Image Pattern Recognition Techniques Research Articles

Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented.

Using a pattern recognition-based technique to assess the hydrophobicity class of silicone rubber materials

Several transmission and distribution companies worldwide have started to replace their existing outdoor ceramic insulators with silicone rubber insulators. The use of silicone rubber insulators in outdoor insulators was first introduced in the market almost 30 years ago. Various studies have looked at the characteristics of this material under contaminated conditions. Despite the numerous advantages of silicone rubber insulators, they still suffer from ageing especially under severe contamination conditions. Therefore, it is important to develop techniques that enable utility engineers to evaluate the ageing performance of silicone rubber insulators. The aim of this paper is to develop an automatic system to classify and assess the condition of silicone rubber insulators using image processing and pattern recognition techniques. In this research, several feature extraction and selection techniques have been used to extract textural and statistical features. These techniques include discrete cosine transformation, wavelet transformation, Radon transformation, contourlet transformation, gray-level co-occurrence matrices, and stepwise regression. Various classifiers were examined to evaluate the extracted features. The examined classifiers included k-nearest neighbor, neural networks, and linear classifiers. A database comprised of 358 images was collected and preprocessed representing the well-known seven hydrophobicity classes. A recognition rate of 96.5% was achieved using fused features selected by a stepwise regression and classified by a neural network classifier. The system proposed by this research can be used to help utilities assess their silicone rubber insulators automatically and effectively.

Data Extraction from Carte Du Ciel Triple Images

Carte du Ciel (from French, map of the sky) is a part of a 19thcentury extensive international astronomical project whose goal was to mapthe entire visible sky. The results of this vast effort were collected in theform of astrographic plates and their paper representatives that are calledastrographic maps and are widely distributed among many observatories andastronomical institutes over the world. Our goal is to design methods andalgorithms to automatically extract data from digitized Carte du Ciel astrographic maps. This paper examines the image processing and pattern recognition techniques that can be adopted for automatic extraction of astronomical data from stars’ triple expositions that can aid variable stars detection in Carte du Ciel maps.

A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera

The dangerous, overgrown vegetation/trees under high voltage transmission lines right-of-ways (ROWs) have caused severe blackouts/flashovers due to interference with power lines which leads to short circuiting among the conductors. Therefore, these dangerous encroachments are monitored periodically along the electrical distribution networks ROWs through visual inspection, or by airborne system. Each of these methods has its own attributes and limitations and have proved to be costly, time consuming and not much accurate. In these circumstances, it is necessary for the electrical utilities to review their vegetation management practices so as to avoid incidents of unintended encroachments. In tropical countries, overgrown vegetation is a common cause for power line failure. This paper proposes an innovative concept of utilizing a single camera for monitoring dangerous vegetation (trees, shrubs and plants, etc.) under transmission lines ROWs. The main focus is on using an imaging device (camera) integrated on each transmission pole to automate inspection for the vegetation encroachments endangering the transmission lines. These cameras are envisioned to be connected wirelessly to each other, forming a series of wireless camera networks that can be monitored remotely. A single camera mounted on power poles acquires images and sends them wirelessly to the base station. At base station, algorithm (software) trained by image processing and pattern recognition techniques is used to identify (height, depth, and width of encroached vegetation, etc.) excess vegetation encroachments within and outside ROWs. The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance.

Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Lung cancer stands out among all other types of cancer for presenting one of the highest incidence rates and one of the highest rates of mortality. Unfortunately, this disease is often diagnosed late, affecting the treatment result. One of the hopes for changing this scenario lies in achieving a more precocious diagnosis of lung cancer through low-dose computed tomography, used as a screening method in risk groups of smokers or former smokers with elevated tobacco load. In order to help specialists in this search and identification of lung nodules in tomographic images, many research centers develop computer-aided detection systems (CAD systems) which are intended to automate procedures. This work has the purpose of developing a methodology for automatic detection of small lung nodules (with sizes between 2 and 10mm) through image processing and pattern recognition techniques. Some of these techniques are widely used in similar applications, as is the case of the region growing technique for segmentation of the pulmonary parenchyma. Other techniques, with more restricted application, are the Gaussian mixture models and the Hessian matrix for segmentation of structures inside the lung, Tsallis׳s and Shannon׳s entropy measurements as texture descriptors, and support vector machine to classify suspect regions as either nodules or non-nodules. The results achieved with the use of this set of techniques, applied to a sample with 28 exams from a public database, showed that small nodules were detected with a sensitivity of 90.6%, a specificity of 85% and an accuracy of 88.4%. The rate of false positives per exam was of 1.17.

English

 Abstract—Some of the aspects of maintenance of mango fruit quality during the supply chain are harvesting practices, adequate orchard management practices, postharvest treatments, transportation and storage conditions, packing operation, temperature management and ripening at destination. Since the said area is a newly growing field, very little literature is available on the defect detection of alphonso mango. The quality attributes of alphonso include freedom from external damages such as latex or sap injury and decay, bruises, uniform weight, color, aroma, firmness, shape and size. Postharvest losses are high during the supply chain due to harvesting fruit at improper maturity, sap burn, mechanical damage during the whole chain, spongy tissue, lenticels discoloration, decay, chilling injury, fruit softening and disease and pest damage. The main aim of postharvest treatments and management practices in the supply chain is to create suitable conditions or environments to extend the storage life and retain the quality attributes, nutrition and functional compositions. To retain the overall mango fruit quality and to reduce postharvest losses during supply chain can be achieved by adopting suitable postharvest novel technologies involving image processing and pattern recognition techniques. To achieve this task the researchers in this area needs the database that contains healthy as well as Spongy Tissue affected alphonso mangoes. This paper is an attempt towards the direction to develop such database for the researchers working on the non-destructive techniques for defect detection in mangoes. Keywords—Alphonso Mangoes, Spongy Tissue, digital X-Ray imaging, database of alphonso, Non-Destructive Testing.

Indian Sign Language Interpreter with Android Implementation

Sign language is used as a communication medium among deaf and dumb people to convey the message with each other. A person who can talk and hear properly (normal person) cannot communicate with deaf and dumb person unless he/she is familiar with sign language. Same case is applicable when a deaf and dumb person wants to communicate with a normal person or blind person. In order to bridge the gap in communication among deaf and dumb community and normal community, lot of research work has been carried out to automate the process of sign language interpretation with the help of image processing and pattern recognition techniques. This paper proposes optimized approaches of implementing the famous Viola Jones algorithm with LBP features for hand gesture recognition which will recognize Indian sign language gestures in a real time environment. The performance analysis of the proposed approaches is presented along with the experimental results. An optimized algorithm has been implemented in the form of an android application and tested with real time data.

Automatic Identification of Needle Insertion Site in Epidural Anesthesia with a Cascading Classifier

Ultrasound imaging was used to detect the anatomic structure of lumbar spine from the transverse view, to facilitate needle insertion in epidural anesthesia. The interspinous images that represent proper needle insertion sites were identified automatically with image processing and pattern recognition techniques. On the basis of ultrasound video streams obtained in pregnant patients, the image processing and identification procedure in a previous work was tested and improved. The test results indicate that the pre-processing algorithm performs well on lumbar spine ultrasound images, whereas the classifier is not flexible enough for pregnant patients. To improve the accuracy of identification, we propose a cascading classifier that successfully located the proper needle insertion site on all of the 36 video streams collected from pregnant patients. The results indicate that the proposed image identification procedure is able to identify the ultrasound images of lumbar spine in an automatic manner, so as to facilitate the anesthetists’ work to identify the needle insertion point precisely and effectively.

Traffic sign detection via interest region extraction

Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data.

Automated morphometric classification of acute lymphoblastic leukaemia in blood microscopic images using an ensemble of classifiers

Leukaemias are neoplastic proliferations of haemopoietic cells which affect both children and adults and remain one of the leading causes of death around the world. Early diagnosis and classification of such malignant disorders are necessary, and it has always been a challenge in the field of haematology and laboratory medicine. Accurate and authentic diagnosis of leukaemia is essential for the confirmation of the disease, prognostic classification and effective treatment planning. Visual microscopic examination of blood slides is considered as an indispensable diagnostic technique for the screening and classification of leukaemia across continents. However, manual investigation is often slow and limited by subjective assimilation and reduced diagnostic precision. In this study, we investigate the use of image morphometry and pattern recognition techniques for subtyping leukaemic lymphoblasts as per French–American–British classification. Reliable classification results were obtained using the robust segmentation methodology, prominent morphological features and an ensemble of classifiers. To evaluate the performance of the proposed methodology, a comparative study is realised over the available image data-set. The classification rates achieved with the standard classifiers, that is naive Bayesian, K-nearest neighbor, multilayer perceptron, probabilistic neural network and support vector machines, were compared with that obtained using an ensemble of classifiers. It is observed that the classification rate is improved with the use of multiple classifier ensemble and is expected to assist clinicians in making the diagnostic process faster and more accurate.

Automated detection of exudates and macula for grading of diabetic macular edema

Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.

A Persian writer identification method using swarm-based feature selection approach

Handwriting is one of the most famous biometrics which is processed based on image processing and pattern recognition techniques. However, there are a lot of reports that have already been published on handwritten text identification methods and researchers try to improve the accuracy and speed of such methods. This paper presents an offline Persian handwriting identification method in which some new text features are extracted and best ones are selected using a swarm-based approach. The essence of this feature selection method is bees algorithm, which is a modern swarm-based meta-heuristic approach. In the proposed technique, the adaptive neuro-fuzzy inference system ANFIS is employed as classifier and trained by the input feature vectors. It is also compared with a multi-layer perceptron MLP and fuzzy K-nearest neighbour classifiers. To test the proposed method, we have collected a handwritten Persian text dataset from 125 people who have written six sheets with five lines in each of optional Persian texts. Experimental results showed that the prediction accuracy was about 98% in average while the method training time is less than most related works. It seems this method can be extended for other languages by adjusting its parameters.

Research Humanoid Robot Walking Based on Vision-Guided

Walking is a basic function of humanoid robot, this paper presents key ideas of stereo vision based humanoid walking. Image processing techniques and pattern recognition techniques are employed for the obstacle detection and object recognition, data fitting technique is also used to plan the path of the humanoid robot. High precision visual feedback is provided by the combination of real time high precision feature detection and high actuary object detection method. The proposed stereo vision based approach and robot guidance system were evaluated partly by experiments and partly by the simulation with the humanoid robot.

Identification of critical samples of stochastic processes towards feasible structural reliability applications

This paper contributes to the structural reliability problem by presenting a novel approach that enables for identification of stochastic oscillatory processes as a critical input for given mechanical models. The proposed method is based on a graphical representation of such processes utilizing state of the art image processing and pattern recognition techniques, leading to a set of finite rules that consistently identifies those realizations of stochastic processes that would lead to a critical response of a given mechanical model. To examine the validity of the suggested method, large sets of realizations of artificial non-stationary processes were generated from known models, several criteria for critical response were formulated and the results were statistically evaluated. The promising results suggest important applications that would dramatically decrease computational costs e.g. in the field of probabilistic seismic design. Further examination may lead to a formulation of a new class of importance sampling techniques.

Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

The present work has the objective of developing an automatic methodology for the detection of lung nodules. The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures. In the second stage, nodule candidates are segmented. Finally, in the third stage, shape and texture features are extracted, selected and then classified using a support vector machine. In the testing stage, with 140 new exams from the Lung Image Database Consortium image collection, 80% of which are for training and 20% are for testing, good results were achieved, as indicated by a sensitivity of 85.91%, a specificity of 97.70% and an accuracy of 97.55%, with a false positive rate of 1.82 per exam and 0.008 per slice and an area under the free response operating characteristic of 0.8062. Lung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis considerably increases the survival chance of patients. The methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.

AN OPTICAL MUSIC RECOGNITION SYSTEM FOR SKEW OR INVERTED MUSICAL SCORES

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy.

Camera-based visibility estimation: Incorporating multiple regions and unlabeled observations

This paper investigates image processing and pattern recognition techniques to estimate atmospheric visibility based on the visual content of images from off-the-shelf cameras. We propose a prediction model that first relates image contrast measured through standard image processing techniques to atmospheric transmission. This is then related to the most common measure of atmospheric visibility, the coefficient of light extinction. The regression model is learned using a training set of images and corresponding light extinction values as measured using a transmissometer.The major contributions of this paper are twofold. First, we propose two predictive models that incorporate multiple scene regions into the estimation: regression trees and multivariate linear regression. Incorporating multiple regions is important since regions at different distances are effective for estimating light extinction under different visibility regimes. The second major contribution is a semi-supervised learning framework, which incorporates unlabeled training samples to improve the learned models. Leveraging unlabeled data for learning is important since in many applications, it is easier to obtain observations than to label them. We evaluate our models using a dataset of images and ground truth light extinction values from a visibility camera system in Phoenix, Arizona.

The Detection of Surface Quality On-Line Based on Machine Vision in the Production of Bearings

The combining of digital image processing technique and pattern recognition technique, it can be wild used in the products of industry classification and recognition Line bearing assembly defects in this article for the detection and identification of needs, Automatic detection system based on machine vision, contrast measurement plane array camera on a different surfaceImage acquisition, binarization processing for subsequent pretreatment image pattern recognition, feature extraction and eigenvalue comparison, product line surface defect detection and identification.

Image processing and recognition for biological images

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target.

Digital radiographic inspection technique for production friendly quality assessment of PM parts

Progress with the EU DIRA-Green/AutoInspect programme is reported. The target of the programme is to develop the capability for inspection of powder metallurgy (PM) parts – including sintered parts, ideally inline – in production environments, to improve the quality of output batches and to reduce scrap to the greatest possible extent. An important component of the inspection system, which is based on digital radiography, is the development of an advanced data processing system for pattern recognition and feature characterisation. This system will utilise modern image processing and pattern recognition techniques such as the Canny algorithm, active shape modelling and comparison with traditional cross-correlation and distance template matching. It is anticipated that the developed quality control system will be sufficiently versatile to favour its adoption by the wider PM community, among other industry sectors. In particular, the pattern recognition capability will be useful for integration into image processing and automation systems.