Forthcoming articles

International Journal of Computational Vision and Robotics

International Journal of Computational Vision and Robotics (IJCVR)

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International Journal of Computational Vision and Robotics (34 papers in press)

Regular Issues

  • Malware Classifier for Dynamic Deep Learning Algorithm.   Order a copy of this article
    by Youngbok Cho 
    Abstract: This study proposes a malware classification model that can handle arbitrary length input data using the Microsoft Malware Classification Challenge dataset. We are based on imaging existing data from malware. The proposed model generates a lot of images when malware data is large, and generates a small image of small data. Dynamic RNN learns the generated image as time series data. The output value of the RNN is classified into malware by using only the highest weighted output by applying the Attention technique, and learning the RNN output value by Residual CNN again. Experiments on the proposed model showed a Micro-average F1 score of 92% in the validation data set. Experimental results show that the performance of a model capable of learning and classifying arbitrary length data can be verified without special feature extraction and dimension reduction
    Keywords: : Convolution neural network; malware; deep learning; recurrent neural network; kaggle data.

  • A Review on Content Based Image Retrieval System: Present Trends and Future Challenges   Order a copy of this article
    by Narendra Kumar Rout, Mithilesh Atulkar, Mitul Kumar Ahirwal 
    Abstract: Recently, to get similar image with better accuracy is becoming matter into concern for Content based image retrieval (CBIR) system due to exponential rise in image databases. In CBIR system, a user takes a sample image as input query and retrieves its relevant similar images from a large database of images by the help of features based on color, shape and texture. This study is based on different databases used in the CBIR system, focusing on commonly used databases e.g., Wang, Corel and Brodatz database. Low level features are used in the CBIR system and importance of each feature was graded by their repute based on citations in various comparable studies. In the CBIR system, the features which are more frequently used for achieving better result have been further considered to search images from the image databases. Along with the feature, different weight assignment methods like individual weightage, equal assignment of weights and other assignment methods employed in the analysis of CBIR systems are also reported. However, the percentage weight assignment to the features of the image is calibrated based on its importance in doing accurate searches on the used image databases. This paper presents an intense review of CBIR systems and frequently used features with different weight assignment methods. Including this, the future challenge of entire study is user-free or automated weight assignment methods based on soft computing approaches are required in CBIR systems.
    Keywords: CBIR; low level features; feature extraction methods; weight assignment method; Image database.

  • A Clustering Based Differential Evolution with Parapatric and Cross-Generation Selection   Order a copy of this article
    by Seong-Yoon Shin 
    Abstract: Differential evolution (DE) is one of the efficient Evolutionary algorithm (EA) for continuous optimization problems. It is commonly known that the mutation is one of the cores of the DE algorithm. However, the mutation strategies randomly selected from the current population cant be fully exploited to search the optimal solution, especially in the big data era. To provide some suitable parent individuals for the mutation strategies, it is essential to exploit the data-driven method for analyzing the population data. Tensor decomposition, proven to be an efficient data processing method, can be used to provide data-driven services. We propose a novel data-driven mutation strategy for parent individuals selection, namely tensor-based DE with parapatric and cross-generation (TPCDE). Firstly, we construct a third-order population tensor to represent the relationship among generation, individual, boundary partition. Then the population data is classified into multiple clusters by combing the tensor-based feature extraction approach and affinity propagation (AP) clustering algorithm. Finally, different parent individuals are selected from other clusters to guide evolution. To evaluate the effectiveness of the proposed TPCDE, a series of data-driven experiments are carried out on 13 benchmark functions. The experimental results indicate that TPCDE is an effective and efficient framework to enhance the performance of the DE algorithms.
    Keywords: differential evolution; tensor; clustering; parapatric selection scheme; cross-generation selection scheme.

  • Novel video coding methods for versatile video coding   Order a copy of this article
    by Heeji Han, Daehyeok Gwon, Jaeryun Choe, Haechul Choi 
    Abstract: Versatile video coding (VVC), which is the next generation video coding standard, is being developed to provide greater coding efficiency than existing video coding standards. In VVC, various coding tools related to intra and inter prediction modes have been adopted. This paper introduces several methods that improve coding efficiency or reduce computational complexity on top of VVC adopted tools. The first method enhances the most probable mode list derivation with the statistics of the intra modes of neighbouring blocks. The second method reduces the number of contexts of the merge with motion vector difference mode. The third method excludes invalid block vector predictors early for the intra block copy mode to improve block vector coding. The experimental results show that the three proposed methods show coding efficiencies of -0.05% for all intra coding, -0.02% for random access, and -0.14% for random access coding scenarios, respectively.
    Keywords: versatile video coding; intra prediction; inter prediction; most probable mode; intra block copy.

  • A high-quality frame rate up-conversion technique for Super SloMo   Order a copy of this article
    by Minseop Kim, Haechul Choi 
    Abstract: In this paper, we propose several methods to improve Super SloMo, a deep learning-based frame rate up-conversion technique for the temporal quality improvement of video. In the proposed methods, the training dataset and hyper-parameter are changed and trained to obtain optimal results while maintaining the existing network structure of Super SloMo. The first method improves the cognition of images when trained with the validation set of characteristics similar to the training set. The second method reduces video loss in all validation sets when trained by adjusting the hyper-parameters of the error function value. The experimental results show that the two proposed methods improved the Peak Signal-to-Noise Ratio and the Mean of the Structural Similarity index by 0.11 dB and 0.033% with the specialised training set and by 0.37dB and 0.077% via adjusting the reconstruction and warping loss parameters, respectively.
    Keywords: frame rate up-conversion; deep learning; image processing.

  • Study on Hand Gesture Recognition with CNN-based Deep Learning   Order a copy of this article
    by Buemjun Kim, Kyounghee Lee 
    Abstract: Currently, natural user interface technology is actively studied to enable a computer to understand a users natural behaviours such as gestures and expressions. To recognize humans motions, while the existing approaches generally require additional facilities such as an infrared camera or motion detection sensors, this paper proposes a system based on deep learning to recognize a users hand gestures in normal images generated by common devices such as a webcam. A key feature of the proposed system is input image pre-processing to improve training efficiency and inference accuracy of a deep neural network. It performs black-white binarization process of each image to effectively distinguish a users hand area from the others. Then the proposed system trains a deep neural network by learning from those black-white scale images and makes an inference model to classify various hand signals. Our implementation shows the proposed system has a practicality to be applied for hand gesture recognition by correctly classifying a lot of hand signals such as rock-paper-scissor signs, decimal number 0~9 and Korean alphabet consonants. It is also shown that the confidence degree of those classifications can be considerably improved by the aforementioned image pre-processing. Our future work will be to extend our study to recognize a meaning of hand movements included in a series of continuous images to understand more complicated gestures such as a sign language.
    Keywords: hand gesture recognition; convolutional neural network; deep learning; image binarization; natural user interface.

  • A New Framework for Contour Tracing using Euclidean Distance Mapping   Order a copy of this article
    by Raju G., Sobhana Mari S. 
    Abstract: In this paper a new fast, efficient and accurate contour extraction method, using eight sequential Euclidean distance map and connectivity criteria based on maximal disk, is proposed. The connectivity criterion is based on a set of point pairs along the image boundary pixels. The proposed algorithm generates a contour of an image with less number of iterations compared to many of the existing methods. The performance of the proposed algorithm is tested with a database of handwritten character images. In comparison to two standard contour tracing algorithms (the Moore method and the Canny edge detection method) the proposed algorithm found to give good quality contour images and require less computing time. Further, features extracted from contours of handwritten character images, generated using the proposed algorithm, resulted in better recognition accuracy.
    Keywords: Contour tracing; Euclidean Distance Mapping; Medial Axis Transform; Handwritten Character Recognition.

  • Improving transmission method of Cluster Head Node in Two-level Wireless Sensor Network   Order a copy of this article
    by Jong-Yong Lee, Daesung Lee 
    Abstract: WSN is a wirelessly configured network of nodes equipped with sensors that can collect data in the real world. Sensor nodes have limited energy because they are configured wirelessly. Therefore, long-term use of the network should minimize energy consumption. There are many WSN protocols to increase energy efficiency, among which LEACH Protocol is typical. LEACH Protocol improves energy consumption by reducing the transmission distance of the sensor nodes. However, the transmission distance of nodes elected by the CH node has not been reduced. To improve this, TL- LEACH Protocol has been proposed. The second Clusters are composed of only the CH nodes, so that the transmission distance can be reduced. However, depending on the situation, the transmission distance may increase. In this paper, we applied the Dual-hop method to solve this problem and showed better network lifetime than the existing protocol.
    Keywords: Cluster; Energy; Network; Protocol; Sensor; WSN.

  • A study on selection of optimised piping supports and E-BOM generation   Order a copy of this article
    by Jung-Cheol Kim, Il-Young Moon 
    Abstract: A piping support is an element installed to maintain the stability of a piping system in the design process of a construction plant project. The type and size of the components are determined according to pipe size and pipe load values. Pipe supports change depending on the type and characteristics of the fluid flowing through the pipe, the weight of the pipe and the installation environment. In particular, in the order production environment, the design is frequently changed at the request of the ordering party. Whenever a design change occurs, the type and specifications of the piping supports are changed. When the piping is changed, selection of the piping support must be repeated, which consumes a large amount of time and money. This study proposes a method for constructing a portable document format drawing and an optimised design bill of material without two-dimensional drawing design for piping supports that are frequently changed. This is achieved by a programme according to the characteristics of the piping.
    Keywords: computer aided design; automatic drawing program; selection program; engineering bill of materials; development of software; hanger support.
    DOI: 10.1504/IJCVR.2020.10033156
     
  • Cluster-based WSN Protocol that Improves Network Lifetime through CH Replacement   Order a copy of this article
    by Jong-Yong Lee, Daesung Lee 
    Abstract: Sensor nodes in wireless sensor networks are wirelessly configured, so they are free to install, but there is a problem with limited energy. Since these nodes are no longer usable when they are exhausted, they must use energy efficiently to maximize the network lifetime span. A variety of protocols have been proposed for this, and the cluster-based protocol, the LEACH Protocol, is typical. LEACH Protocol has improved the problem of having the same node elected as the cluster head multiple times through a critical expression, but because it is a probability formula, the cluster can be configured inefficiently. In this paper, we are going to improve the network lifetime by replacing the cluster head with a node in an appropriate position after the cluster is constructed.
    Keywords: Clustering; CH; Formation; Network; Sensor; WSN.

  • Melanoma skin cancer identification with amalgamated TSBTC and BTC colour features using ensemble of machine learning algorithms   Order a copy of this article
    by Sudeep D. Thepade, Gaurav Ramnani, Shubham Mandhare 
    Abstract: Manual diagnosis of diseases is time-consuming, subjective and error prone. There is significant scarcity of medical experts in rural areas. Computer assisted diagnosis may help to overcome these challenges. Melanoma skin cancer may become fatal if not detected during its early stages. In absence of experienced medical professionals, early diagnosis of melanoma may be attempted using machine learning. This paper proposes the melanoma skin cancer identification from dermoscopy skin images by exploring the ensembles of machine learning algorithms using amalgamation of TSBTC and BTC feature extraction methods with various colour spaces. Experimentations conducted with various colour spaces and machine learning algorithms with ensembles resulted in 432 variations of proposed technique. Considering the average of accuracy, sensitivity and specificity; ensemble of AD tree-random forest-SVM in YCbCr colour space with TSBTC features performs best, followed by ensemble of random tree-random forest-AD Tree-SVM in LUV colour space with TSBTC features.
    Keywords: dermoscopy skin images; melanoma; machine learning; feature extraction; colour spaces; ensemble; TSBTC; LUV.
    DOI: 10.1504/IJCVR.2020.10032197
     
  • Cooperative Pixel Clustering for Accurate Automatic Inflamed Appendix Extraction from Ultrasound Images   Order a copy of this article
    by Kwang Baek Kim, Doo Heon Song, Hyun Jun Park 
    Abstract: Reliable diagnosis and management of acute appendicitis is a difficult problem. Automatic extraction of inflamed appendix from ultrasonography is desirable to minimize the operator subjectivity of the ultrasound image analysis. In this paper, we propose a cooperative unsupervised machine learning approach to this automatic segmentation problem. The quantization process is done by fuzzy ART with dynamic controlled vigilance parameter and fuzzy C-Means pixel clustering with good parameter initialization related with fuzzy ART. Two results are combined to produce a conservative but reliable inflamed appendix object formation. In experiment using 80 DICOM format Ultrasonographic images with inflamed appendix, the proposed method was successful in 77 cases or 96.25% correct by pathologists evaluation which is much better performance than previous edge detection-based approach whose performance was less than 83%. This new approach is also relatively immune to the appendix shape which was a weak point of previous pixel clustering approaches.
    Keywords: appendicitis; ultrasound; fuzzy ART; fuzzy c-means; image quantization.

  • An improved edge detection technique   Order a copy of this article
    by Vishtasp Meherhomji, K.B. Ajitha Shenoy 
    Abstract: Traditional edge detection methods tend to apply a single threshold over the entire image. However, natural images rarely have uniform illumination throughout, thus just a single threshold across the image is insufficient. This paper explores a method to recursively divide an image into regions and provide each region with an optimal threshold. For each region, we have calculated the threshold automatically using Otsu's binarisation method. The methods key goal is to reduce the effect of noise present in images, which leads to the elimination of false edges. It does this while also ensuring that true edges present within the image are not lost. We have proved that asymptotic time complexity of the proposed method is O(MN logl) (where l = min{M, N}). We have compared the performance of our method with the Canny edge detection technique. The Canny edge detector is a well known and widely used edge detection technique which outperforms all the classical edge detection techniques. The results show that our method outperforms the Canny edge detection technique. PSNR values for our method are much higher than that of the Canny edge detection algorithm for almost all the images considered from BSD500 benchmark dataset.
    Keywords: image processing; edge detection; feature extraction; computer vision; PSNR; Otsu’s binarisation.
    DOI: 10.1504/IJCVR.2021.10036437
     
  • Dynamic hand gesture recognition of sign language using geometric features learning   Order a copy of this article
    by Saba Joudaki, Amjad Rehman 
    Abstract: In the sign language alphabet, several hand signs are in use. Automatic recognition of dynamic hand gestures could facilitate several applications such as people with a speech impairment to communicate with healthy people. This research presents dynamic hand gesture recognition of the Sign Language alphabet based on the neural network model with enhanced geometric features fusion. A 3D depth-based sensor camera captures the user's hand in motion. Consequently, the hand is segmented using by extracting depth features. The proposed system is termed as Depth based Geometrical Sign Language Recognition (DGSLR). The DGSLR adopted in easier hand segmentation approach, which is further used in other segmentation applications. The proposed geometrical features fusion improves the accuracy of recognition due to unchangeable features against hand orientation or rotation compared to Discrete Cosine Transform (DCT) and Moment Invariant. The findings of the iterations demonstrated that the fusion of the extracted features resulted in a better accuracy rate. Finally, a trained neural network is employed to enhance recognition accuracy. The proposed framework is proficient for sign language recognition using dynamic hand gesture and produces an accuracy of up to 89.52 %.
    Keywords: Digital learning; Deaf community; Healthcare; Sign language; Dynamic hand gesture; Best features selection.

  • Salient Object Detection Using Semantic Segmentation Technique   Order a copy of this article
    by Bashir Ghariba, Mohamed Shehata, Peter McGuire 
    Abstract: Salient Object Detection (SOD) is the operation of detecting and segmenting a salient object in a natural scene. Several studies have examined various state-of-the-art machine learning approaches for SOD. In particular, Deep Convolutional Neural Networks (CNNs) are commonly applied for SOD because of their powerful feature extraction abilities. In this paper, we investigate the capability of several well-known pre-trained models for semantic segmentation, including FCNs, VGGs, ResNets, MobileNet-v2, Xception, and InceptionResNet-v2. These models have been trained over an ImageNet dataset, fine-tuned on a MSRA-10K dataset, and evaluated using other public datasets, such as ECSSD, MSRA-B, DUTS, and THUR15k. The results illustrate the superiority of ResNet50 and ResNet18, which have Mean Absolute Errors (MAE) of approximately 0.93 and 0.92, respectively, compared to other well-known FCN models. Moreover, the most robust model against noise is ResNet50, whereas VGG-16 is the most sensitive, relative to other state-of-the-art models.
    Keywords: Salient Object Detection; Deep learning; Fully Convolutional Network; Semantic segmentation.

  • U-Mosquitto: Extension of Mosquitto Broker for Delivery of Urgent MQTT Message   Order a copy of this article
    by Kitae Hwang, Inhwan Jung, Jae Moon Lee 
    Abstract: MQTT is a message communication protocol that is useful for applications where small devices or remote sensors communicate with low processing capacity or low network bandwidth due to low communication burden or communication code. However, since MQTT does not distinguish between urgent and normal messages, it is not suitable for applications that need to deliver urgent information quickly. This paper attempts to modify the existing MQTT broker to able to accept urgent messages by not modifying the MQTT protocol. We implemented U-Mosquitto to handle urgent messages by modifying Mosquitto well known as the standard MQTT broker. Also, we inserted a message type information into the payload of the MQTT packet. U-Mosquitto\'s urgent message handling effect increases as the number of clients sending messages or traffic increase. It\'s because that urgent messages rarely compete with normal messages when message traffic is low. In this paper, a test system was constructed with a server computer running U-Mosquitto and a number of client computers and various experiments were conducted. Experimental results show that the effect of delivering urgent message faster gets higher as the number of publisher increases in U-Mosquitto. However, it is found that there is a limit to the fast delivery of urgent messages as long as the base algorithm of Mosquitto processing messages is maintained
    Keywords: MQTT; publish-subscribe; Mosquitto; Urgent Message.

  • Energy Based Virtual Screening of Drugs Documented for Schizophrenia against DRD2 and HTR2A   Order a copy of this article
    by Sushma Rani Martha, Ganapati Panda, Manorama Patri 
    Abstract: Schizophrenia is the most commonly known mental disorder with the number of reported cases increasing very fast. The drugs available for the disorder are unable to cure the disease completely and can only offer symptom based treatment and relief. At the same time, it is difficult for practitioners to choose and prescribe the best out of many drugs available in the market. Therefore, it is attempted to find all possible drugs documented for Schizophrenia in the Drugbank and perform Virtual Screening of these drugs against two widely known proteins, DRD2 (Dopamine Receptor - D2) and HTR2A (5-hydroxytryptamine receptor 2A) to discover the drugs that have a higher affinity towards these proteins. After all analysis, it is found that Bromocriptine, Paliperidone, Perospirone, and Risperidone were ranked as the best drugs by Autodock Vina with lowest binding energy values as -10.7, -9.4, -9.2 and 9.1 with DRD2 and -10.2, -9.7, -9.9, -9.9 HTR2A respectively. Due to the unavailability of complete 3D structures of the proteins in Protein Data Bank (PDB), they were modeled using the advanced modeling program of Modeller 9.19 taking three templates for each target protein which were obtained after five rounds of iteration of Delta BLAST (Domain Enhanced Lookup Time Accelerated BLAST) program.
    Keywords: Schizophrenia; Dopamine Receptor; Serotonin Receptor; Delta BLAST; Virtual Screening.

  • Narrow Passage RRT*: A new variant of RRT*   Order a copy of this article
    by Amine BELAID, Boubekeur MENDIL, Ali DJENADI 
    Abstract: Rapidly Exploring Random Tree Star (RRT*) has been widely used for optimal path planning for the reason that can solve high degrees of freedom problems. However, this method has many limitations such as slow convergence rate and solving problems with narrow passages. In addition, the collision checking for this method consumes a lot of time in cluttered environments. In this paper, we present a new variant of RRT* named Narrow Passage RRT* (NP-RRT*), to deal mainly with narrow passage problems and cluttered environments. Our idea is to generate samples near obstacles to explore ef?ciently complex regions in the con?guration space. We have also implemented a path optimization technique to speed up the convergence rate. In order to reduce the complexity of collision checking, we used a pre-procedure that localizes the obstacles before running the planning process. We demonstrate that the complexity of collision checking with our approach does not depend on number of obstacles. Simulation results, performed in different environments comparing our algorithm with RRT*, alongside statistical analysis, con?rm the ef?ciency of NP-RRT* method.
    Keywords: path planning; collision checking; steer function; narrow passage.

  • Plant Leaf Disease Detection using Deep Learning on Mobile Devices   Order a copy of this article
    by Shaheera Rashwan, Marwa Elteir 
    Abstract: Conventional plant disease detection by human experts is subjective, sensitive to human errors, requiring specialized training, and limited to fields that can support the needed human infrastructure. Computer vision algorithms powered by the deep convolutional neural network (DCNN) models have the ability of improving the plant leaf disease detection. However, real world conditions imply that the DCNN models used for plant disease detection must be deployed on mobile/embedded devices. In this paper, we investigate the accuracy and performance of DCNN on mobile devices in order to detect plant diseases. We use pepper, potato and tomato data from the plant village benchmark dataset. We exploit one of the DCNN models commonly used with embedded devices i.e., MobileNetV2 model. We also investigated the effectiveness of other heavy DCNN model which is not designed for embedded devices i.e., AlexNet to assess the performance loss compared to the accuracy gain. Specifically, the used models are trained using data augmentation and different training sizes and different training epochs. We then deploy the model on two mobile devices and test the inference performance using different optimizations. MobilenetV2 achieves overall accuracy of 98.38%, 98.14%, and 93.10 % for pepper, potato, and tomato data, respectively. However, AlexNet achieves better accuracy reaching 99.1, 98.1, and 96.4% for pepper, potato, and tomato data, respectively. Regarding the inference performance on mobile devices, the best performance is achieved mostly when the embedded GPU is utilized. The inference takes 26.3 and 27.5 milliseconds on the average for MobilenetV2 and AlexNet, respectively on a professional class mobile device and 155.07 and 80.67 milliseconds on the average for MobilenetV2 and AlexNet, respectively on an average class mobile device. We conclude that the advanced computational power of current mobile devices makes it feasible to achieve high accuracy from DCNN without scarifying the performance, thus enabling heavy-weighted CNN models to be efficiently deployed on mobile devices.
    Keywords: Plant leaf disease detection; convolutional Neural Network; mobile devices; embedded GPUs; tensorflow; MobileNetV2; AlexNet.

  • Face recognition with Raspberry Pi using deep neural networks   Order a copy of this article
    by Xhevahir Bajrami, Blendi Gashi 
    Abstract: Identifying a person through photography is difficult when dealing with different conditions such as light, color and image cleanliness. Old facial recognition methods are not applicable due to the poor performance they have shown when dealing with a lot of data and under different conditions. However, with deep neural networks we can create systems that achieve high facial recognition efficiency from digital photography. The new deep learning methods have enabled the accuracy of person identification through digital photography to be very high. Knowing the face through software systems remains a separate problem. Facial recognition systems enable the person to be identified through digital photography and deal with large amounts of digital imaging. In this paper will be presented the implementation of deep neural networks, for face recognition from digital photography, on the electronic device Raspberry Pi. In addition to the efficiency of this system in different areas, this implementation is cost effective.
    Keywords: face recognition; raspberry pi; deep neural networks; knn.

  • Anti-phishing model based on relative content mining   Order a copy of this article
    by Parvinder Singh, Bhawna Sharma 
    Abstract: Phishing has attracted larger section of researchers and application developers not due to the rising instances of phishing attacks but also due to the sophisticated techniques that are being employed to execute on the attack. To address one of the diverse mechanisms of phishing attacks, authors have proposed an anti-phishing model for detecting phishing URLs using relative content mining. The relative similarity calculation method uses a combination of cosine similarity and Jaccard similarity. Machine learning oriented feed forward back propagation neural networks (FFBPNN) in combination with support vector machine (SVM) algorithms are used as an anti-phishing technique. A hybrid training and classification algorithm using three kernels namely linear, polynomial and radial basis function (RBF) are implemented. The proposed approach provides best solution for the detection of the phisher in the cyber world. Multiple scenarios such as precision and accuracy are calculated to evaluate the proposed work. Precision of the proposed work is 0.781456 for the detection of cyber-attacks.
    Keywords: phishing; machine learning; training; classification.
    DOI: 10.1504/IJCVR.2021.10036031
     
  • The using of deep neural networks and natural mechanisms of acoustic wave propagation for extinguishing flames   Order a copy of this article
    by Jacek Wilk-Jakubowski, Pawel Stawczyk, Stefan Ivanov, Stanko Stankov 
    Abstract: The article presents an innovative method of flame extinguishing with a high-power acoustic extinguisher, which is equipped with a deep neural network (DNN) flame detection module. Experimental results of flame detection with the use of the DNN networks are presented, and then their extinguishing with the use of sinusoidal waves modulated by triangular waveform, as well as with triangular waves without modulation. The article provides a justification for the approach taken, as well as information on the parameters of the signals used and hardware components. The results are discussed taking into account the power supplied to the loudspeaker and the influence of sound pressure on flame extinguishing as a function of a distance from the extinguisher output. The article concludes with a short summary, in which the benefits and potential application of the technology were indicated.
    Keywords: acoustic extinguisher; acoustic testing; acoustic waves fire suppression; amplitude modulation; deep neural networks; DNN; extinguishing effect; fire detection; firefighting; fire retardation; non-invasive extinguishing of the flames; TensorFlow; wave modulation.
    DOI: 10.1504/IJCVR.2021.10037050
     
  • An anti-phishing model based on similarity measurement   Order a copy of this article
    by Parvinder Singh, Bhawna Sharma, Jasvinder Kaur 
    Abstract: Phishing has represented a more noteworthy danger to clients. In the current work author attempted to build up a powerful anti-phishing technique based on hybrid similarity approach combining Cosine and Soft Cosine similarity that measures the resemblance between user query and database. The proposed similarity hybrid is also evaluated against another similarity hybrid comprising of Cosine and Jaccard similarity measure so as to validate the proposed work. Both hybrid similarities are separately fed to validation layer of feed forward back propagation neural network (FFBPNN) to predict phishing and legitimate websites. The performance of the proposed work is evaluated against data set comprising of 3,000 sample files in terms of positive predictive value (PPV), true positive rate (TPR) and F-measure. The comparative analysis demonstrated that the anti-phishing model using proposed similarity hybrid outperformed the cosine and Jaccard similarity hybrid with 0.233%, 0.2833% and 0.258% higher PPV, TPR and F-measure, respectively.
    Keywords: phishing; cosine similarity; soft-cosine similarity; similarity index; FFBPNN.
    DOI: 10.1504/IJCVR.2021.10037232
     
  • Performance Evaluation of Shannon and Non-Shannon Fuzzy 2-Partition Entropies for Image Segmentation using Teaching-Learning-Based Optimization   Order a copy of this article
    by Baljit Singh Khehra, Arjan Singh, Gurdeep Singh Hura 
    Abstract: Image segmentation is the most significant pre-processing phase of computer vision. Thresholding is one of most suitable approach used for the segmentation of image that has been used extensively by different researchers due to its accuracy and precision. Fuzzy 2-partition entropy with various evolutionary algorithms has been used widely to determine optimal threshold value for image segmentation. Teaching-Learning Based Optimization (TLBO), which is also an evolutionary optimization algorithm, has also been used to maximize the objective function based on the fuzzy 2-partition entropy and subsequently finding optimal threshold value for image segmentation. Fuzzy 2-partition Shannon entropy is generally applied for thresholding. In this paper, fuzzy 2-partition non-Shannon measure of entropy i.e. Havrda-Charvat fuzzy 2-partition entropy and Renyi fuzzy 2-partion entropy using TLBO have been proposed for selecting optimal threshold value. The performance of fuzzy 2-partition Shannon and non-Shannon measures of entropy using TLBO has been compared with other nature based evolutionary algorithms namely Genetic Algorithm (GA), Biogeography-based Optimization (BBO) and with a recursive approach, which is a non-evolutionary approach. The standard test images from the benchmark datasets have been used for experimental purpose. The evaluation of experimental results has been done from qualitative as well as quantitative point of view. From results, it has been observed that TLBO based Havrda-Charvat fuzzy 2-partition entropy gives better performance than all other approaches in terms of quality of the segmented image as well as taking less computational time.
    Keywords: Shannon entropy; Havrda-Charvat entropy; Renyi entropy; Kapur entropy; TLBO; GA; BBO.

  • Comparative analysis of wavelet-based copyright protection techniques   Order a copy of this article
    by Jasvinder Singh, Parvinder Kaur 
    Abstract: Copyright protection of digital content is need of hour. The watermarking in form of wavelet in digital images is becoming popular nowadays. We have implemented and compared different wavelets family-based techniques for copyright protection. We have used Haar, Daubechies, biorthogonal and reverse biorthogonal wavelets to calculate the MSE and PSNR values for sample cover image and watermark images with constant values of approximate coefficient and intensity gain factor. We have also proposed to choose local threshold value of approximate coefficient of cover image in insertion algorithm. The local threshold value of cover image can improve the PSNR value significantly.
    Keywords: wavelets; information hiding; watermarking; copyright protection.
    DOI: 10.1504/IJCVR.2021.10038404
     
  • A secure identity and access management system for decentralising user data using blockchain   Order a copy of this article
    by Tripti Rathee, Parvinder Singh 
    Abstract: The arrival of blockchain technology has made a revolution in the field of cybersecurity. Since on the Internet, almost every interaction involves some digital identity, therefore the ways needed to protect the digital identity over the Internet becomes stronger. In this paper, a blockchain based Identity and access management system MedSecureChain has been implemented on a medical ecosystem. An OAuth-based authentication mechanism is used to provide delegated access, so as to protect and provide the control over user data. Further a document verification system using Interplanetary File System (IPFS) and blockchain technology has been proposed. IPFS is used to store the users data in decentralized manner thus reducing the size of the data. The proposed system provides security and privacy to the identity of the user by using smart contracts. The use of blockchain helps in decentralizing the system thus eliminating the control of single authority over the data.
    Keywords: access control; security; blockchain; distributed ledger; identity management.

  • ARP cache poisoning: detection, mitigation and prevention schemes   Order a copy of this article
    by Jayati Bhardwaj, Virendra Kumar Yadav, Munesh Chandra Trivedi, Anurag Kumar Sen 
    Abstract: ARP is a network communication protocol employed for mapping a network address to a MAC address at the data link layer of the IP suite. However, the absence of authentication process in the ARP protocol allows vulnerabilities like ARP cache poisoning or ARP spoofing to take place. This allows malicious nodes to associate its MAC address with the IP address of host, and hence resulting in the exposure of network to several severe attacks like DoS, MITM, session hijacking and many more. There is no universally accepted benchmark scheme that reaches to the solution at fullest. This paper presents a comprehensive review of all those schemes along with their associated strengths and weaknesses. Also, a comparative evaluation of schemes is included in the paper for further insight into the development of improvised solutions to the above stated problem.
    Keywords: ARP cache poisoning; MAC address; proxy ARP; spoofing; public key cryptography.
    DOI: 10.1504/IJCVR.2021.10037475
     
  • A feature-based approach for digital camera identification using photo-response non-uniformity noise   Order a copy of this article
    by Megha Borole, Satish R. Kolhe 
    Abstract: Source camera identification of an image is an emerging field of digital forensics. To identify the source camera through which the image is captured, photo-response non-uniformity (PRNU) noise is used as a camera fingerprint, as it is a unique characteristic that distinguishes images taken from the similar cameras. This paper presents a feature-based approach to identify the source camera. The input image is denoised using the denoising filter and from this denoised image, PRNU noise pattern is extracted. These PRNU noise patterns are represented by Hu's invariants, which are perpetual under image scaling, translation and rotation. These features are fed to fuzzy min-max neural network (FMNN) for training and classification for digital camera identification. The proposed approach has the ability to identify the cameras capturing the same scene.
    Keywords: camera identification; photo-response non-uniformity; PRNU; fuzzy min-max neural network; FMNN.
    DOI: 10.1504/IJCVR.2021.10035505
     
  • Majority voting-based hybrid feature selection in machine learning paradigm for epilepsy detection using EEG   Order a copy of this article
    by Sunandan Mandal, Bikesh Kumar Singh, Kavita Thakur 
    Abstract: This article presents a combination of statistical and discrete wavelet transform (DWT)-based features for the identification of epileptic seizures in electroencephalogram (EEG) signals. A total of 150 quantitative features are extracted from EEG signals. A multi-criteria hybrid feature selection is proposed by combining six feature ranking methods using the majority voting technique to identify the most relevant EEG markers. Kernel-based support vector machine is used to evaluate the proposed approach along with a hybrid classifier namely support vector neural network (SVNN) which is a combination of support vector machine (SVM) and artificial neural network (ANN). For performance evaluation of the proposed method, a benchmarked database is used. A comparative study of various types of SVM and SVNN with ten-fold and hold-out cross-validation techniques is conducted. The highest classification accuracy (CA) of 98.18% and 100% sensitivity is achieved with a fine Gaussian SVM classifier with hold-out data division protocol.
    Keywords: EEG quantitative features; epilepsy; wavelet transform; multi-criteria feature selection; classification.
    DOI: 10.1504/IJCVR.2021.10037478
     
  • Multi source retinal fundus image classification using convolution neural networks fusion and Gabor-based texture representation   Order a copy of this article
    by Radia Touahri, Nabiha Azizi, Nacer Eddine Hammami, Monther Aldwairi, Nacer Eddine Benzebouchi, Ouided Moumene 
    Abstract: Glaucoma is one of the most known irreversible chronic eye disease that leads to permanent blindness but its earlier diagnosis can be treated. Convolutional neural networks (CNNs), a branch of deep learning, have an impressive record for applications in image analysis and interpretation, including medical imaging. This necessity is justified by their capacity and adaptability to extract pertinent features automatically from the original image. In other hand, the use of ensemble learning algorithms has an important impact to improve the classification rate. In this paper, a two-stage-based image processing and ensemble learning approach is proposed for automated glaucoma diagnosis. In the first stage, the generation of different modalities from original images is adopted by the application of advanced image processing techniques especially Gabor filter-based texture image. Next, each dataset constructing from the corresponding modality will be learned by an individual CNN classifier. Aggregation techniques will be then applied to generate the final decision taking into account the outputs of all CNNs classifiers. Experiments were carried out on Rime-One dataset for glaucoma diagnosis. The obtained results proved the superiority of the proposed ensemble learning system compared to the existing studies with classification accuracy of 89.63%.
    Keywords: deep learning; ensemble classifier fusion; convolution neural networks; CNNs; glaucoma diagnosis; Gabor filter.
    DOI: 10.1504/IJCVR.2021.10037477
     
  • An integrative approach for path planning and tracking of a shape-aware mobile robot in structured environment using vision sensor   Order a copy of this article
    by Sangram Keshari Das, Sabyasachi Dash, B.K. Rout 
    Abstract: A shape-aware path planning algorithm is necessary for real-time execution of a task by a mobile robot whereas path planning algorithms available in literature consider mobile robot as a point object. Current work proposes a shape-aware A* path planning approach with a heuristic function to accommodate the shape of mobile robot. In this paper, the detection, tracking and control of the robot has been carried out for the mobile robot while performing a task in a structured environment. For the implementation and validation, an overhead camera is used to capture the images of obstacles in the task space and published in ROS platform. The captured images are also processed using OpenCV software for detection and tracking using Kanade-Lucas-Tomasi (KLT) and Kalman filter algorithms for different test scenarios. The proposed approach accurately detects and tracks the shape aware mobile robot with percentage error ranging from 6%-10% in different cases.
    Keywords: obstacle detection; path planning; shape aware algorithm; robot operating system; ROS; KLT-based tracking; Kalman filter; OpenCV.
    DOI: 10.1504/IJCVR.2020.10034377
     
  • Design of an ICT convergence farm machinery for an automatic agricultural planter   Order a copy of this article
    by Byungchul Kim, Jaesu Jang, Sangjo Kim, Seonmin Hwang, Moonsun Shin 
    Abstract: Recently, ICT technology such as information technology and automatic control technology has been applied into agriculture, and the era of conversion to smart agriculture aimed at improving productivity and improving quality of agriculture has been reached. In particular, it is required to develop a technology that maximises productivity through growth and quality control based on optimised parameters for each cultivated crop by applying a new automatic control system to the existing traditional agricultural field. In this paper, we propose and design a controller module of agriculture planter applying ICT convergence techniques in order to control rotating speed of various devices in real-time. The planters with the controller are useful for saving time than the existing planters which has been dependent on mechanical type.
    Keywords: agricultural machinery; smart farming; ICT convergence farm machinery; agriculture planters.
    DOI: 10.1504/IJCVR.2021.10037479
     

Special Issue on: The Role of Computer Vision for Smart Cities

  • Image enhancement based on skin-colour segmentation and smoothness   Order a copy of this article
    by Haitao Sang, Bo Chen, Shifeng Chen, Li Yan 
    Abstract: The image restoration tasks represented by image denoising, super-resolution and image deblurring have a wide range of application background, and have become a research hotspot in academia and business circles. A novel image enhancement algorithm based on skin texture preserving is proposed in this paper. The mask has been obtained using the Gaussian fitting, which can have a box blur for many times for skin feather. The denoising smoothing image is fused with the original image mask to preserve the hair details of the original image and enhance the edge details of the contour, so as to provide more effective information for the extraction of edge features. Compared with different methods of image smoothing algorithms, this algorithm is more effective in smoothing the skin edge contour and achieving better detection of images. Experimental results show that the proposed algorithm has strong adaptive capacity and significant effect on most images detection. Specifically, it can moderately smooth the edges of the areas with many details, leaving no traces of an artificial process. The proposed algorithm with image enhancement has a wide range of practicality.
    Keywords: image enhancement; image restoration; image generation and synthesis; texture preserving smoother; skin-colour model.
    DOI: 10.1504/IJCVR.2021.10036485
     
  • Supervised learning software model for the diagnosis of diabetic retinopathy   Order a copy of this article
    by M. Padmapriya, S. Pasupathy 
    Abstract: Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.
    Keywords: diabetic retinopathy; fundus imaging; grey level features; invariant moments; vessel segmentation.
    DOI: 10.1504/IJCVR.2021.10037274