A Meta-Analysis on Obstacle Detection for Visually Impaired People

Volume 6 Issue 1 March - May 2019

Survey Paper

A Meta-Analysis on Obstacle Detection for Visually Impaired People

N. Veeranjaneyulu*, K. K. Baseer**, V. S. Asha***, T. Madhu Prakash****

*_****Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

Veeranjaneyulu, N., Baseer, K., K., Asha, V., S., Madhu Prakash, T. (2019). A Meta-Analysis on Obstacle Detection for Visually Impaired People.i-manager’s Journal on Pattern Recognition, 6(1), 40-62. https://doi.org/10.26634/jpr.6.1.15523

Abstract

In general humans have five senses, among all vision is the most important and best gift given to the humans by GOD, but it is limited to some of the people due to their Visual Impairment issues. If vision is the problem then GOD will give the capabilities in other senses. The proportion of visually impaired and blind people in the overall world has become a very large. In a survey report given by WHO (World Health Organization) in 2010, they estimated nearly 285.389 million people are suffering with visual impairment problems across the globe. Many equipment's (Ex: Cane, Assistive shoe, Spectacles) are developed by different authors for detection of obstacles by visual impaired people over the time. All these equipment's are developed by using different techniques like IoT enabled smart cane, GPS/GSM based smart cane, Wearable devices like Assistive shoe's and blind vision spectacles which detects the obstacles, Smart Phone based navigation technology , Image processing techniques based smart cane which uses the camera for capturing the images, ETA's (Electronic Travel Aid's), normal Ultrasonic sensor based smart canes, Sensors(Ultrasonic, LDR's, Soil moisture and water detection) used smart cane and the most advanced smart canes which uses the Algorithms of Machine Learning and Deep Learning ANN, CNN, RNN. In this paper, we present a clear survey of the navigation systems of blind/Visual impaired people that are proposed by different authors highlighting various technologies used, designs implemented, working challenges faced and requirements of blind people for their autonomous navigation either in indoor or outdoor environment. Also we aims at presenting several existing literatures which are based on object detection by blind people. Due to the advancement in techniques and technology, study, analysis and evaluation of all these proposals by different authors will play a vital role. Hence this survey will concentrate on analyzing the process involved in detection of obstacles with different techniques.

Overview of Motion Estimation Algorithms for Video Coding

Volume 6 Issue 1 March - May 2019

Survey Paper

Overview of Motion Estimation Algorithms for Video Coding

Kiran Kumar Vemula*, Neeraja S.**

*Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India.

** Department of Electronics and Communication Engineering, GITAM Institute of Technology, Visakhapatnam, Andhra Pradesh, India.

Vemula, K., K., Neeraja. S. (2019). Overview of Motion Estimation Algorithms for Video Coding.i-manager’s Journal on Pattern Recognition, 6(1), 35-39. https://doi.org/10.26634/jpr.6.1.16511

Abstract

Motion estimation process is an important module in digital video coding applications as it demands more computations when compared to other modules of digital video coding. In order to overcome this difficulty, many motion estimation algorithms were proposed. This paper presents an analysis of some famous algorithms in motion estimation process for digital video coding. In this review, the search procedures, computational complexity and quality of these algorithms are discussed.

Estimation of volume of a solid object From Three Dimensional Point Clouds Generated By Convolutional Neural Networks based Semantic Segmentation

Volume 6 Issue 1 March - May 2019

Research Paper

Estimation of volume of a solid object From Three Dimensional Point Clouds Generated By Convolutional Neural Networks based Semantic Segmentation

Radhamadhab Dalai*

* Department of Computer Science & Engineering,Birla Institute of Technology, Ranchi, India.

Dalai, R. (2019). Estimation of volume of a solid object From Three Dimensional Point Clouds Generated By Convolutional Neural Networks based Semantic Segmentation.i-manager’s Journal on Pattern Recognition, 6(1), 27-34. https://doi.org/10.26634/jpr.6.1.16458

Abstract

Generating three dimensional point cloud for an object in image has found many applications in used in many computer vision systems. In this work a convolutional neural network based semantic segmentation has been used to find region of interest in an image. The region of interest has been represented as point clouds in three dimensional space. Then using image processing technique area based filter operations have been applied to find the total surface area. Finally adding all these small volumes total volume has been calculated. A large number of algorithms have been adapted reconstruction methods have been experimented and tested only for uniform backgrounds, which is disadvantageous for the applications on real images which consists of complex nonuniform regions. In this work semantic segmentation has been used to partition the regions into similar instance based regions. We have used UNET model for the region based segmentation. Then using encoderdecoder scheme the 3D point cloud has been generated after merging pixel clouds. This paper proposes an end-to-end efficient generation network, which is composed of an encoder, a 3D image model, and a decoder. First, a single-view image of object and a nearest-shape retrieval has been formed from UNET are fed into the network; then, the two encoders are merged adaptively according to their homo-graphic or similarity in nature. Then decoder generates fine-grained point clouds from the pixel clouds generated from multiple view images. Each point in the cloud represents a weight according the intensity and color information from which the density and volume of object has been calculated. The experiments on uniform background images show that our method attains accuracy 12 to 15 %margin compared with volumetric and point set generation methods particularly toward large solid objects, and it works multiple view angles as well.

Robot Control using Hand Gesture

Volume 6 Issue 1 March - May 2019

Research Paper

Robot Control using Hand Gesture

U. B. Mahadewaswamy*, Anusha H. N**

*-** Department of Electronics and Communication, JSSS & TU, Mysuru, India.

Mahadevaswamy , U., B., Anusha, H., N. (2019). Overview Robot Control Using Hand Gesture.i-manager’s Journal on Pattern Recognition, 6(1), 11-26. https://doi.org/10.26634/jpr.6.1.15963

Abstract

Hand controllers and electromechanical devices have been used by humans to control robots or machines but there were some constraints in several factors of interaction. Pattern recognition and Gesture recognition are the growing fields of analysis. Hand gesture recognition is very significant for human-computer interaction (HCI). In this work, we present a completely unique real-time methodology for robot control using hand gesture recognition. It is necessary for the user to communicate and control a device in the natural efficient way in human-robot interaction based. The implementation is done using Kinect sensor and Matlab environment. The robot arm is controlled by Firebird V robot. We have implemented a prototype using gesture as a tool for communication with ma-chine command signals are generated using gesture control algorithm. These generated signals are then given to the robot to perform a set of task. This Kinect sensor recognizes the hand gestures and then assigns functions to be performed by the robot for each hand gesture.

Artificial Neural Network-Based Pelvic Inflammatory Disease Diagnosis System

Volume 6 Issue 1 March - May 2019

Research Paper

Artificial Neural Network-Based Pelvic Inflammatory Disease Diagnosis System

Yahaya Mohammed Sani*, Dere Boluwatife Adesola**, Hussaini Abubakar Zubairu***, Ilyasu Anda****

*-*** Department of Information and Media Technology, Federal University of Technology, Minna, Nigeria.

**** Department of Library and Information Technology, Federal University of Technology, Minna, Nigeria.

Sani , Y., M., Adesola, D., B., Zubairu, H., A., & Anda, I. (2019). Artificial Neural Network-Based Pelvic Inflammatory Disease Diagnosis System. i-manager’s Journal on Pattern Recognition, 6(1), 1-10. https://doi.org/10.26634/jpr.6.1.16510

Abstract

Pelvic Inflammatory Disease (PID) is a reproductive health infective disease of feminine genital tract and is commonly affecting the young women and adult female. Clinical manifestation of PID differs among patients and decision of medical experts are based on clinician experience instead of hidden data in the knowledge database. The diagnosis of PID based on heuristic lead to errors, where ectopic pregnancy could be mistaken for PID. This paper presents Artificial Neural Network based model to diagnose pelvic inflammatory diseases based on a set of clinical data. The ANN model was trained with 259 clinical data as input to the neural network. The system can predict the presence or absence of PID based on the available symptoms. An accuracy of 96.1% was recorded based on the confusion matrix. The obtained result is promising, an indication that the system can be effective in diagnosis of PID cases.

Separation, Classification and Expert Mapping of Old Grantha Documents Symbols

Volume 5 Issue 4 December - February 2019

Research Paper

Separation, Classification and Expert Mapping of Old Grantha Documents Symbols

Lalit Prakash Saxena*

* Research Scientist, Applied Research Section, Combo Consultancy, Obra UP India.

Saxena, L. P. (2019). Separation, Classification and Expert Mapping of Old Grantha Documents Symbols. i-manager’s Journal on Pattern Recognition, 5(4), 51-67. https://doi.org/10.26634/jpr.5.4.16108

Abstract

This paper attempts to decipher old documents using symbol to script mapping scheme. Symbols are confined to documents either as isolated notations or handwritten texts with a number of not able features. This paper describes a method to separate and classify handwritten non-cursive symbols in Grantha script. This work uses statistical correlation coefficient method for separation and classification, without the recognition of the symbols. The Grantha script symbols mapping model comprises of selection, separation, preprocessing, classification, and finally mapping. The proposed model employs bounding box algorithm for locating the symbols. The algorithm selects the symbols and excludes the non-symbol components to an extent possible. For experiments, 135 Grantha script document images of varying deteriorating complexities were used. The resulting symbol classification rate (i.e., the proportion of symbols automatically classified) was obtained near to 80%, aiding in mapping to a predetermined mapping scheme.

A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients

Volume 5 Issue 4 December - February 2019

Research Paper

A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients

Surekha Samsani *, G.Jaya Suma**

* Department of Computer Science and Engineering, UCEK(A), Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.

** Department of Information Technology, UCEV(A), Jawaharlal Nehru Technological University Kakinada, Andra Pradesh, India.

Samsani,S.,&Suma,G.J. (2019). A Medical Expert System for Predicting the Prevalence of Autoimmune Diabetes Mellitus in Thyroid Patients. i-manager’s Journal on Pattern Recognition, 5(4), 44-50. https://doi.org/10.26634/jpr.5.4.15947

Abstract

Diabetes Mellitus (DM) and Thyroid are the major coexistent autoimmune disorders affecting people globally. Due to prolonged chronic mental stress in the modern lifestyle, Thyroid disorder is affecting all age groups and people with Thyroid disorder have an increased risk of developing DM complications. Because, abnormal Thyroid dysfunction can have dreadful effects on blood glucose control and can affect the course of DM. This paper proposes a Medical Expert system to assist clinicians in predicting the prevalence of developing autoimmune DM more precisely in patients suffering from Thyroid and further helps to investigate better in the line of improving public health. In this work, Fuzzy logic based inference system and unsupervised machine learning algorithms are used to discover associations and dependencies between Thyroid and DM. The inferred knowledge base is used to design Fuzzy based Expert system. To develop a more realistic expert system, blood sample reports of people affected by DM and Thyroid disorder have been collected from various Endocrine centres in Andhra Pradesh, India. Specificity, Sensitivity, Predictive Values, and Likelihood Ratios of the proposed system are promising in support of system functionality