An Online Numeral Recognition System Using Improved Structural Features – A Unified Method for Handwritten Arabic and Persian Numerals

With the advances in machine learning techniques, handwritten recognition systems also gained importance. Though digit recognition techniques have been established for online handwritten numerals, an optimized technique that is writer independent is still an open area of research. In this paper, we propose an enhanced unified method for the recognition of handwritten Arabic and Persian numerals using improved structural features. A total of 37 structural based features are extracted and Random Forest classifier is used to classify the numerals based on the extracted features. The results of the proposed approach are compared with other classifiers including Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Four different well-known Arabic and Persian databases are used to validate the proposed method. The obtained average 96.15% accuracy in recognition of handwritten digits shows that the proposed method is more efficient and produces better results as compared to other techniques

Fuzzy-Based Histogram Partitioning for Bi-Histogram Equalisation of Low Contrast Images

The conventional histogram equalisation (CHE), though being simple and widely used technique for contrast enhancement, but fails to preserve the mean brightness and natural appearance of images. Most of the improved histogram equalisation (HE) methods give better performance in terms of one or two metrics and sacri ce their performance in terms of other metrics. In this paper, a novel fuzzy based bi-HE method is proposed which equalises low contrast images optimally in terms of all considered metrics. The novelty of the proposed method lies in selection of fuzzy threshold value using level-snip technique which is then used to partition the histogram into segments. The segmented sub-histograms, like other bi-HE methods, are equalised independently and are combined together. Simulation results show that for widerange of test images, the proposed method improves the contrast while preserving other characteristics and provides good trade-off among all the considered performance metrics.This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant DF-374-135-1441

Estimating incomplete information in group decision making: A framework of granular computing

A general assumption in group decision making scenarios is that of all individuals possess accurate knowledge of the entire problem under study, including the abilities to make a distinction of the degree up to which an alternative is better than other one. However, in many real world scenarios, this may be unrealistic, particularly those involving numerous individuals and options to choose from conflicting and dynamics information sources. To manage such a situation, estimation methods of incomplete information, which use own assessments provided by the individuals and consistency criteria to avoid discrepancy, have been widely employed under fuzzy preference relations. In this study, we introduce the information granularity concept to estimate missing values supporting the objective of obtaining complete fuzzy preference relations with higher consistency levels. We use the concept of granular preference relations to form each missing value as a granule of information in place of a crisp number. This offers the flexibility that is required to estimate the missing information so that the consistency levels related to the complete fuzzy preference relations are as higher as possible

Reinforcing synthetic data for meticulous survival prediction of patients suffering from left ventricular systolic dysfunction

Congestive heart failure is among leading genesis of concern that requires an immediate medical attention. Among various cardiac disorders, left ventricular systolic dysfunction is one of the well known cardiovascular disease which causes sudden congestive heart failure. The irregular functioning of a heart can be diagnosed through some of the clinical attributes, such as ejection fraction, serum creatinine etcetera. However, due to availability of a limited data related to the death events of patients suffering from left ventricular systolic dysfunction, a critical level of thresholds of clinical attributes can not be estimated with higher precision. Hence, this paper proposes a novel pseudo reinforcement learning algorithm which overcomes a problem of majority class skewness in a limited dataset by appending a synthetic dataset across minority data space. The proposed pseudo agent in the algorithm continuously senses the state of the dataset (pseudo environment) and takes an appropriate action to populate the dataset resulting into higher reward. In addition, the paper also investigates the role of statistically significant clinical attributes such as age, ejection fraction, serum creatinine etc., which tends to efficiently predict the association of death events of the patients suffering from left ventricular systolic dysfunctio

Bayesian data fusion approaches for vehicle video analytics

University of Technology, Sydney. Faculty of Engineering.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Vehicle Video Analytics (WA) deals with the analysis of the data from a video associated with car images and automatically picking out the information that identifies the vehicle. For example, Licence Plate Recognition (LPR) is a one of the WA applications dealing with the identification of the plates automatically, however, it may not be enough to completely characterise the vehicle; vehicles' features include colour, model, size, speed, shape and number of axles. Combining multiple vehicles' features in a framework will be useful in many applications including stolen car retrieval, petrol theft, car park management and road asset protection. Inspired by these applications, the thesis introduces a comprehensive framework for a vehicle video analytics to identify the many features of vehicles. Several new and improved algorithms are proposed to extract and recognise vehicles features along with image enhancement methods to boost the over all performance of the WA systems. Most LPR systems perform well in a controlled environment or when the effect of noise on the image processing algorithms is small. However, in realistic situations, random noise occurs for many reasons; such as variations in lighting conditions, image quality and additional noise from image sensors. The impact of the random noise can be reduced using information from multiple frames/thresholds. This thesis proposes novel multi frame/threshold data fusion approaches to reduce the effect of the external and internal random noise. Licence plate localisation algorithms are exploited from thresholding techniques (Otsu, Hysteresis, etc.) in order to extract the plate from the image; however, the thresholding does not segment the plate under various illumination conditions. We propose an approach of threshold variation which ensures the object is to be segmented at least once, the dusters are modelled as a joint distribution of their features, and all data from different thresholds update the posterior using Bayesian inference. In the recognition part, we propose a novel approach for increasing the accuracy for any particular character recognition technique by taking advantage of the available frames. Each frame result acts as an individual sensor which informs its recognition results. These results from each individual frame (sensor) are fused to obtain the final recognition result. Simple approaches for the speed, model and colour of the vehicle based on the plate location are presented. The speed is estimated through out tacking the plate across frames. The approaches of plate extraction and recognition are exploited for emblem recognition as well. Since colours can be seen differently under different illuminations, we use illumination estimation and correction for the image before fusing the colour histograms of three areas around the plate which represent vehicle's colour. Extensive experimental test were carried using real data collected on high ways and car parks. Through out the thesis, the fusion problem formulation in the context of WA is illustrated and performance improvements are demonstrated

Pregled algoritmov za analizo slike za prepoznavanje registrske tablice

Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.Ozadje in namen: V članku raziskujemo problem prepoznavanja registrskih tablic (LPR), in podamo pregled števil­nih algoritmov, ki jih lahko uporabimo pri problemih analize slik. V sistemih za podporo vodenju, ki uporabljajo za prepoznavanje slikovnih objektov, je inteligenca vgrajena v statistične algoritme, ki jih je mogoče uporabiti v različnih korakih razpoznavanja. Opisujemo več rešitev, od začetnega koraka do lokalizacije in prepoznavanja slikovnih el­ementov. Cilj tega prispevka je predstaviti več verjetnostnih pristopov v korakih razpoznavanja, nato pa združiti te pristope v en sistem. Večina pristopov uporablja deterministične modele, ki so občutljivi na številne nenadzorovane vplive, kot so osvetlitev, razdalja vozila do kamere, šum pri procesiranju itd. Bistvo naših pristopov je v statističnih algoritmih, ki lahko natančno lokalizirajo in prepoznajo registrsko tablico. Oblikovanje / metodologija / pristop: Predstavimo enostavne in poceni metode za reševanje relativno pomemb­nih problemov z uporabo verjetnostnih pristopov. Pri teh pristopih opisujemo številne statistične rešitve od stopnje začetnega praga do lokalizacije in prepoznavanja slikovnih elementov. V koraku lokalizacije uporabljamo frekvenčne signale iz slik registrskih tablic, ki jih analiziramo z uporabo diskretne Fourier-jeve transformacije. Pri prepoznavanju znakov na tablicah smo uporabili tudi verjetnostni model. Na koncu prikazujemo, kako združiti rezultate iz dvojezičnih tablic, kot so na primer tablice Saudove Arabije. Rezultati: Algoritmi so učinkoviti pri razpoznavanju znakov na vozilih, v stavbah in drugod. Rezultat kaže prednost uporabe verjetnostnega pristopa v vseh korakih razpoznavanja registrskih tablic. Povprečne stopnje uspešnega raz­poznavanja pri uporabi lokalnega nabora podatkov so dosegle 79,13%. Zaključek: Izboljšanje stopnje razpoznavanja je mogoče doseči, če obstajata dva vira informacij, še posebej na registrskih tablicah, na katerih sta dve neodvisni besedili

Modelling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners\u27 needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner

Pregled algoritmov za analizo slike za prepoznavanje registrske tablice

Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.Ozadje in namen: V članku raziskujemo problem prepoznavanja registrskih tablic (LPR), in podamo pregled števil­nih algoritmov, ki jih lahko uporabimo pri problemih analize slik. V sistemih za podporo vodenju, ki uporabljajo za prepoznavanje slikovnih objektov, je inteligenca vgrajena v statistične algoritme, ki jih je mogoče uporabiti v različnih korakih razpoznavanja. Opisujemo več rešitev, od začetnega koraka do lokalizacije in prepoznavanja slikovnih el­ementov. Cilj tega prispevka je predstaviti več verjetnostnih pristopov v korakih razpoznavanja, nato pa združiti te pristope v en sistem. Večina pristopov uporablja deterministične modele, ki so občutljivi na številne nenadzorovane vplive, kot so osvetlitev, razdalja vozila do kamere, šum pri procesiranju itd. Bistvo naših pristopov je v statističnih algoritmih, ki lahko natančno lokalizirajo in prepoznajo registrsko tablico. Oblikovanje / metodologija / pristop: Predstavimo enostavne in poceni metode za reševanje relativno pomemb­nih problemov z uporabo verjetnostnih pristopov. Pri teh pristopih opisujemo številne statistične rešitve od stopnje začetnega praga do lokalizacije in prepoznavanja slikovnih elementov. V koraku lokalizacije uporabljamo frekvenčne signale iz slik registrskih tablic, ki jih analiziramo z uporabo diskretne Fourier-jeve transformacije. Pri prepoznavanju znakov na tablicah smo uporabili tudi verjetnostni model. Na koncu prikazujemo, kako združiti rezultate iz dvojezičnih tablic, kot so na primer tablice Saudove Arabije. Rezultati: Algoritmi so učinkoviti pri razpoznavanju znakov na vozilih, v stavbah in drugod. Rezultat kaže prednost uporabe verjetnostnega pristopa v vseh korakih razpoznavanja registrskih tablic. Povprečne stopnje uspešnega raz­poznavanja pri uporabi lokalnega nabora podatkov so dosegle 79,13%. Zaključek: Izboljšanje stopnje razpoznavanja je mogoče doseči, če obstajata dva vira informacij, še posebej na registrskih tablicah, na katerih sta dve neodvisni besedili

Modeling mobile learning system using ANFIS

Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to determine all possible conditions. ANFIS uses the hybrid (least-squares method and the back propagation gradient descent method) as learning mechanism for the Neural Network to determine the incompleteness in the decision made by human experts. The simulating results by Matlab indicate that the performance of ANFIS approach is valuable and easy to implement