Research on Color Normalization of Traffic Sign Image Based on Surface Segmentation and Improved K-means Clustering Algorithm

2020 ◽  
Author(s):  
Yang Liu ◽  
Yang Liu ◽  
Wei Zhong
Keyword(s):  
Clustering Algorithm ◽  
Traffic Sign ◽  
Surface Segmentation

Research on Traffic Sign Detection Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3304-3308
Author(s):  
Mei Hua Xu ◽  
Yi Da Liu ◽  
Chen Jun Xia
Keyword(s):  
Clustering Algorithm ◽  
Color Image ◽  
Ccd Camera ◽  
Detection Algorithm ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Image Separation ◽  
Series Of Experiments ◽  
Traffic Sign Detection

As an important part of Advanced Driver Assistance System (ADAS), the traffic sign detection has been paid more and more attention. This paper studied and implemented a valid algorithm of traffic sign detection. Using K-means clustering algorithm to complete the image separation and extraction of prohibition signs from the RGB color image, and then matching them with templates to realize the detection of traffic signs by SIFT algorithm. Series of experiments for traffic sign detection have been carried out to prove the validity and correctness of the algorithm on the basis of the road images in front of the vehicle collected by CCD camera.

scholarly journals Automatic road sign detection and recognition based on neural network

2021 ◽  
Author(s):  
Redouan Lahmyed ◽  
Mohamed El Ansari ◽  
Zakaria Kerkaou
Keyword(s):  
Clustering Algorithm ◽  
Color Space ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Traffic Sign ◽  
Initial Image ◽  
Dbscan Clustering ◽  
Road Sign ◽  
Sign Detection ◽  
Detection And Recognition

Abstract Road sign detection and recognition is an integral part of intelligent transportation sys-tems (ITS). It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using Artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns (GLPB) feature to the RGB color space and the local self-similarity (LSS) feature. ANN, Adaboost and support vector machine (SVM) have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databasets, well known in the traffic sign community (GTSRB, GTSDB and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.

scholarly journals Robust surface segmentation and edge feature lines extraction from fractured fragments of relics

2015 ◽  
Vol 2 (2) ◽  
pp. 79-87 ◽  
Author(s):  
Jiangyong Xu ◽  
Mingquan Zhou ◽  
Zhongke Wu ◽  
Wuyang Shui ◽  
Sajid Ali
Keyword(s):  
Surface Roughness ◽  
Clustering Algorithm ◽  
Integral Invariant ◽  
Computer Assisted ◽  
Normal Vector ◽  
Edge Extraction ◽  
Feature Lines ◽  
Surface Segmentation ◽  
Novel Method ◽  
Edge Feature

Abstract Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.

scholarly journals Application of Cluster Analysis to Define Level of Service Criteria of U-turns at Median Openings

2021 ◽  
Vol 81 ◽  
pp. 1-17
Author(s):  
Smruti Sourava Mohapatra
Keyword(s):  
Clustering Algorithm ◽  
Microscopic Analysis ◽  
Optimal Number ◽  
Level Of Service ◽  
Clustering Methods ◽  
Traffic Sign ◽  
Service Delay ◽  
Validation Parameters ◽  
Merging Process ◽  
Median Opening

Defining Level of Service (LOS) criteria of U-turns is important for proper planning, design of transportation projects and also allocating resources. The present study attempts to establish a framework to define LOS criteria of U-turns keeping in mind the peculiar behavior of drivers and heterogeneity in urban Indian context. The U-turns at uncontrolled (no traffic sign, no signal, no traffic personnel) median openings are very risky. Upon arrival at the median opening, the U-turning vehicle looks for a suitable gap in the approaching traffic stream before initiating the merging process. While waiting for a suitable gap the U-turning vehicle experiences service delay. This service delay has been studied to quantify the delay ranges for different LOS categories. In this study, service delay data were collected from 7 different sections and microscopic analysis procedure was adopted to extract data from the recorded video. Subsequently, clustering technique has been utilized to defining delay ranges of different level of service categories. Four clustering methods, namely; K-mean, K-medoid, Affinity Propagation (AP), and Fuzzy C-means (FCM) are used. Four validation parameters are applied to determine most suitable clustering algorithm for the study and to determine the optimal number of cluster. AP was found to be the most suitable clustering method and 6 was found to be the optimal number and accordingly the collected delay data were clustered into 6 categories using AP. The delay range is found to be less than 4 s for LOS A is greater than 35 s for LOS F.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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