scholarly journals Thumb Inclination-Based Manipulation and Exploration, a Machine Learning Based Interaction Technique for Virtual Environments

2021 ◽  
Vol 40 (2) ◽  
pp. 358-370
Author(s):  
Muhammad Raees ◽  
Sehat Ullah ◽  
Inam Ur Rehman ◽  
Muhammad Azhar
Keyword(s):  
Virtual Environments ◽  
Region Of Interest ◽  
Input Image ◽  
Learning Phase ◽  
Support Vector ◽  
Svm Classifier ◽  
Interaction Technique ◽  
Lighting Condition ◽  
Wide Range ◽  
Two Phases

In the context of Virtual Reality (VR), interactions refer to the plausible actions in a Virtual Environment (VE). To have an engrossing interface, interactions by the gestures of hand are becoming prominent. With thisresearch work, a novel interaction technique is proposed where interactions are performed on the basis of the position of thumb in dynamic image stream. The technique needs no expensive tracker but an ordinary camera to trace hand movements and position of thumb. The interaction tasks are enacted in distinct interaction states, where the Angle of Inclination (AOI) of thumb is used for state-to-state transition. The angle is computed dynamically between the tip-of-thumb and the base of the Region of Interest (ROI) of an input image. The technique works in two phases: learning phase and application phase. In the learning phase, user-defined fist-postures with distinct AOI are learnt. The Support Vector Machine (SVM) classifier is trained by the AOI of the postures. In the application phase, interactions are performed in distinct interaction states whereas a particular state is activated by posing the known posture. To follow the trajectory of thumb, dynamic mapping is performed to control a virtual hand by the position of thumb in the input image. The technique is implemented in a Visual Studio project called Thumb-Based Interaction for Virtual Environments (TIVE). The project was evaluated by a group of 15 users in a moderate lighting condition. The 89.7% average accuracy rate of the evaluation proves suitability of the technique in the wide range VR applications.

scholarly journals AUTOMATIC FEATURE EXTRACTION FOR BREAST DENSITY SEGMENTATION AND CLASSIFICATION

2017 ◽  
Vol 10 (12) ◽  
pp. 111 ◽  
Author(s):  
Aswathy K Cherian ◽  
Poovammal E ◽  
Malathy C
Keyword(s):  
Human Life ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Textural Features ◽  
Automatic Feature Extraction ◽  
Wide Range ◽  
Risk Of Cancer ◽  
Cancerous Cells ◽  
Aided Diagnosis

Objective: Cancer is the uncontrollable multiplication of cells in human body. The expansion of cancerous cells in the breast area of the women is identified as breast cancer. It is mostly identified among women aged above 40. With the current advancement in the medical field, various automatic tests are available for the identification of cancerous tissues. The cancerous cells are spotted by taking the photo imprint in the form of X-ray comprising the breast area of the woman. Such images are called mammograms. Segmentation of mammograms is the primary step toward diagnosis. It involves the pre-processing of the image to identify the region of interest (ROI). Later, features are extracted from the image which involves the learned features that may be statistical and textural features [7]. When these features are used as input to the simple classifier, it helps us to predict the risk of cancer. The support vector machine (SVM) classifier was proved to produce a better accuracy percentage with the features extracted.Methods: The mammograms are subjected to a pre-processing stage, where the images are processed to identify the ROI. Next, the features are extracted from these images to identify the statistical [9] and textural features. Finally, these features are used as input to the simple classifier, it helps us to predict the risk of cancer.Results: The SVM classifier was proved to produce the maximum accuracy of about 88.67% considering 13 features including both statistical and textural features. The features taken for the study are mean, inverse difference moment, energy, entropy, root mean square, correlation, homogeneity, variance, skewness, range, contrast, kurtosis, and smoothness.Conclusion: Computer-aided diagnosis is one of the most common methods of detection of cancer with mammograms, and it involves minor human intervention. The dataset of mammograms was analyzed and found that SVM provided the highest accuracy of 88.67%. A wide range of the study is progressing in the field of cancer as this disease causes a high threat of human life in this era.

scholarly journals Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition: Performance Assessment and Results Analysis

2021 ◽  
Vol 13 (13) ◽  
pp. 2521
Author(s):  
Jianxin Jia ◽  
Changhui Jiang ◽  
Wei Li ◽  
Haohao Wu ◽  
Yuwei Chen ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Incident Angle ◽  
Vegetation Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Spectral Parameters ◽  
Lighting Condition ◽  
Wide Range ◽  
Spectral Profiles ◽  
Angle Effect

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

2018 ◽  
Vol 11 (6) ◽  
pp. 2455-2474 ◽  
Author(s):  
Christine A. Shields ◽  
Jonathan J. Rutz ◽  
Lai-Yung Leung ◽  
F. Martin Ralph ◽  
Michael Wehner ◽  
...  
Keyword(s):  
Experimental Design ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Tracking Method ◽  
Detection Algorithms ◽  
Atmospheric River ◽  
Wide Range ◽  
Tier 1 ◽  
Intercomparison Project ◽  
Two Phases

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

scholarly journals Fast Pedestrian Recognition Based on Multisensor Fusion

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hongyu Hu ◽  
Zhaowei Qu ◽  
Zhihui Li ◽  
Jinhui Hu ◽  
Fulu Wei
Keyword(s):  
Region Of Interest ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Multisensor Fusion ◽  
Feature Extraction Method ◽  
Urban Road ◽  
Histograms Of Oriented Gradients ◽  
Large Numbers ◽  
Candidate Regions

A fast pedestrian recognition algorithm based on multisensor fusion is presented in this paper. Firstly, potential pedestrian locations are estimated by laser radar scanning in the world coordinates, and then their corresponding candidate regions in the image are located by camera calibration and the perspective mapping model. For avoiding time consuming in the training and recognition process caused by large numbers of feature vector dimensions, region of interest-based integral histograms of oriented gradients (ROI-IHOG) feature extraction method is proposed later. A support vector machine (SVM) classifier is trained by a novel pedestrian sample dataset which adapt to the urban road environment for online recognition. Finally, we test the validity of the proposed approach with several video sequences from realistic urban road scenarios. Reliable and timewise performances are shown based on our multisensor fusing method.

scholarly journals The classification of skateboarding tricks via transfer learning pipelines

2021 ◽  
Vol 7 ◽  
pp. e680
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Aizzat Zakaria ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Accuracy ◽  
Learning Models ◽  
Test Dataset ◽  
Flat Ground

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

scholarly journals Product Label Reading System for Blind People using Support Vector Machine Algorithm

2019 ◽  
Vol 8 (6S2) ◽  
pp. 179-186
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Region Of Interest ◽  
Ground Truth ◽  
Support Vector ◽  
Svm Classifier ◽  
Stroke Width ◽  
Optical Character ◽  
Character Size ◽  
Reading System

Theoretical—This paper shows a camera based assistive content perusing of item marks from articles to support outwardly tested individuals. Camera fills in as fundamental wellspring of info. To recognize the items, the client will move the article before camera and this moving item will be identified by Background Subtraction (BGS) Method. Content district will be naturally confined as Region of Interest (ROI). Content is extricated from ROI by consolidating both guideline based and learning based technique. A tale standard based content limitation calculation is utilized by recognizing geometric highlights like pixel esteem, shading force, character size and so forth and furthermore highlights like Gradient size, slope width and stroke width are found out utilizing SVM classifier and a model is worked to separate content and non-content area. This framework is coordinated with OCR (Optical Character Recognition) to extricate content and the separated content is given as a voice yield to the client. The framework is assessed utilizing ICDAR-2011 dataset which comprise of 509 common scene pictures with ground truth.

scholarly journals Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases

2019 ◽  
Vol 8 (6) ◽  
pp. 3310-3315
Keyword(s):  
Social Life ◽  
Color Image ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Morphological Operations ◽  
Color Processing ◽  
Grey Scale ◽  
Average Accuracy ◽  
Crop Disease

Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.

scholarly journals Pre-trained classification of scalp conditions using image processing

2020 ◽  
Vol 20 (1) ◽  
pp. 138
Author(s):  
Shafaf Ibrahim ◽  
Zarith Azuren Noor Azmy ◽  
Nur Nabilah Abu Mangshor ◽  
Nurbaity Sabri ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
...  
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Conventional Technique ◽  
Input Image ◽  
Support Vector ◽  
Suitable Treatment ◽  
New Treatment ◽  
Processing Techniques ◽  
Selection Of

<span>Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a Region of Interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of Support Vector Machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.</span>

scholarly journals Fingerprint Image Segmentation using Deep Features and SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 1633-1638
Keyword(s):  
Learning Algorithm ◽  
Classification Problem ◽  
Input Image ◽  
Svm Classifier ◽  
Identification System ◽  
Fingerprint Image ◽  
Wide Range ◽  
Fingerprint Segmentation ◽  
Sparse Autoencoder ◽  
The Given

The task of fingerprint segmentation is the most important step in an automated fingerprint identification system. It is essential to separate the fingerprint foreground with ridge and valley structure from the background, which usually contains unwanted data hindering an accurate feature extraction. In the proposed method, fingerprint segmentation is treated as a classification problem by classifying the given input image into foreground class or background class. Here, we have used an unsupervised learning algorithm by using Stacked Sparse Autoencoder (SSAE) to learn the deep features which can very well distinguish the background region from foreground one. Finally, these deep features are given to the SVM classifier. The experimental results prove that the proposed method meets the state-of-the-art results in a wide range of applications.

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