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.

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.

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

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.

A Novel Method for Fingerprint Image Segmentation Based on Adaptative Gabor Filters

2017 ◽  
Author(s):  
J.J. Brasileiro ◽  
R.C. Ramos ◽  
I.L.P. Andrezza ◽  
R.L. Parente ◽  
H.M. Gomes ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Gabor Filters ◽  
Fingerprint Image ◽  
Novel Method

ROBUST EYE TRACKING IN VIDEO SEQUENCE

2012 ◽  
Vol 21 (01) ◽  
pp. 1250012 ◽  
Author(s):  
HUCHUAN LU ◽  
SHIPENG LU ◽  
GANG YANG
Keyword(s):  
Eye Tracking ◽  
Visual Tracking ◽  
Video Sequence ◽  
Experimental Results ◽  
Video Sequences ◽  
Affine Manifold ◽  
Second Stage ◽  
Novel Method ◽  
Iris Center ◽  
Image Streams

In this paper, we present a novel method for eye tracking, in detail describing the eye contour and the visible iris center. Combining the IVT (Incremental Visual Tracking) tracker, the proposed online affine manifold model, in which the sequentially learning shape and texture are modeled in the first stage and noniterative recovering estimation in the second stage, tracks the eye contour in video sequences. After that, an adaptive black round mask is generated to match the visible iris center. Experimental results of eye tracking indicate that our tracker works well in the PC or domestic camera captured image streams with considerable head and eyeball rotation.

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