Noise removal and enhancement of binary images using morphological operations

Jamil, Nursuriati; Sembok, Tengku Mohd Tengku; Bakar, Zainab Abu

doi:10.1109/itsim.2008.4631954

Cited by 70 publications

(33 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filter mask size is 3 × 3 pixels. This is performed by converting the black pixels with the majority white neighbourhood to logical one [13]. The reason for not using other types of morphological operations is to avoid erosions or degradation in the fingers borders.…”

Section: Robust Finger Extraction Methodsmentioning

confidence: 99%

Robust feature extraction and salvage schemes for finger texture based biometrics

Al-Nima¹,

Dlay

Al-Sumaidaee

et al. 2016

IET biom.

View full text Add to dashboard Cite

In this paper, an efficient human authentication method is proposed which utilizes Finger Texture (FT) patterns. This method consists of two essential contributions: a robust and automatic finger extraction method to isolate the fingers from the hand images; and a new feature extraction method based on an Enhanced Local Line Binary Pattern (ELLBP). To overcome poorly imaged regions of the FTs a method is suggested to salvage missing feature elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN) used to perform classification. Three databases have been applied in this paper: PolyU3D2D, IIT Delhi and spectral 460 from Multi-spectral CASIA images. Experimental studies show that the best result was achieved by using ELLBP feature extraction. Furthermore, the salvaging approach proved effective in increasing the verification rate.

show abstract

Section: Robust Finger Extraction Methodsmentioning

confidence: 99%

Robust feature extraction and salvage schemes for finger texture based biometrics

Al-Nima¹,

Dlay

Al-Sumaidaee

et al. 2016

IET biom.

View full text Add to dashboard Cite

show abstract

“…A reading function of the OpenCV library loads the photo image [15], and a detection function returns a region list where a rectangular area for license plate recognition finds alphanumeric characters whereas a preprocessing function performs color conversion to HSV in the photo image by extracting values [16] from the OpenCV library [15]. A segmentation function links to the photo image channels, setting addition and subtraction operations to maximize contrasts for removing gray-scale image noises [17], converted previously into erosion and dilation. Moreover, the first filtering removes the Gaussian noise, making it possible to change images to gray-scale as shown in Figure 4(a) for comparing each pixel of the threshold and getting its binary form as shown in Figure 4(b).…”

Section: System Designmentioning

confidence: 99%

Cyber-physical system based on image recognition to improve traffic flow: A case study

Padilla

Ponguillo

Abad

et al. 2020

IJECE

View full text Add to dashboard Cite

Vehicular trafﬁc in metropolitan areas turns congested along either paths or periods. As a case study, we have considered a mass transport system with a bus ﬂeet that rides over exclusive lanes across streets and avenues in an urban area that does not allow the circulation of lightweight vehicles, cargo, and motorcycles. This trafﬁc ﬂow becomes congested due to the absence of restriction policies based on criteria. Moreover, the exclusive lanes are at ground level, decreasing lanes for other vehicles. The main objective of this proposal consists of controlling the access to the exclusive lanes by a cyber-physical system following authorization conditions, verifying the permission status of a vehicle by the accurate recognition of license plates to reduce trafﬁc congestion. Therefore, in the case of invading an exclusive lane without permission, the vehicle owner gets a notiﬁcation of the ﬁne with the respective evidence.

show abstract

“…Objects: noise and some unwanted pixels are removed from image using morphological opening operation and this will lead to reduce the processing time in the next stages [11].…”

Section: Removing Smallmentioning

confidence: 99%

Speed Limit Road Sign Detection and Recognition System

Ali¹,

Ismail²

2015

IJCA

View full text Add to dashboard Cite

Traffic signs in general and speed limit signs in particular are considered one of the most important means of traffic safety, and the aim of the current research is to design a system that detects and recognizes speed limit sign with high accuracy and high processing speed. At the beginning, red color objects are detected from the image and after finding the red color signs the circle is determined using Hough's transform then from inside the circle, the numeric part from the circle image is extracted. Digital circle images are segmented to extract the number alone, and then these numbers are recognized by a trained neural network. Neural network achieved a success rate in recognition reached to 98.9%. Parallel programming concept is used to reduce the execution time using OpenMP and OpenCl programming. The study showed that the total execution speed according to the designed scheme to run the speed limit sign detection and recognition by using a mix of central processing unit with multi cores and graphics processing unit is 65 frames/sec for complete images and 90 frames/sec when cropping the effective part from the total size of the image. Recognition system is capable of recognizing the sign even if the vehicle speed exceeds 120 km/h.

show abstract

Noise removal and enhancement of binary images using morphological operations

Cited by 70 publications

References 10 publications

Robust feature extraction and salvage schemes for finger texture based biometrics

Robust feature extraction and salvage schemes for finger texture based biometrics

Cyber-physical system based on image recognition to improve traffic flow: A case study

Speed Limit Road Sign Detection and Recognition System

Contact Info

Product

Resources

About