Iris Identification Based on the Fusion of Multiple Methods

Iris recognition occupies an important rank among the biometric types of approaches as a result of its accuracy and efficiency. The aim of this paper is to suggest a developed system for iris identification based on the fusion of scale invariant feature transforms (SIFT) along with local binary patterns of features extraction. Several steps have been applied. Firstly, any image type was converted to grayscale. Secondly, localization of the iris was achieved using circular Hough transform. Thirdly, the normalization to convert the polar value to Cartesian using Daugman’s rubber sheet models, followed by histogram equalization to enhance the iris region. Finally, the features were extracted by utilizing the scale invariant feature transformation and local binary pattern. Some sigma and threshold values were used for feature extraction, which achieved the highest rate of recognition. The programming was implemented by using MATLAB 2013. The matching was performed by applying the city block distance. The iris recognition system was built with the use of iris images for 30 individuals in the CASIA v4. 0 database. Every individual has 20 captures for left and right, with a total of 600 pictures. The main findings showed that the values of recognition rates in the proposed system are 98.67% for left eyes and 96.66% for right eyes, among thirty subjects.

Cluster analysis of clinical data on patients with chronic endometritis

Chronic endometritis (CE) is one of the causes of impaired fertility, disorders of menstrual function, formation of pelvic pain, and for peri- and postmenopausal patients it serves as the background for the development of a number of proliferative diseases. Objective. Application of the method of cluster analysis of clinical data on patients with chronic endometritis. Patients and methods. The clinical data on 257 patients were analyzed according to a specially developed program. For feature clustering, distance measurement was performed by the Manhattan distance method (city-block distance) using Ward's algorithm. Results. The use of cluster analysis made it possible to group the clinical, anamnestic and laboratory features of CE and their distribution into homogeneous groups or clusters. Seven clusters were obtained. Conclusion. The widespread use of statistical analysis methods, in particular the use of clustering method, made it possible to demonstrate clinically significant combinations of disease features in a group of patients, the analysis of each cluster demonstrated a number of cause-effect relationships and allowed to evaluate the clinical and anamnestic data on CE patients from a different perspective. Key words: clustering, feature combinations, treatment, chronic endometritis

Understanding and Using Common Similarity Measures for Text Analysis

This lesson introduces three common measures for determining how similar texts are to one another: city block distance, Euclidean distance, and cosine distance. You will learn the general principles behind similarity, the different advantages of these measures, and how to calculate each of them using the SciPy Python library.

Numerosity and cumulative surface area are perceived holistically as integral dimensions

Human and non-human animals have the remarkable capacity to rapidly estimate the quantity of objects in the environment. The dominant view of this ability posits an abstract numerosity code, uncontaminated by non-numerical visual information. The present study provides novel evidence in contradiction to this view by demonstrating that number and cumulative surface area are perceived holistically, classically known as integral dimensions. Whether assessed explicitly (Experiment 1) or implicitly (Experiment 2), perceived similarity for dot arrays that varied parametrically in number and cumulative area was best modeled by Euclidean, as opposed to city-block, distance within the stimulus space, comparable to other integral dimensions (brightness/saturation and radial frequency components), but different from separable dimensions (shape/color and brightness/size). Moreover, Euclidean distance remained the best-performing model, even when compared to models that controlled for other magnitude properties (e.g., density) or image similarity. These findings suggest that numerosity perception entails the obligatory processing of non-numerical magnitude.

Use of KNN Classifier for Emotion Recognition Based on Distance Measures

Human-computer interaction (HCI), in recent times, is gaining a lot of significance. The systems based on HCI have been designed for recognizing different facial expressions. The application areas for face recognition include robotics, safety, and surveillance system. The emotions so captured aid in predicting future actions in addition to providing valuable information. Fear, neutral, sad, surprise, happy are the categories of primary emotions. From the database of still images, certain features can be obtained using Gabor Filter (GF) and Histogram of Oriented Gradient (HOG). These two techniques are being used while extracting features for getting the expressions from the face. This paper focuses on the customized classification of GF and HOG using the KNN classifier.GF provides texture features whereas HOG finds applications for images exhibiting differing lighting conditions. Simplicity and linearity of KNN classifier appeals for its use in the present application. The paper also elaborates various distances used in KNN classifiers like city-block, Euclidean and correlation distance. This paper uses Matlab implementation of GF, HOG and KNN for extracting the required features and classification, respectively. Results exhibit that the accuracy of city- block distance is more .

Performance Analysis of Distance Metric for Content Based Image Retrieval

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

Heuristic Optimization of Portfolio Considering Sharpe's Single Index Model

Selection of weights of the selected securities in the portfolio is a cumbersome job for any investor. The famous nonlinear Sharpe's single index model has been simplified with a linear solution and the risk-taking propensity of the investors have been taken into consideration in the simplified formulation. The coefficient of optimism is included to observe the effect of risk-taking propensity in the portfolio selection. After the empirical analysis it is found that heuristically an investor can reach near to the optimum solution. For empirical analysis 126 months data have been considered of NSE Bank Index. To reduce the volatility of the data the whole period again has been divided into two parts each of 63 months duration, and separately the data pertaining to the three periods have been considered for calculation. The city block distance is used to calculate the nearness between the optimum solutions and the heuristic solutions.