of color landscape photos.

Biokinetic Evaluation of Contrast Media Loaded Carbon Nanotubes Using a Radiographic Device

Considerable progress has been made in various fields of applied research on the use of carbon nanotubes (CNTs). Because CNTs are fibrous nanomaterials, biosafety of CNTs has been discussed. The biokinetic data of CNTs, such as using the radioisotope of carbon and surface labeling of CNTs, have been reported. However, the use of radioisotopes requires a special facility. In addition, there are problems in the surface labeling of CNTs, including changes in surface properties and labels eliminating over time. In order to solve these problems and properly evaluate the biokinetics of CNTs, the authors synthesize peapods with platinum (Pt) encapsulated within the hollow region of Double-Walled CNTs (DWCNTs) and develop a new system to evaluate biokinetics using widely available imaging equipment. In the cell assay, no significant difference is observed with and without Pt in CNTs. In animal studies, radiography of the lungs of rats that inhaled Pt-peapods show the detectability of Pt inside the CNTs. This new method using Pt-peapods enables image evaluation with a standard radiographic imaging device without changing the surface property of the CNTs and is effective for biokinetics evaluation of CNTs.

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.

A Study on Consumers’ Visual Image Evaluation of Wrist Wearables

This study aimed to investigate consumers’ visual image evaluation of wrist wearables based on Kansei engineering. A total of 8 representative samples were screened from 99 samples using the multidimensional scaling (MDS) method. Five groups of adjectives were identified to allow participants to express their visual impressions of wrist wearable devices through a questionnaire survey and factor analysis. The evaluation of eight samples using the five groups of adjectives was analyzed utilizing the triangle fuzzy theory. The results showed a relatively different evaluation of the eight samples in the groups of “fashionable and individual” and “rational and decent”, but little distinction in the groups of “practical and durable”, “modern and smart” and “convenient and multiple”. Furthermore, wrist wearables with a shape close to a traditional watch dial (round), with a bezel and mechanical buttons (moderate complexity) and asymmetric forms received a higher evaluation. The acceptance of square- and elliptical-shaped wrist wearables was relatively low. Among the square- and rectangular-shaped wrist wearables, the greater the curvature of the chamfer, the higher the acceptance. Apparent contrast between the color of the screen and the casing had good acceptance. The influence of display size on consumer evaluations was relatively small. Similar results were obtained in the evaluation of preferences and willingness to purchase. The results of this study objectively and effectively reflect consumers’ evaluation and potential demand for the visual images of wrist wearables and provide a reference for designers and industry professionals.

Brain CT Registration Using Hybrid Supervised Convolutional Neural Network

Background: Brain computed tomography (CT) image registration is an essential step in the image evaluation of acute cerebrovascular disease (ACVD). Due to the complexity of human brain morphology, low brain CT soft-tissue resolution, low gray/white matter contrast, and the large anatomy variation across individuals, it is still a great challenge to perform brain CT registration accurately and rapidly. This study developed a hybrid supervised convolutional neural network (HSCN-Net) which may be used for assessment of ACVD in brain CT.Method: HSCN-Net generates synthetic deformation fields by a simulator to solve the lack of registration gold standard. The simulator are used to generate multi-scale deformation fields to overcome the registration challenge of large deformation. HSCN-Net adopts a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization ability. In this work, one hundred and one brain CT images were included for HSCN-Net training and evaluation, and the results were compared with Demons and VoxelMorph. Qualitative analysis by visual evaluation, as well as quantitative analysis by Endpoint Error (EPE) between deformation fields, image Normalized Mutual Information (NMI), and Dice coefficient were carried out to access the model performance.Results: Qualitative analysis of HSCN-Net was similar to that of Demons, and both were superior to that of VoxelMorph. Moreover, HSCN-Net was more competent for large and smooth deformations. For quantitative evaluation, the EPE mean of HSCN-Net (3.29 mm) was lower than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the Dice mean of HSCN-Net was 0.96, which was better than that of Demons (0.90) and VoxelMorph (0.87); and the NMI mean of HSCN-Net (0.83) was slightly lower than that of Demons(0.84) but higher than that of VoxelMorph (0.81). In addition, the mean registration time of HSCN-Net (17.86 s) was lower than that of VoxelMorph (18.53 s) and Demons (147.21 s).Conclusion: The proposed hybrid supervised convolution registration network can achieve accurate and rapid brain CT registration. It is helpful for improving image evaluation of ACVD, thereby assisting clinicians in diagnosis and treatment decision-making.

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.