IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

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, localization 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 proposed algorithm differs significantly from those utilized 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 proposed 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 license plates which is a popular benchmark used in previous works. The proposed algorithm 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

Color Hit-or-Miss Transform (CMOMP)

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

An investigation of a pattern recognition system to analyse and classify dried fruit

Includes bibliographical references.Both the declining cost and increasing capabilities of specialised computer hardware for image processing have enabled computer vision systems to become a viable alternative to human visual inspection in industrial applications. In this thesis a vision system that will analyse and classify dried fruit is investigated. In human visual inspection of dried fruit, the colour of the fruit is often the main determinant of its grade; in specific cases the presence of blemishes and geometrical fault are also incorporated in order to determine the fruit grade. A colour model that would successfully represent the colour variations within dried fruit grades, was investigated. The selected colour feature space formed the basis of a classification system which automatically allocated a sample unit of dried fruit to one specific grade. Various classification methods were investigated, and that which suited the system data and parameters was selected and evaluated using test sets of three types of dried fruit. In order to successfully grade dried fruit, a number of additional problems had to be catered for: the red/brown coloured central core area of dried peaches had to be removed from the colour analysis, and Black blemishes upon dried pears had to be isolated and sized in order to supplement the colour classifier in the final classification of the pear. The core area of a dried peach was isolated using the Morphological Top-Hat transform, and Black blemishes upon pears were isolated using colour histogram thresholding techniques. The test results indicated that although colour classification was the major determinant in the grading of dried fruit, other characteristics of the fruit had to be incorporated to achieve successful final classification results; these characteristics may be different for different types of dried fruit, but in the case of dried apricots, dried peaches and dried pears, they include the: peach core area removal, fruit geometry validation, and dried pear blemish isolation and sizing

Human Face Detection By YCbCrHs Technique

Abstract: Human face detection plays important roles in many applications such as video surveillance and face image database management. In our Paper, we have studied worked on face detection By YCbCrHs Techniques and developed algorithms for them. In face detection, we have developed an algorithm that can detect human faces from an image. We have taken skin color as a tool for detection. This technique works well for Indian faces which have a specific complexion varying under certain range. We have taken real life examples and simulated the algorithms in MATLAB successfully. Our aim, which we believe we have reached, was to develop a method of face recognition that is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and. techniques. The examples provided in this Paper are real-time and taken from our own surroundings. While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. In This Paper presents a novel skin colour model, RGB-YCbCrHs for the detection of human faces

Bioplastics from sweet potatoes

As oil runs out and the use of fossil fuels becomes expensive, the need for replacement source of raw material for the manufacture of plastics becomes essential. Bioplastics are essential as an alternative of commercial plastics from fossil fuels. Bioplastics are eco-friendly and biodegradable hence provide an effective way to replace the commercial plastics. Producing bioplastics from three types of sweet potatoes which are white, orange and purple in colour is the main goal of the study. Extraction techniques were applied to obtain starch from the sweet potatoes. The starch was then mixed with chemical solution such as glycerine, vinegar and distilled water to form bioplastic. The bioplastics were tested for biodegradability, stretch and water adsorption tests. The results show that the bioplastics from white potatoes degrade faster than the other types of sweet potatoes while commercial plastics cannot be degraded at all. White sweet potatoes have less absorption of water which it is the best criteria for bioplastics. Stretchable of white sweet potatoes is more compared to the other types of sweet potatoes. Bioplastics from white sweet potatoes have a good potential as a replacement of commercial plastics

Automatic detection of specular reflectance in colour images using the MS diagram

In this paper we present a new method for the identification of specular reflectance in colour images. We have developed a bi-dimensional histogram which allows the exploitation of the relations between the signals of intensity and saturation of a colour image. Once the diagram has been constructed, it is possible to verify that the pixels of the specular reflectance are located in a well-defined region. The brightness is automatically identified by means of the extraction of pixels present in this region of the diagram, independently of their hue values. The effectiveness of the method in a variety of real chromatic images has been proven

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention.This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis. © 2013 Elsevier Ltd