Image segmentation and feature extraction for recognizing strokes in tennis game videos

This paper addresses the problem of recognizing human actions from video. Particularly, the case of recognizing events in tennis game videos is analyzed. Driven by our domain knowledge, a robust player segmentation algorithm is developed real video data. Further, we introduce a number of novel features to be extracted for our particular application. Different feature combinations are investigated in order to find the optimal one. Finally, recognition results for different classes of tennis strokes using automatic learning capability of Hidden Markov Models (HMMs) are presented. The experimental results demonstrate that our method is close to realizing statistics of tennis games automatically using ordinary TV broadcast videos

Biometric iris image segmentation and feature extraction for iris recognition

PhD ThesisThe continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State University and Education Task Fund, Nigeri

Human Vision Inspired Technique Applied to Detect Suspicious Masses in Mammograms

Several competitive techniques have been applied for efficient image segmentation and automatic feature extraction through the literatures. There are a lot of open problems and controversial ambiguities regarding to the mechanism which applied by human eye for image segmentation and feature extraction. Here we have first extracted the human vision technique applied for image segmentation and we have implemented this technique for automatic image segmentation and feature extraction. The features have been categorized into the internal and external modalities. We have introduced the negative curvature minima (NCM) points as a dominant external feature and the textures detected using pulse coupled neural networks (PCNNs) and LAWs methods as the dominant internal feature used by human vision to segment and extracts the features of an image. These features have been used to detect suspicious masses in mammogram images using the proposed human eye inspired technique. The results justify the efficiency of the proposed method

Segmentation and feature extraction of fluid-filled uterine fibroid–A knowledge-based approach

Uterine fibroids are the most common pelvic tumours in females. Ultrasound images of fibroids require image segmentation and feature extraction for analysis. This paper proposes a new method for segmenting the fluid-filled fibroid found in the uterus. It presents a fully automatic approach in which there is no need for human intervention. The method used in this paper employs a number of knowledge-based rules to locate the object and also utilises the concepts in mathematical morphology. It also extracts the necessary features of the fibroid which can be used to prepare the radiological report. The performance of this method is evaluated using area-based metrics

Classification of Macronutrient Deficiencies in Maize Plant Using Machine Learning

Detection of nutritional deficiencies in plants is vital for improving crop productivity. Timely identification of nutrient deficiency through visual symptoms in the plants can help farmers take quick corrective action by appropriate nutrient management strategies. The application of computer vision and machine learning techniques offers new prospects in non-destructive field-based analysis for nutrient deficiency. Color and shape are important parameters in feature extraction. In this work, two different techniques are used for image segmentation and feature extraction to generate two different feature sets from the same image sets. These are then used for classification using different machine learning techniques. The experimental results are analyzed and compared in terms of classification accuracy to find the best algorithm for the two feature sets

Identifying persons from iris images using neural networks for image segmentation and feature extraction

Задача идентификации личности играет важную роль в обеспечении безопасности: информационной, общественной и др. В последнее время наиболее актуальными и перспективными являются биометрические методы идентификации личности. В статье представлено исследование метода идентификации личности по радужной оболочке глаза с использованием нейросетевого подхода на этапах сегментации и формирования признакового представления изображений. Представлено описание набора данных, используемого для реализации этапа сегментации с использованием сверточных нейронных сетей, а также предоставлен доступ к маскам сегментации всего набора данных. Предложен метод формирования признакового представления данных с использованием предварительно обученных сверточных нейронных сетей для решения задачи классификации радужной оболочки глаза. Проведен сравнительный анализ методов формирования признакового представления радужной оболочки глаза, включая классические подходы и нейросетевой подход. Проведен сравнительный анализ методов классификации, включая классические алгоритмы машинного обучения, а именно: метод опорных векторов, случайный лес, метод k-ближайших соседей. Результаты экспериментальных исследований показали высокое качество классификации при применении предложенного подхода.Работа выполнена при финансовой поддержке Министерства науки и высшего образования РФ в рамках госзадания ФНИЦ «Кристаллография и фотоника» РАН. В параграфе «Эксперименты» используется набор данных MMU Iris Database, предоставленный Multimedia University [43]

Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia

Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input

Automated Feature Description of Renal Size Using Image Processing

Ultrasonography (US) is one of the procedures to monitor the growth of renal size in diagnose kidney disease. However considering the complexity of renal size, this procedure leads to inter-observer variability and poor repeatability. Given images from Abdominal CT scan, a level set thresholding and combination of logical and arithmetic operation based method was developed to calculate the automated feature description of renal size. This is achieved by applying 2D CT scan image into image segmentation and feature extraction where thresholding and morphological segmentation method are conducted. Then, parameters of the kidney such as perimeter, area, major axis and minor axis were measured and analyzed in classification step. As a result, analysis on the kidney size between subjects who are normal and the results from the studies has shown capability to classify correctly the size of kidneys about accuracy of 80% to 81% in terms of the kidney’s relative axis which is the ratio of right kidney and left kidneys. In addition, the method in measurement kidney size is compared between manual method and automated method and results shows that the accuracy of the automated method in terms of compactness is about 91% to 95

Evaluation and Analysis of Different Type of Edge Detection Techniques on Cartridge Case Image

The forensic identification of ballistics specimens relies on the detection, recognition and ultimate matching of markings on the surfaces of cartridges and projectiles made by the firearms. The feature extraction from the images is always a very important step in automated examination system. The edge detection refers to the process of identifying and locating sharp discontinuities in an image which helps in pattern recognition. The edge detection of ballistics specimens like, the firing pin mark, head stamp mark and striation on cartridge case the positive identification is based mainly on the image segmentation and feature extraction of the proposed area. Thus, applying an edge detection algorithm to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be substantially simplifiedIn this paper we have compared different techniques of Gradient-based and Laplacian based edge detection on the cartridge case image with MATLAB tool