MATHEMATICAL MODELING AND STABILITY ANALYSIS OF VISUAL LOCALIZATION ALGORITHMS UNDER BRIGHTNESS AND NOISE VARIATIONS

This study aimed to assess the influence of the file format on the image quality parameters (image noise, brightness, and uniformity) of periapical radiographs acquired with different digital systems. Radiographic images of an acrylic phantom were acquired with two digital systems - Digora Toto and Express, and exported into five different file formats - TIFF, BMP, DICOM, PNG, and JPEG. Image noise, image brightness (mean of gray values), and image uniformity (standard deviation of gray values) were evaluated in all images. A two-way analysis of variance with Tukey's test as a post-hoc test was used to compare the results, considering the file formats and radiographic systems as the studied factors. A significance level of 5% was adopted for all analyses. The DICOM image file format presented lower image noise, higher brightness (higher mean gray values), and greater image uniformity (p<0.001) than the other file formats, which did not differ from each other for both digital radiography systems (p>0.05). The Express system revealed lower image noise and greater image uniformity than the Digora Toto system regardless of the image file format (p<0.001). Moreover, the Express showed higher brightness than the Digora Toto for all image file formats (p<0.001), except for the DICOM image file format, which did not significantly differ between the digital radiography systems tested (p>0.05). The DICOM image file format showed lower image noise, higher brightness, and greater image uniformity than the other file formats (TIFF, BMP, PNG, and JPEG) in both digital radiography systems tested.

On accuracy of region based localization algorithms for wireless sensor networks

Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of Patlak K i can be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images of K i . In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of Patlak K i . Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications.

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945–5.301 m2/m3. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots.

Whole-body parametric PET imaging along with Patlak graphical analysis has the potential to provide improved diagnosis. However, a voxel-based fitting approach for a short dynamic scan protocol results in high statistical noise in the parametric images. The objective of our study is to present the framework of a novel multiple clustering realizations (MCR) method for estimating parametric images with improved image quality. The method relies primarily on using standard k-means clustering for segmenting the time-activity curves within the whole-body volume. In addition, in order to obtain improved accuracy without increasing noise, multiple realizations of clustering were performed. During each realization, cluster centers were selected from a unique ordered set of time-activity curves within the whole body volume. All the remaining data were classified into the cluster centers based on minimum Eucledian distance measure. Patlak analysis was performed on the cluster average to form the slope and intercept images. Parametric images thus obtained for all realizations were averaged. An XCAT phantom based simulations for the torso were performed using dynamic time-activity curves to model FDG uptake. Five dynamic images each representing 1 min scan time with 7 min intervals were created starting 60 minutes post injection. In addition, 5 whole-body dynamic FDG patient datasets with image-derived blood input function and whole-body dynamic data measurements were also used. All dynamic data were reconstructed using OSEM applying corrections for image-degrading factors. Slope and intercept parametric images were obtained for the voxel-fitting and MCR method. Noise in a liver region of interest increased as a function of the number of clusters for the simulated data. On the other hand, bias decreased with increasing number of clusters. However, as number of clustering realizations increased, noise reduced and K i estimates stabilized. The parametric images obtained with MCR method showed better image quality compared to voxel-based fitting method for the patient and simulated datasets. Multiple clustering realizations method has the potential to provide improved parametric image quality for short scan whole-body parametric PET imaging.

Effectiveness Comparison of Visual and Semantic Features for Noise Image Removal

The purpose of the study is to perform phantom-based optimisation of paediatric computed tomography (CT) protocols and quantify the impact upon radiation dose and image noise levels. The study involved three Portuguese paediatric centres. Currently employed scanning protocols for head and chest examinations and combinations of exposure parameters were applied to a Catphan(®)600 phantom to review the CT dose impact. Contrast-noise ratio (CNR) was quantified using Radia Diagnostic(®) tool. Imaging parameters, returning similar CNRs (<1) and dose savings were applied to three paediatric anthropomorphic phantoms. OsiriX software based on standard deviation pixel values facilitated image noise analysis. Currently employed protocols and age categorisation varied between centres. Manipulation of exposure parameters facilitated mean dose reductions of 33 and 28 % for paediatric head and chest CT examinations, respectively. The majority of the optimised CT examinations resulted in image noise similar to currently employed protocols. Dose reductions of up to 33 % were achieved with image quality maintained.

Impact of image noise levels, scout scan dose and lens shield on image quality and radiation exposure in z-axis dose-modulated neck MSCT on 16- and 64-slice Toshiba Aquilion scanners

The article is devoted to studying the chasing problem games regarding to the levels of digital image brightness, described by discrete second order linear equations. We obtained sufficient conditions for finish of chase. Partly on a model example we showed that using the management in specified area, you could define a certain level of brightness in a digital image in case of presence of the player, which prevented this transition.

Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

Localization is an essential problem in Wireless Sensor Networks (WSNs). Many localization algorithms have been proposed, but few efforts have been paid on theoretical analysis on the accuracy of these algorithms. Because it is naturally to formalize range-based localization problems as deterministic parameter estimation problems, for range-based localization algorithms Cramér-Rao Lower Bound (CRLB) has been used to lower bound the variance on the estimation of sensor's positions. However, few similar works have been done for range-free localization algorithms. In this paper, based on geometry properties, we theoretically analyze bounds on accuracy for Region-Based Localization (RBL) algorithms which can be classified as one type of range-free localization algorithms. We prove that if in a RBL algorithm, the deployment region R with the area size s is partitioned into k regions (they can be with any shape and any area size), the localization accuracy is bounded below by no matter how the algorithm partitions R. Although the lower bound is not theoretically tight, our simulation results show that the gap between this bound and achievable accuracy is very small. We conjecture a tighter lower bound when k is large enough. We also observe that in order to achieve high localization accuracy, partitioned regions should have nearly the same size. We give three examples with simulation results to show how the results can be used to set the values of the parameters, like k and the corresponding anchor/event number, in a RBL algorithm in order to achieve desired localization accuracy.

Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.

Noise in an image is a random variation of brightness or color information in the original image. Noise is consistently presented in digital images during picture obtaining, coding, transmission, and processing steps. Image noise is most apparent in image regions with a low signal level. There are various reasons for the creation of noise in an image, such as electronic noise in amplifiers or detectors, disturbances and overheating of the sensor, disturbances in the medium of traveling for a digital image, etc. Noise is exceptionally hard to eliminate from the digital pictures without the earlier information of the noise model. There are various types of noise that can be available in a noise model. Filters are used to remove these types of noises in a digital image in image processing. In this research, we have implemented different filtering techniques that have been used to remove the noises in an image.

Abstract: This paper presents a comprehensive comparative analysis of deep learning-based methods and traditional techniques for image denoising and restoration. The objective is to evaluate the performance, computational efficiency, and generalizability of these methods across various types and levels of noise in images. This report presents a comprehensive comparative analysis of image denoising and restoration techniques, focusing on traditional methods and deep learning-based approaches. The study evaluates the performance, computational efficiency, and generalizability of these methods across various types and levels of noise in images.

Based on SVM, A fast and accurate algorithm of automatic eye localization is introduced for face image's normalization in face recognition, which the localization of the eyeball's center can be accomplished in a second, and the localization accuracy can arrive at 95 percent and the average localization error is 3.16 pixels. Compare to existing eye localization methods, the method mentioned in this paper is no need to set parameters and build templates, and the localization is simple and easy to implement. At the same time, it is of definite robustness to the face rotation in image plane, scale and facial expression variation. In terms of localization results, the localization velocity of the mentioned method is faster and the localization accuracy is higher. Therefore it can be applied to real-time face detection system.