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International Journal of Computer Vision and Image Processing
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ISSN (Print) 2155-6997 - ISSN (Online) 2155-6989
Published by IGI Global

[146 journals]

Adaptive Active Contour Model for Brain Tumor Segmentation
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  Authors: IGI Global
  Abstract:
  Authors : Gunjan Naik (University of Pune, India), Aditya Abhyankar (Savitribai Phule Pune University, India), Bhushan Garware (Persistent Systems, India), Shubhangi Kelkar (Persistent Systems, India)
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.314947
  Date Posted: 12/2/2022 12:00:00 AMFor accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active contour model (GACM) is a standard method for the segmentation of complex organ structures based on edge maps. The GACM performs poorly due to this noise and weak edges. So, the authors propose a method that uses adaptive kernels instead of a constant kernel for creating strong edge maps for GACM. The kernels used in phase congruency are Log Gabor kernels, which resemble similar anisotropic properties like Gabor kernels. They have replaced these with adaptive kernels. This adaptive kernel-based phase congruency provides a robust edge map to be used in GACM. Experimentation shows that when compared with state-of-the-art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Moreover, the proposed methodology substantially requires fewer parameters compared to existing ACM methods.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Fri, 02 Dec 2022 00:00:00 GMT
Scorpion Detection and Classification Systems Based on Computer Vision as
a Prevention Tool
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  Authors: IGI Global
  Abstract:
  Authors :
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.301605
  Date Posted: 1/1/2022 12:00:00 AMIn this paper, automatic and real-time systems were developed to detect and classify two different genera of scorpions using computer vision and deep learning techniques, with the purpose of providing a prevention tool. The images of scorpions were obtained from an arachnology laboratory in Argentina. YOLO (you only look once) and MobileNet models were implemented. The data augmentation technique was applied to significantly increase the amount of training data. High accuracy and recall values have been achieved for both models, which guarantees that they can early and successfully detect scorpions. In addition, the MobileNet model has shown to have excellent performance to detect scorpions within an uncontrolled environment, to carry out multiple detections, and to recognize their danger in case of accidents. Finally, a comparison has been made with other different machine learning-based models used to identify scorpions.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Sat, 01 Jan 2022 00:00:00 GMT
Spatio-Temporal Deep Feature Fusion for Human Action Recognition
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  Authors: IGI Global
  Abstract:
  Authors : Indhumathi C. (Manonmaniam Sundaranar University, India), Murugan V. (Manonmaniam Sundaranar University, India), Muthulakshmi G. (Manonmaniam Sundaranar University, India)
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.296584
  Date Posted: 1/1/2022 12:00:00 AMAction Recognition plays a vital role in many secure applications. The objective of this paper is to identify actions more accurately. This paper focuses on the two stream network in which keyframe extraction method is utilized before extracting spatial features. The temporal features are extracted using Attentive Correlated Temporal Feature (ACTF) which uses Long Short Term Memory (LSTM) for deep features. The spatial and temporal features are fused and classified using multi Support Vector Machine (multiSVM) classifier. Experiments are done on HMDB51 and UCF101 datasets. The results of the proposed method are compared with recent methods in terms of accuracy. The proposed method is proved to work better than other methods by achieving an accuracy of 96% for HMDB51 dataset and 98% for UCF101 dataset.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Sat, 01 Jan 2022 00:00:00 GMT
Simple Approach for Violence Detection in Real-Time Videos Using Pose
Estimation With Azimuthal Displacement and Centroid Distance as Features
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  Authors: IGI Global
  Abstract:
  Authors : Felipe Boris De Moura Partika (violencedetector.org, USA)
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.304462
  Date Posted: 1/1/2022 12:00:00 AMDetecting violence in real time videos is not an easy task even for the most advanced deep learning architectures, considering the subtle details of human behavior that differentiate an ordinary from a violent action. Even with the advances of deep learning, human activity recognition(HAR) in videos can only be achieved at a huge computational cost, most of the time also requiring special hardware for reaching an acceptable accuracy. We present in this paper a novice method for violence detection, a sub-area of HAR, which outperforms in speed and accuracy the state of the art methods. Our method is based on features extracted from the Pose estimator method OpenPose. These features are then transformed into more representative elements in the context of violence detection, which are then submitted to a LSTM neural network to learn how to identify violence. This work was inspired by the violencedetector.org, the first open source project for violence detection in real time videos.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Sat, 01 Jan 2022 00:00:00 GMT
A Hybrid Moth-Flame Optimization Technique for Feature Selection in Brain
Image Classification and Image Denoising by Improved Log Gabor Filter
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  Authors: IGI Global
  Abstract:
  Authors : P. M. Diaz (Dedicated Juncture Researcher's Association, India), M. Julie Emerald Jiju (CSI IT, India)
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.296585
  Date Posted: 1/1/2022 12:00:00 AMIn brain image classification, feature set reduction is essential to build an optimised feature subset that will lead to precise measurement. In this paper, an improved technique for feature selection by Moth Flame Optimization with Opposition Based Learning (OBL) and Simulated Annealing (OB-MFOSA) is proposed. The OBL strategy is used to create the optimum initial solution, while Simulated Annealing improves the search space. The proposed OB-MFOSA shows improved performance than other well-known existing algorithms by eliminating getting stuck in the local optima. By using this hybrid moth flame optimization, the feature set is reduced to 40%. Also, image denoising is performed by Dual Tree Complex Wavelet Transform (DTCWT) with an improved Log Gabor filtering technique. The filter bank of Log Gabor filter bank is tuned by Genetic Algorithm. The selected features from hybrid MFO algorithm are classified using SVM classifier. Experiments reveal that this hybrid algorithm shows accurate classification outputs than the previous methods.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Sat, 01 Jan 2022 00:00:00 GMT
A Deep Learning-Based Approach to Classification of Baby Sign Language
Images
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  Authors: IGI Global
  Abstract:
  Authors : Sulochana Nadgeri (Sir Padampat Singhania University, India), Arun Kumar (Sir Padampat Singhania University, India)
  Volume/Issue: 12/1
  ISSN: 2155-6997
  EISSN: 2155-6989
  
  DOI : 10.4018/IJCVIP.2022010104
  Date Posted: 1/1/2022 12:00:00 AMBaby Sign Language is used by hearing parents to hearing infants as a preverbal communication which reduce frustration of parents and accelerated learning in babies, increases parent-child bonding, and lets babies communicate vital information, such as if they are hurt or hungry is known as a Baby Sign Language . In the current research work, a study of various existing sign language has been carried out as literature and then after realizing that there is no dataset available for Baby Sign Language, we have created a static dataset for 311 baby signs, which were classified using a MobileNet V1, pretrained Convolution Neural Network [CNN].The focus of the paper is to analyze the effect of Gradient Descent based optimizers, Adam and its variants, Rmsprop optimizers on fine-tuned pretrained CNN model MobileNet V1 that has been trained using customized dataset. The optimizers are used to train and test on MobileNet for 100 epochs on the dataset created for 311 baby Signs. These 10 optimizers Adadelta, Adam, Adamax, SGD, Adagrad, RMSProp were compared based on their processing time.This article is available on IGI Global’s premier research database, InfoSci-Journals. To obtain a copy of this article, click here. For more information about the International Journal of Computer Vision and Image Processing (IJCVIP) click here.
  PubDate: Sat, 01 Jan 2022 00:00:00 GMT