Adaptive threshold multi bit flipping for low density parity check codes

When using thresholding method to segment an image, a fixed threshold is not suitable if the background is uneven. Here, we propose a new adaptive thresholding method using variational theory. The method requires only one parameter to be selected and the adaptive threshold surface can be found automatically from the original image.

MDD brain network analysis based on EEG functional connectivity and graph theory

The world is marching towards digital communication and digital video surveillance is an indispensable part of it. Moving object detection and face detection play an important role in the video surveillance applications. It is a part of security oriented applications such as traffic systems, hospitals, banks. Background subtraction algorithm is used in order to detect the foreground. Due to the light intensity variations in dynamically changing environment, background subtraction module needs an adaptive threshold value for each input video frame. The pixel values above the threshold value are detected as foreground pixels. The range of adaptive threshold values usually varies from 10 to 85 in the proposed work. This gives a better detection capability in moving object detection wherein PSNR of 32.39 is obtained when two detected images with and without adaptive threshold are chosen as inputs to the PSNR block. Face detection is treated as an extension module of moving object detection wherein the input face image is subtracted from the test image with the help of adaptive threshold but finally the matching unit is the additional component which decides the match based on the global threshold value which is chosen as 183000 here. Here, instead of detecting the moving body, only face is considered for detection. Since real time videos need to be processed using FPGA kits, an area efficient moving object and face detection system is designed using Spartan 6 FPGA kit. The number of slice registers used has reduced from 365 to 320 in case of moving object detection, from 861 to 537 in case of face detection. The results obtained reveal that the use of adaptive threshold concept results in better detection process with PSNR of 32.39.

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.

Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local direc- tional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the esti- mated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as op- posed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images. © 2011

본 논문에서는 2차원 바코드 영상을 위한 적응적 이진화 방법을 제안한다. 적응형 이진화 방법은 조명의 영향을 최소화하여 이진화를 수행하는 기술이다. 적응적 이진화 방법은 주로 문서 영상에 맞게 발전되어 왔다. 기존 방법들은 적응적 이진화에서 사용되는 박스에 대한 크기 설정 문제를 가지고 있다. 이 문제로 기존 방법들은 이차원 바코드 영상 인식에 적용하기에 부적절하다. 문제점을 극복하기 위해 먼저 박스크기와 기존 방법들의 문제점을 분석하고, 이를 기반으로 적분영상을 사용한 새로운 적응형 이진화 방법을 소개한다. 제안한 방법의 성능 입증을 위해 기존의 방법과 속도, 성능 비교 실험을 수행하였고 실험 결과는 기존 방법보다 우수함을 입증하였다. In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

In the paper, a practical method for road detection is proposed. Many features of vehicle are introduced to detect the preceding vehicles. According to the characteristics of lane marks and vehicles, the improved Sobel operator convolution kernel is introduced in the image preprocessing to enhance the robustness of the algorithm. In order to be feasible to the change of illumination and road conditions, the adaptive double threshold method is introduced to get the binary image, the edges which are needed during detection of lane marks and front vehicles are extracted with Hough Transformation and SUSAN algorithm based on adaptive threshold in different contrast ratio. In order to apply the information to implement the lane departure warming system and forward collision warming system, both distances from host vehicle to left and right lane lines and preceding vehicles are estimated according to the inverse projection. Experiment results indicate that this algorithm is feasible for different road conditions and can realize the detection and tracking of the lane lines and vehicles with robustness and efficiency simultaneously.

<span>Segmentation of blood vessels (BVs) from retinal image is one of the important steps in developing a computer-assisted retinal diagnosis system and has been widely researched especially for implementing automatic BV segmentation methods. This paper proposes an improvement to an existing retinal BV (RBV) segmentation method by combining the trainable B-COSFIRE filter with adaptive thresholding methods. The proposed method can automatically configure its selectivity given a prototype pattern to be detected. Its segmentation performance is comparable to many published methods with the advantage of robustness against noise on retinal background. Instead of using grid search to find the optimal threshold value for a whole dataset, adaptive thresholding (AT) is used to determine the threshold for each retinal image. Two AT methods investigated in this study were ISODATA and Otsu’s method. The proposed method was validated using 40 images from two benchmark datasets for retinal BV segmentation validation, namely DRIVE and STARE. The validation results indicated that the segmentation performance of the proposed unsupervised method is comparable to the original B-COSFIRE method and other published methods, without requiring the availability of ground truth data for new dataset. The Sensitivity and Specificity values achieved for DRIVE and STARE are 0.7818, 0.9688, 0.7957 and 0.9648, respectively.</span>

Fault detection in a grid-connected photovoltaic system using adaptive thresholding method

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

Exponentially weighted moving average-based procedure with adaptive thresholding for monitoring nonlinear profiles: Monitoring of plasma etch process in semiconductor manufacturing

The process of learning is deeply intertwined with students’ emotional states, with frustration recognized as a particularly disruptive affective condition that undermines motivation, engagement, and achievement. This study investigates frustration detection in an online postgraduate course delivered through a Moodle-based learning environment. Building on prior work in affect-aware tutoring, we propose a sensor-free approach that relies on anonymized logfiles that capture student interactions. An adaptive thresholding method was applied to identify anomalous bursts of activity, such as repeated clicking, that signal frustration. Results revealed 4,763 frustration incidents, with navigation and forum activities generating the majority of signals, while submissions and quizzes were secondary contributors. Peaks in frustration closely aligned with assignment deadlines, quizzes, and examinations, though sustained navigation and forum engagement also proved highly indicative of stress. The findings highlight the importance of contextual and category-specific analysis in capturing frustration dynamics, and they underscore the need for inclusive support strategies that extend beyond high-stakes assessment moments.

Rheumatoid arthritis (RA) is a chronic autoimmune disease which affects the hand joints, wrist, feet, knee, shoulders and other regions of the body. Animal model of RA is used to evaluate the inflammatory condition and for diagnosing the disease. Although various imaging modalities like x-rays, CT and MRI are available in evaluation and diagnosing the disease, those modalities are expensive and have radiation effects. Thermal imaging plays a vital role in evaluation and monitoring the inflammation in rheumatoid arthritis Thermal imaging is a non invasive method for detecting the pathogenesis of the disease compared to other diagnostic methods. The objectives of this study were: i) to measure the changes in skin temperature of induced RA limbs when comparing to normal; ii) to find out the efficacy of an automated segmentation of induced RA limbs using the following image processing techniques: a) Robert edge detector; b) Canny edge detector; and c) Adaptive thresholding technique. 0.2ml of mixture of freunds complete adjuvant (FCA) mixed with a phosphate buffer saline at 1∶1 was injected at right side both hind- and fore- limbs of the Wistar rats (n=10), whereas normal saline was injected at left side both hind- and fore- limbs of the rats. After having induced RA in the limbs of the rats (after 30 days), thermal image of each rat was obtained under standard conditions. The measured mean temperature (°C) was found to be higher by 1.8% and 2.0% respectively at RA hind and fore limbs respectively than in the corresponding normal limbs. Adaptive thresholding method was found to be the best of method studied in segmenting the induced RA limb of the rat.

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

This paper presents a robust adaptive moving human detection and recognition method in videos. The human detection method consists of modified moving average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The moving average background model is used for background modeling and the background subtraction system is used to provide foreground image through difference image between current image and background model. The adaptive threshold method is used to simultaneously update the system to environment changes. The modified human model consists of five parts with robust features to facilitate human recognition process. For recognition purpose Support Vector Machine has been used as classifier. Experimental results show the effectiveness of proposed system.