A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.

Quantitative Investigation of Tomographic Effects in Abnormal Regions of Complex Structures

The detection of abnormal regions in complex structures is one of the most challenging targets for underground space engineering.Natural or artificial geologic variations reduce the effectiveness of conventional exploration methods.With the emergence of real-time monitoring,seismic wave velocity tomography allows the detection and imaging of abnormal regions to be accurate,intuitive,and quantitative.Since tomographic results are affected by multiple factors in practical small-scale applications,it is necessary to quantitatively investigate those influences.We adopted an improved three-dimensional(3D)tomography method combining passive acoustic emission acquisition and active ultrasonic measurements.By varying individual parameters(i.e.,prior model,sensor configuration,ray coverage,event distributions,and event location errors),37 comparative tests were conducted.The quantitative impact of different factors was obtained.Synthetic experiments showed that the method could effectively adapt to complex structures.The optimal input parameters based on quantization results can significantly improve the detection reliability in abnormal regions.

Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach

BACKGROUND Artificial intelligence and deep learning have shown promising results in medical imaging and interpreting radiographs.Moreover,medical community shows a gaining interest in automating routine diagnostics issues and orthopedic measurements.AIM To verify the accuracy of automated patellar height assessment using deep learning-based bone segmentation and detection approach on high resolution radiographs.METHODS 218 Lateral knee radiographs were included in the analysis.82 radiographs were utilized for training and 10 other radiographs for validation of a U-Net neural network to achieve required Dice score.92 other radiographs were used for automatic(U-Net)and manual measurements of the patellar height,quantified by Caton-Deschamps(CD)and Blackburne-Peel(BP)indexes.The detection of required bones regions on high-resolution images was done using a You Only Look Once(YOLO)neural network.The agreement between manual and automatic measurements was calculated using the interclass correlation coefficient(ICC)and the standard error for single measurement(SEM).To check U-Net's generalization the segmentation accuracy on the test set was also calculated.RESULTS Proximal tibia and patella was segmented with accuracy 95.9%(Dice score)by U-Net neural network on lateral knee subimages automatically detected by the YOLO network(mean Average Precision mAP greater than 0.96).The mean values of CD and BP indexes calculated by orthopedic surgeons(R#1 and R#2)was 0.93(±0.19)and 0.89(±0.19)for CD and 0.80(±0.17)and 0.78(±0.17)for BP.Automatic measurements performed by our algorithm for CD and BP indexes were 0.92(±0.21)and 0.75(±0.19),respectively.Excellent agreement between the orthopedic surgeons’measurements and results of the algorithm has been achieved(ICC>0.75,SEM<0.014).CONCLUSION Automatic patellar height assessment can be achieved on high-resolution radiographs with the required accuracy.Determining patellar end-points and the joint line-fitting to the proximal tibia joint surface allows for accurate CD and BP index calculations.The obtained results indicate that this approach can be valuable tool in a medical practice.

Review of Theory and Methods of Image Segmentation

Image segmentation refers to the technique and process of partitioning a digital image into multiple segments based on image characteristics so as to extract the object of interest from it. It is a key step from image processing to image analysis. In the mid-1950s, people began to study image segmentation. For decades, various methods for image segmentation have been proposed. In this paper, traditional image segmentation methods and some new methods appearing in recent years were reviewed. Thresholding segmentation methods, region-based, edge detection-based and segmentation methods based on specific theoretical tools were introduced in detail.

Infrared and Visible Image Fusion Based on Region of Interest Detection and Nonsubsampled Contourlet Transform

In order to enhance the contrast of the fused image and reduce the loss of fine details in the process of image fusion,a novel fusion algorithm of infrared and visible images is proposed.First of all,regions of interest(RoIs)are detected in two original images by using saliency map.Then,nonsubsampled contourlet transform(NSCT)on both the infrared image and the visible image is performed to get a low-frequency sub-band and a certain amount of high-frequency sub-bands.Subsequently,the coefcients of all sub-bands are classified into four categories based on the result of RoI detection:the region of interest in the low-frequency sub-band(LSRoI),the region of interest in the high-frequency sub-band(HSRoI),the region of non-interest in the low-frequency sub-band(LSNRoI)and the region of non-interest in the high-frequency sub-band(HSNRoI).Fusion rules are customized for each kind of coefcients and fused image is achieved by performing the inverse NSCT to the fused coefcients.Experimental results show that the fusion scheme proposed in this paper achieves better efect than the other fusion algorithms both in visual efect and quantitative metrics.

Shadow regions detection algorithm by adaptive narrowband two-phase Chan-Vese model

An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.

DETECTION OF FOREST FIRE IN DA HINGGAN LING REGION BY METEOROLOGICAL SATELLITE

From May 6 to June 2,1987,a huge forest fire broke out and raged for 28 days in Da Hinggan Ling region in far Northeast China,causing heavy loss of life and property,which claims the biggest forest fire disaster in Chinese history.The fire drew attention of the whole of China and was also concerned by many other countries.How were the meteorological satellites used in the detection of the forest fire?This paper elaborates the principles and methods of the fire detection using meteorological satellites,so that to sum up the experience and to increase the ability of forest fire detection.

3D reconstruction of movable cultural relics based on salient region optimization

How to protect cultural retics is of great significance to the transmission and dissemination of history and culture.Digital 3-dimensional(3D)modeling of cultural relics is an effective way to preserve them.The efficiency and complexity of cultural relic model reconstruction algorithms are significant challenges due to redundant data.To tackle the above issue,a 3D reconstruction algorithm,named COLMAP+LSH,was proposed for movable cultural relics based on salient region optimization.COLMAP+LSH algorithm introduces saliency region detection and locality-sensetive Hashing(LSH)to achieve efficient,accurate,and robust digital 3D modeling of cultural relics.Specifically,400 cultural model data were collected through offline and online collection.COLMAP+LSH algorithm detects the salient region interactively and reduces the number of images in the salient region by feature diffusion.Additionally,COLMAP+LSH algorithm utilizes LSH to calculate the image selection scores and employs the image selection scores to reduce the redundant image.The experiments on the self-constructed cultural relics dataset show that COLMAP+LSH algorithm can efficiently achieve image feature diffusion and ensure the quality of artifact reconstruction while selecting most of the redundant image data.

Pre-Earthquake Ionospheric Anomalies of the Wenchuan Earthquake Studied with DEMETER Satellite

The pre-earthquake ionospheric anomalies in Wenchuan,China(21°-41°N,93°-113°E)are studied and analyzed using the summer nighttime data from 2005 to 2008 measured by DEMETER(Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions)satellite detectors ICE(Internet Communications Engine),IAP(In Application Programming),and ISL(Interior Switching Link).In this paper,we take the 12 May 2008 Wenchuan earthquake as an example,use the spatial gridding method to construct the background field over the epicenter,analyze the background characteristics of very low frequency(VLF)electric field components,low-energy particle parameters,and plasma parameters,and define the perturbation intensity index of each parameter before the earthquake to extract each parameter anomaly in both space and time dimensions.The results show that the background values of some ionospheric parameters in the Wenchuan area are related to spatial distribution.Moreover,anomalous enhancement of low-frequency electric field power spectral density,H+concentration,He+concentration and ion concentration with different intensities and anomalous weakening of ion temperature were extracted in the fifteen days before the Wenchuan earthquake.After filtering the data to exclude external interference,such as solar activity,this paper concludes that there is some connection between these anomalies and the Wenchuan earthquake.

Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective

The use of multi-perspective and multi-scalar city networks has gradually developed into a range of critical approaches to understand spatial interactions and linkages. In particular, road linkages represent key characteristics of spatial dependence and distance decay, and are of great significance in depicting spatial relationships at the regional scale. Therefore, based on highway passenger flow data between prefecture-level administrative units, this paper attempted to identify the functional structures and regional impacts of city networks in China, and to further explore the spatial organization patterns of the existing functional regions, aiming to deepen our understanding of city network structures and to provide new cognitive perspectives for ongoing research. The research results lead to four key conclusions. First, city networks that are based on highway flows exhibit strong spatial dependence and hierarchical characteristics, to a large extent spatially coupled with the distributions of major megaregions in China. These phenomena are a reflection of spatial relationships at regional scales as well as core-periphery structure. Second, 19 communities that belong to an important type of spatial configuration are identified through community detection algorithm, and we suggest they are correspondingly urban economic regions within urban China. Their spatial metaphors include the administrative region economy, spatial spillover effects of megaregions, and core-periphery structure. Third, each community possesses a specific city network system and exhibits strong spatial dependence and various spatial organization patterns. Regional patterns have emerged as the result of multi-level, dynamic, and networked characteristics. Fourth, adopting a morphology-based perspective, the regional city network systems can be basically divided into monocentric, dual-nuclei, polycentric, and low-level equilibration spatial structures, while most are developing monocentrically.