Dynamic Thresholding Algorithm Research Articles

Analysis of the change path of news dissemination under the trend of short-video convergence - Based on multi-priority dynamic thresholding algorithm

Abstract This paper proposes a multi-priority dynamic threshold algorithm for short video news transmission. The average queue threshold is dynamically adjusted according to the packet priority and unused cache space, and the data frames of short video news nodes are processed into multiple pulse data segments after the encoding process. The success rate and confidentiality of data transmission are improved by scrambling each segment of pulse data. Finally, after updating the thresholds at all levels, the overall thresholds of short video news are kept full to meet the performance requirements of the highest priority queue of short video news. The results show that 95.03% of the short video news with a high dissemination degree has a file size of less than 5 MB, and its video length is most popular among users in the range of 8~15 seconds. Compared to the traditional news dissemination mode, short video news is not limited by time and space, which greatly improves the efficiency of information dissemination.

Dynamic thresholding for video anomaly detection

Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. changes in illumination conditions. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data-driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. On both synthetic and real-world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.

Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection.

This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training images. The algorithm enables robust detection in highly-variable lighting conditions. The image is dynamically split into variably-sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected to accommodate three different illumination levels for three different dimensions in four color spaces: RGB, HSI, LAB, and NDI. Each color space uses a different method to represent a pixel in an image: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), LAB (Lightness, Green to Red and Blue to Yellow) and NDI (Normalized Difference Index, which represents the normal difference between the RGB color dimensions). The thresholds were selected by quantifying the required relation between the true positive rate and false positive rate. A tuning process was developed to determine the best fit values of the algorithm parameters to enable easy adaption to different kinds of fruits (shapes, colors) and environments (illumination conditions). Extensive analyses were conducted on three different databases acquired in natural growing conditions: red apples (nine images with 113 apples), green grape clusters (129 images with 1078 grape clusters), and yellow peppers (30 images with 73 peppers). These databases are provided as part of this paper for future developments. The algorithm was evaluated using cross-validation with 70% images for training and 30% images for testing. The algorithm successfully detected apples and peppers in variable lighting conditions resulting with an F-score of 93.17% and 99.31% respectively. Results show the importance of the tuning process for the generalization of the algorithm to different kinds of fruits and environments. In addition, this research revealed the importance of evaluating different color spaces since for each kind of fruit, a different color space might be superior over the others. The LAB color space is most robust to noise. The algorithm is robust to changes in the threshold learned by the training process and to noise effects in images.

A New Imaging System for Real-Time Process Control

In this paper, a new tomography technique called electrical charge tomography for two-phase flow imaging is presented. The probe consists of few pair of electrodes which are electrically energized to generate electrical charges within the fluid under test. The intensity of these charges depends on the chemical and physical properties of the fluid, as well as to its molecular distribution. Another group of electrodes surrounding the cross section of the fluid under test are used to capture the induced electrical charges. These are then converted into an electrical signal using a high sensitive charge amplifier. A postprocessing unit which consists of an analog to digital converter, followed by an field programmable gate array (FPGA) module is then used for high level signal processing (i.e., a dedicated dynamic thresholding algorithm) and image reconstruction. Experimental results demonstrate the capability of the system to accurately generate 2-D cross-sectional images, where the error is lower by up to 14% when using another electrical capacitance (ECT) tomography probe. The other advantage of this technique over ECT is the reduced data acquisition time, since in ECT a minimum time is required for the charge and discharge of the capacitance in order to achieve acceptable accuracy. This makes the probe another attractive concept for future tomography systems targeting real-time applications.

Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

Snow cover in mountain areas is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/α , Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P vol from Pauli, λ3 from H/A/α , and Y vol from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.

Comparison Study and Result Analysis of Improved Back-Propagation Algorithms in Bangla Speech Recognition

This research is concerned with the study of different improved and faster back-propagation (BP) algorithms of neural networks and the analysis of recognition result in continuous Bangla speech. For speech recognition, a comparison study on neural networks and speech recognition result analysis with different improved and faster BP algorithms (such as, BP with momentum, variable learning rate BP, resilient BP, conjugate gradient BP and Levenberg-Marquardt BP algorithms) have been done. In this research, the MATLAB Neural Network Toolbox 7.12.0 is used to create, train and simulate the feedforward neural network with the BP learning algorithm. The convergence obtained from standard BP algorithm is very slow; that's why, this research proposes different improved and faster BP algorithms to solve the speech recognition problems. The developed system has been justified by several networks trained with different Bangla speech words. To test the performance of the system, 20 samples of 50 Bangla speech words have been used; from which 10 samples of 50 words are used as training pattern and another 10 samples of 50 words are used as testing pattern in the network. The binary features of speech words have been generated using dynamic thresholding algorithm. The recognition system has been achieved recognition rate of 83% using resilient BP algorithm, 90% using conjugate gradient BP algorithm and 90% using Levenberg-Marquardt BP algorithm, respectively, for recognising 50 speech words.

Blocking Black Area Method for Speech Segmentation

Speech segmentation is an important sub problem of automatic speech recognition. This research is concerned with the development of a continuous speech segmentation system using Bangla Language. This paper presents a dynamic thresholding algorithm to segment the continuous Bngla speech sentences into words/sub-words. The research uses Otsu’s method for dynamic thresholding and introduces a new approach, named blocking black area method to identify the voiced regions of the continuous speech in speech segmentation. The developed system has been justified with continuously spoken several Bangla sentences. To test the performance of the system, 100 Bangla sentences have been recorded from 5 (five) male speakers of different ages and 656 words have been presented in the 100 Bangla sentences. So, the speech database contains 500 Bangla sentences with 3280 words. All the algorithms and methods used in this research are implemented in MATLAB and the proposed system has been achieved the average segmentation accuracy of 90.58%.

Dynamic multiple thresholding breast boundary detection algorithm for mammograms

Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms. A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM). In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p < 0.0001). The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.

Semiautomated DNA probe mapping using digital imaging microscopy: I. System development.

Algorithms have been developed to help automate the mapping of DNA sequences along metaphase chromosomes using fluorescence in situ hybridization (FISH). Custom algorithms computationally define chromosome boundaries and compute chromosomal medial axes. A dynamic regional thresholding (DRT) algorithm is described that allows reliable detection of hybridization domains, even when they differ substantially in size and intensity. Chromosomal locations are calculated by determining the fractional location of each hybridization probe along the medial axis of a metaphase chromosome relative to the short arm (FLpter). These algorithms were tested on simulated data and by analysis of the location of probes that had been previously mapped by other techniques. These algorithms allow probes to be mapped rapidly along human chromosomes with a precision of 2-3 Mb.