The double threshold method based on Kinect depth map

2015 ◽  
pp. 227-230
Keyword(s):  
Depth Map ◽  
Threshold Method ◽  
Double Threshold

scholarly journals Getting rid of rain and stars: mitigating inhibition effects in ddPCR assays, the case of the invasive crayfish Pacifastacus leniusculus in the streams of Luxembourg

2021 ◽  
Vol 4 ◽  
Author(s):  
David Porco ◽  
Sylvie Hermant ◽  
Chanistya Purnomo ◽  
Mario Horn ◽  
Guy Marson ◽  
...  
Keyword(s):  
Partial Resistance ◽  
Pacifastacus Leniusculus ◽  
Pcr Inhibition ◽  
Threshold Method ◽  
Low Concentration ◽  
Emulsion Pcr ◽  
Invasive Crayfish ◽  
High Fluorescence ◽  
Double Threshold

ddPCR is getting more and more popular in the field of eDNA-based aquatic monitoring. Even if emulsion PCR used in ddPCR confers a partial resistance to inhibition due to the high number of reactions for the same sample (between 10K and 20K), it is not impervious to it. Inhibition impacts the fluorescence amplitude of positive droplets, affecting both their dispersion and their position relatively to the negative droplets cloud. This fluctuation could jeopardize the use of a shared threshold among several samples and thus the objective assignation of the positive droplets. This is even more critical for low concentration samples such as eDNA samples: the positive droplets are scarce and it is thus crucial to objectively discriminate if they can be counted as positive by establishing an appropriate threshold. Another issue is the artifactual generation of high fluorescence droplets that could be counted as positive with a single threshold solution. Here we propose a double threshold method to take both high fluorescence droplets and PCR inhibition impact into account allowing for an objective sorting of the positive and negative droplets in ddPCR assays.

Contrast Enhancement Using Optimum Threshold Selection

2020 ◽  
Vol 8 (3) ◽  
pp. 96-118
Author(s):  
Geeta Rani ◽  
Monika Agarwal
Keyword(s):  
Contrast Enhancement ◽  
Maximum Entropy ◽  
Dynamic Range ◽  
Threshold Value ◽  
Histogram Equalization ◽  
Threshold Method ◽  
Essential Information ◽  
Low Contrast ◽  
Brightness Preservation ◽  
Double Threshold

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

Double threshold method for mastitis diagnosis based on NIR spectra of raw milk and chemometrics

2009 ◽  
Vol 104 (2) ◽  
pp. 243-249 ◽  
Author(s):  
Hesti Meilina ◽  
Shinichiro Kuroki ◽  
B.M. Jinendra ◽  
Kentarou Ikuta ◽  
Roumiana Tsenkova
Keyword(s):  
Raw Milk ◽  
Threshold Method ◽  
Double Threshold

An improved double-threshold method based on gradient histogram

2004 ◽  
Vol 9 (4) ◽  
pp. 473-476 ◽  
Author(s):  
Yang Shen ◽  
Chen Shu-zhen ◽  
Zhang Bing
Keyword(s):  
Threshold Method ◽  
Double Threshold

scholarly journals Improving the sorting efficiency of maize haploid kernels using an NMR-based method with oil content double thresholds

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yanzhi Qu ◽  
Zonghua Liu ◽  
Yazhou Zhang ◽  
Jiwei Yang ◽  
Haochuan Li
Keyword(s):  
Oil Content ◽  
High Efficiency ◽  
Lower Boundary ◽  
Kernel Weight ◽  
Breeding Cycle ◽  
Threshold Method ◽  
Discrimination Rate ◽  
Sorting Efficiency ◽  
Double Threshold

Abstract Background Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the automatic haploid sorting based on nuclear magnetic resonance (NMR) using a single threshold method has been realized. However, embryo-aborted (EmA) kernels are usually produced during in vivo haploid induction, and both haploids and EmA kernels have lower oil content and are separated together using a single threshold method based on NMR. This leads to a higher haploid false discrimination rate (FDR) and requires secondary manual sorting to select the haploid kernels from the mixtures, which increases the sorting cost and decreases the haploid sorting efficiency. In order to improve the correct discrimination rate (CDR) in sorting haploids, a method to distinguish EmA kernels is required. Results Single kernel weight and oil content were measured for the diploid, haploid, and EmA kernels derived from three maize hybrids and nine inbred lines by in vivo induction. The results showed that the distribution of oil content showed defined boundaries between the three types of kernels, while the single kernel weight didn't. According to the distribution of oil content in the three types of kernels, a double-threshold method was proposed to distinguish the embryo-aborted kernels, haploid and diploid kernels based on NMR and their oil content. The double thresholds were set based on the minimum oil content of diploid kernels and the maximum content of EmA kernels as the upper and lower boundary values, respectively. The CDR of EmA kernels in different maize materials was > 97.8%, and the average FDR was reduced by 27.9 percent. Conclusions The oil content is an appropriate indicator to discriminate diploid, haploid and EmA kernels. An oil content double-threshold method based on NMR was first developed in this study to identify the three types of kernels. This methodology could reduce the FDR of haploids and improve the sorting efficiency of automated sorting system. Thus, this technique represents a potentially efficient method for haploid sorting and provides a reference for the process of automated sorting of haploid kernels with high efficiency using NMR.

scholarly journals Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shanshan Yu ◽  
Ju Liu ◽  
Jing Wang ◽  
Inam Ullah
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Threshold Energy ◽  
Average Energy ◽  
Energy Detection ◽  
Detection Performance ◽  
Weighting Coefficient ◽  
Threshold Method ◽  
Double Threshold

Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.

A Single-Frame Based Approach to Measure Traffic Queue Length

2012 ◽  
Vol 468-471 ◽  
pp. 213-216
Author(s):  
Qing Wu Li ◽  
Hao Li ◽  
Guan Ying Huo
Keyword(s):  
Image Segmentation ◽  
Queue Length ◽  
Video Sequences ◽  
Threshold Method ◽  
Length Measure ◽  
Single Frame ◽  
Novel Method ◽  
Double Threshold

A novel method for measurement of vehicle queue length is presented in this paper. Previous methods proposed by researchers for queue detection are based on video sequences. The method proposed here measures the queue length in signal-frame image acquired by a stationary camera, which avoids the effect of uncontrollable camera motions. Besides, considering the varying illumination, double-threshold method is used for image segmentation and flashlight is applied to the system for realizing the round-the-clock traffic status detect. The experiment results of queue length measure from images of real road scenes show the accuracy and reliability of the proposed method.

Construction Research on Multi-Threshold Segmentation Based on Improved Otsu Threshold Method

2014 ◽  
Vol 1046 ◽  
pp. 425-428 ◽  
Author(s):  
Yan Qing Wang ◽  
Lu Lu Zhuang ◽  
Chao Xia Shi
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Intelligent Vehicle ◽  
Road Detection ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
The Real ◽  
Time Motion ◽  
Double Threshold

The aim of unstructured road detection is the recognition of road region and the tracking of road boundary. An improved Otsu threshold method available in double-threshold segmentation was proposed in this paper to resolve the multi-peaks problem in both complicated background and target. This method remedies the mis-segmentation of Otsu threshold method caused by huge diversity of between-class variances and refines the segmentation precision by matching the proposed Otsu edges with the weighted Canny edges. According to the irregular characteristics of the unstructured road and the requirement of the real-time motion planning for intelligent vehicle, improved Otsu threshold method was proposed. The experiment results in different road environments demonstrated the validity and real-time of the proposed method.

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