Image Reproduction based on Texture Image Extension with Traced Drawing for Heavy Damaged Mural Painting

AbstractNot only geometric information but also optical information is needed to reproduce ruins using three-dimensional realistic computer graphics as they were when those were founded. In order to give a model a sense of reality, it is common to carry out the texture mapping of the photographed image. However such information can not be acquired from either weathered or partially destroyed ruins. While there are various conventional techniques for image restoration, which can overcome in the case of small missing and cracks, it is difficult to restore such a heavy damaged mural painting well when there is no information from the periphery.In this paper, we propose an image reproduction of a heavy damaged mural painting using a texture information extracted from another mural painting which has actually been restored by conservators and a traced drawing which the specialist guessed and drew. The restored image was used same pigment inks. Based on texture information from the restored image and a segmented traced drawing, we produce a restored image by applying the texture extension to each segment

Representation of Swinging Liquid on Virtual Liquid Manipulation

Abstract In this paper, a model to realize an interactive manipulation of virtual liquid using a virtual vessel is described first. Then, this model is extended to represent the swinging liquid in a vessel. The liquid receives reactionary acceleration according to the acceleration of the vessel. This reactionary acceleration is considered to represent swinging liquid. Our system with this proposed model makes it possible to swing the liquid surface with swinging the vessel, then to spill the liquid with swinging it

A novel set of features for continuous hand gesture recognition

Applications requiring the natural use of the human hand as a human–computer interface motivate research on continuous hand gesture recognition. Gesture recognition depends on gesture segmentation to locate the starting and end points of meaningful gestures while ignoring unintentional movements. Unfortunately, gesture segmentation remains a formidable challenge because of unconstrained spatiotemporal variations in gestures and the coarticulation and movement epenthesis of successive gestures. Furthermore, errors in hand image segmentation cause the estimated hand motion trajectory to deviate from the actual one. This research moves toward addressing these problems. Our approach entails using gesture spotting to distinguish meaningful gestures from unintentional movements. To avoid the effects of variations in a gesture’s motion chain code (MCC), we propose instead to use a novel set of features: the (a) orientation and (b) length of an ellipse least-squares fitted to motion-trajectory points and (c) the position of the hand. The features are designed to support classification using conditional random fields. To evaluate the performance of the system, 10 participants signed 10 gestures several times each, providing a total of 75 instances per gesture. To train the system, 50 instances of each gesture served as training data and 25 as testing data. For isolated gestures, the recognition rate using the MCC as a feature vector was only 69.6 % but rose to 96.0 % using the proposed features, a 26.1 % improvement. For continuous gestures, the recognition rate for the proposed features was 88.9 %. These results show the efficacy of the proposed method

Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair

Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7

The importance of real-time vehicle detection tracking and counting system based on YOLOv7 is an important tool for monitoring traffic flow on highways. Highway traffic management, planning, and prevention rely heavily on real-time traffic monitoring technologies to avoid frequent traffic snarls, moving violations, and fatal car accidents. These systems rely only on data from timedependent vehicle trajectories used to predict online traffic flow. Three crucial duties include the detection, tracking, and counting of cars on urban roads and highways as well as the calculation of statistical traffic flow statistics (such as determining the real-time vehicles flow and how many different types of vehicles travel). Important phases in these systems include object detection, tracking, categorizing, and counting. The YOLOv7 identification method is presented to address the issues of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban highways, weak perspective perception of small targets, and insufficient feature extraction. This system aims to provide real-time monitoring of vehicles, enabling insights into traffic patterns and facilitating informed decision-making. In this paper, vehicle detecting, tracking, and counting can be calculated on real-time videos based on modified YOLOv7 with high accuracy

Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network

Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment