Special Issue "Security Enhancement of Image Recognition System in IoT based Smart Cities"

Submission Deadline: 05 October 2019 (closed)
Guest Editors
Dr. Gunasekaran Manogaran, University of California, Davis, USA
Professor Hassan Qudrat-Ullah, York University, Toronto, Canada
Assistant Professor Bharat S. Rawal Kshatriya, Pennsylvania State University, Abington, USA


Smart cities can improve the quality of life and in order to improve the quality IoT plays a major role in building the smart environment. Certain factors like security, monitoring and safety measures should be considered in building the smart cities. By implementing Internet of things in a smart environment the cities become smarter. The Recent advancements in sensor technologies have made the environment much smarter. The image recognition system as a part of secure and smart monitoring has the facility to recognize the gesture, signs, movements and the color in real time basis. By integrating the IoT with image recognition system the security and safety can be increased the smart cities. The security enhancement of the image recognition system should be considered as a primary factor because the image can be transferred from various places over the unsecured network or less secure network. By using the right kind of encryption for the image recognition process the security can be increased and the overall process becomes much safer. This special issue on the Security Enhancement of Image Recognition System in IoT based Smart Cities provides an excellent platform to exchange ideas, frameworks and technological approaches in developing, designing, implementing and operating within a specific environment inside the smart cities. This research on the security enhancement in image recognition system in an IOT based environment can be used in identifying the various opportunities and threats in the deployment of smart environment. Topics of interest include but are not restricted to: 
• Intelligent and secure face recognition system in Smart Cities

• An efficient Security based algorithm for image recognition system in IoT smart city framework

• Hardware Integration and Implementation of IoT and image processing on Security aspects

• Advanced image processing security applications for Smart Cities

• Security and privacy concerns of Image recognition system in an IoT based environment

• Secure Intrusion detection systems for IoT based smart cities

• Secure integration of Internet of Things with Image Processing system

• Recent advances secure image processing system for smart cities

• Enhancing security and privacy in image recognition based authentication systems

• IoT based Automated Image Detection Algorithm for Surveillance Systems in Smart Cities

• Increase of Security and Dependability for IoT enabled Smart environment

• Planning and building smart cities based on internet of things using secure recognition methods

• Importance of Internet of Things Security for Smart Cities

Internet of things; Image recognition system; Security enhancement; Smart cities

Published Papers
  • Fish-Eye Image Distortion Correction Based on Adaptive Partition Fitting
  • Abstract The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens. As a result, it is difficult to use the obtained image information. To make use of the effective information in the image, these distorted images must first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities. To solve this problem, this study presents an adaptive classification fitting method for fish-eye image correction. The degree of distortion in the image is represented by the difference value of the distances from the distorted… More
  •   Views:1257       Downloads:1137       Cited by:1        Download PDF

  • An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm
  • Abstract The implementation of content-based image retrieval (CBIR) mainly depends on two key technologies: image feature extraction and image feature matching. In this paper, we extract the color features based on Global Color Histogram (GCH) and texture features based on Gray Level Co-occurrence Matrix (GLCM). In order to obtain the effective and representative features of the image, we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively. And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to… More
  •   Views:1912       Downloads:1617        Download PDF

  • Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network
  • Abstract Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale… More
  •   Views:2355       Downloads:1697       Cited by:2        Download PDF

  • A Lane Detection Method Based on Semantic Segmentation
  • Abstract This paper proposes a novel method of lane detection, which adopts VGG16 as the basis of convolutional neural network to extract lane line features by cavity convolution, wherein the lane lines are divided into dotted lines and solid lines. Expanding the field of experience through hollow convolution, the full connection layer of the network is discarded, the last largest pooling layer of the VGG16 network is removed, and the processing of the last three convolution layers is replaced by hole convolution. At the same time, CNN adopts the encoder and decoder structure mode, and uses the index function of the… More
  •   Views:2829       Downloads:2652       Cited by:1        Download PDF

  • Mixed Noise Parameter Estimation Based on Variance Stable Transform
  • Abstract The ultimate goal of image denoising from video is to improve the given image, which can reduce noise interference to ensure image quality. Through denoising technology, image quality can have effectively optimized, signal-to-noise ratio can have increased, and the original mage information can have better reflected. As an important preprocessing method, people have made extensive research on image denoising algorithm. Video denoising needs to take into account the various level of noise. Therefore, the estimation of noise parameters is particularly important. This paper presents a noise estimation method based on variance stability transformation, which estimates the parameters of variance stability… More
  •   Views:4055       Downloads:1885       Cited by:1        Download PDF

  • Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network
  • Abstract In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels, this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks. After adding idle cognitive users for detection, the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time. Both theoretical analysis and simulation results show that using idle cognitive users can reduce… More
  •   Views:2177       Downloads:1547        Download PDF