Palm print recognition based on harmony search algorithm

Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Automatic Segmentation of Optic Disc in Eye Fundus Images: A Survey

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region

Computer Vision Algorithms For An Automated Harvester

Image classification and segmentation are the two main important parts in the 3D vision system of a harvesting robot. Regarding the first part, the vision system aids in the real time identification of contaminated areas of the farm based on the damage identified using the robot’s camera. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve the recognition accuracy and efficiency of the robot. Initially, a median filter is applied to remove the inherent noise in the colored image. SIFT features of the image are then extracted and computed forming a vector, which is then quantized into visual words. Finally, the histogram of the frequency of each element in the visual vocabulary is created and fed into an SVM classifier, which categorizes the mushrooms as either class one or class two. Our preliminary results for image classification were promising and the experiments carried out on the data set highlight fast computation time and a high rate of accuracy, reaching over 90% using this method, which can be employed in real life scenario. As pertains to image Segmentation on the other hand, the vision system aids in real time identification of mushrooms but a stiff challenge is encountered in robot vision as the irregularly spaced mushrooms of uneven sizes often occlude each other due to the nature of mushroom growth in the growing environment. We address the issue of mushroom segmentation by following a multi-step process; the images are first segmented in HSV color space to locate the area of interest and then both the image gradient information from the area of interest and Hough transform methods are used to locate the center position and perimeter of each individual mushroom in XY plane. Afterwards, the depth map information given by Microsoft Kinect is employed to estimate the Z- depth of each individual mushroom, which is then being used to measure the distance between the robot end effector and center coordinate of each individual mushroom. We tested this algorithm under various environmental conditions and our segmentation results indicate this method provides sufficient computational speed and accuracy

Page layout analysis and classification in complex scanned documents

Page layout analysis has been extensively studied since the 1980`s, particularly after computers began to be used for document storage or database units. For efficient document storage and retrieval from a database, a paper document would be transformed into its electronic version. Algorithms and methodologies are used for document image analysis in order to segment a scanned document into different regions such as text, image or line regions. To contribute a novel approach in the field of page layout analysis and classification, this algorithm is developed for both RGB space and grey-scale scanned documents without requiring any specific document types, and scanning techniques. In this thesis, a page classification algorithm is proposed which mainly applies wavelet transform, Markov random field (MRF) and Hough transform to segment text, photo and strong edge/ line regions in both color and gray-scale scanned documents. The algorithm is developed to handle both simple and complex page layout structures and contents (text only vs. book cover that includes text, lines and/or photos). The methodology consists of five modules. In the first module, called pre-processing, image enhancements techniques such as image scaling, filtering, color space conversion or gamma correction are applied in order to reduce computation time and enhance the scanned document. The techniques, used to perform the classification, are employed on the one-fourth resolution input image in the CIEL*a*b* color space. In the second module, the text detection module uses wavelet analysis to generate a text-region candidate map which is enhanced by applying a Run Length Encoding (RLE) technique for verification purposes. The third module, photo detection, initially uses block-wise segmentation which is based on basis vector projection technique. Then, MRF with maximum a-posteriori (MAP) optimization framework is utilized to generate photo map. Next, Hough transform is applied to locate lines in the fourth module. Techniques for edge detection, edge linkages, and line-segment fitting are used to detect strong-edges in the module as well. After those three classification maps are obtained, in the last module a final page layout map is generated by using K-Means. Features are extracted to classify the intersection regions and merge into one classification map with K-Means clustering. The proposed technique is tested on several hundred images and its performance is validated by utilizing Confusion Matrix (CM). It shows that the technique achieves an average of 85% classification accuracy rate in text, photo, and background regions on a variety of scanned documents like articles, magazines, business-cards, dictionaries or newsletters etc. More importantly, it performs independently from a scanning process and an input scanned document (RGB or gray-scale) with comparable classification quality

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camera

Automatic Detection and Segmentation of Lentil Breeding Plots from Images Captured by Multi-spectral UAV-Mounted Camer