License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi-views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE-Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy-based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2017.0368

License number plate recognition system using entropy-based features selection approach with SVM

Automatic license plate detection in hazardous condition

Multinational vehicle license plate detection in complex backgrounds

Automatic license plate recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localisation is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The present algorithm differs significantly from those utilised in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The present algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek number plates and was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2017.0224

Improved license plate localisation algorithm based on morphological operations

Efficient multi-scale non-sub-sampled shearlet fusion system based on modified central force optimization and contrast enhancement

This paper proposes a faster mechanism for license plate detection. Usually license plate has more vertical edges compared to horizontal edge. By focusing on this concept an algorithm called VEDA is used here. First convert the color image into gray image. Then for binarizing the image a technique called adaptive thresholding (AT) is used. The noise in the image is removed by using ULEA algorithm. Then the proposed VEDA is used to extract the vertical edges. Then HDD algorithm is used for highlighting the license plate area. Compared to other edge detection mechanisms such as SOBEL and CANNY, VEDA has lesser complexity. Also the computation time is also less for VEDA when compared against SOBEL.

Automatic license plate detection using vertical edge detection method

Unlike text, images represent an effective communication media for humans. Today we use photographs for variety of applications. Traditionally, everyone have confidence in the content of the image. Sometimes, a picture printed in a newspaper is accepted as a certification of the truthfulness of the news. As the technology develops, we can't trust images. Image manipulation can be done easily by using photo editing tools. As a result, we have to prove the originality of images. One of the most common forms of image modification is the image composition. Composite image is created from two or more image sources. All the different images are taken from the different devices and at different world view conditions. Digital forensics field has emerged to help restore some trust to digital images. When creating a composite image, it becomes hard to match the color of one object with reference to the other. This paper describes a technique for detecting digital image forgery using illuminant color.

Image forgery detection using SVM classifier

Medical imaging is the process of creating images of the human body for the purpose of diagnosis and treatment. In medical imaging various modalities provide different features of body because they use different principles of imaging. Since no single image modality provides a complete information in all cases, fusion will be very useful. The objective of image fusion is to improve the image content, by combining information from multiple images. In this paper three transform based fusion rules, The Wavelet transform(WT), Contourlet transform(CT) and Nonsubsampled contourlet transform(NSCT) are compared. Experimental results show that the image fusion method based on NSCT performs better than the other two methods.

Comparative analysis of transform based image fusion techniques for medical applications

This paper is describing about various methods for the person identification It verifies the human retina along with the sclera features extracted using the graph matching algorithm The paper also explains various other methods for person identification using retina and also irises Thus on the whole it is a biometric system using the graph matching algorithm

Biometric system using graph matching

Advancements in EEG signal processing yields tremendous research in the field of BCI. Online based BCI fails in selecting independent components from different brain regions. Unknown order of the independent components and the random weight matrix used in repeated ICA trainings may leads to a different ICA result. This paper highlights a new idea of automatic ICi selection by taking an average of particular brain regions which resolves the problem of online BCI. The proposed method has been tested in EEG datasets such as .SET, .SMA which succeeds in selecting reference ICi.

C Arunvinodh

Papers

Automatic license plate detection using vertical edge detection method

Image forgery detection using SVM classifier

Comparative analysis of transform based image fusion techniques for medical applications

Biometric system using graph matching

A novel method for automatic ICi selection in EEG signals