Resource Optimized Selective Image Encryption of Medical Images Using Multiple Chaotic Systems

The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition

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High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

Electronic Health Record, which comprises of medical imagery, patient history, prescription and clinical observations is a highly sensitive data. This data is not generally sent over the network in its original form as it is prone to numerous security related issues. Generally, some encryption algorithms are applied on the data (medical image) before transmission over the network. However, resource constrained devices cannot perform complex encryption sand the process of encryption is entrusted to some third party like Cloud. But this third-party encryption requires the privacy of data to be preserved. After ensuring the privacy and performing third party encryption, the encrypted data can be securely transmitted over the network. This concept is used in the proposed work and a secure data transmission method is implemented. We have used two encryption techniques viz, chaotic encryption and bitplane encryption on the medical image which gives extra security to the image. While using the facilities of cloud for encryption (chaotic) the proposed method ensures that the cloud administrators get no hint about the information present in the data that is sent to them. In addition, the authenticity of the medical images is ensured and the encryption time obtained is small (must criterion for real time applications). The objectives of our work are verified by experimentations that are carried out on various medical images. The experimental results and theoretical analysis confirm the effectiveness of proposed work in terms of encryption time, security and authenticity.

Secure patient data transmission on resource constrained platform

To achieve non-uniform imaging with a large field of view and high efficiency as well as to obtain an adjustable fovea with super-resolution, we proposed a curved retina-like camera array imaging system (CRCS), which is built by an eight-camera array distributed non-uniformly on a curved surface and a camera coaxial with Risley prisms located in the center. By the non-uniform imaging, the field of view of the developed prototype is 150∘×40∘ with a reduction of data redundancy by 87.62%. Besides, the experimental results show that CRCS can obtain clear and sharp images of farther targets of interest around the fovea field of view with a constant focal length.

Curved retina-like camera array imaging system with adjustable super-resolution fovea.


 How to effectively apply AR (Augmented Reality) new technology to create better safety environment for society and enterprises is the concern of many government and enterprise leaders. This research combines AR smart glasses, multi face recognition technology and control system technology to develop a face recognition active security platform which can effectively improve the arrest of criminals and establish a safer social environment.
 


An Active Security System Based on AR Smart Classes and Face Recognition Technology

Different from those in low-resolution tasks, targets in high-resolution remote sensing recognition tasks are closed. Usually targets with higher resolution, such as oil tanks and ships bear relatively simple features and gather densely in small areas. The recognition of such targets is more dependent on the fundamental features. Ships, tanks and aircraft targets with high resolution are closed targets, which contain obvious contour and have features that are quite different from the surrounding environment. Therefore, aiming at the targets in remote sensing images with high resolution of sub-meter that is below 1m, we proposed a recognition method based on region of interest (ROI) extraction using visual saliency mechanism. Combining the result of saliency detection, MeanShift algorithm is used to image segmentation and feature extraction. Then, machine learning algorithms are applied to perform the target recognition. We achieved a recognition accuracy of more than 85% on the dataset of aircraft, oil tank and ship targets in our work.

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Method of Target Recognition in High-resolution Remote Sensing Image Based on Visual Saliency Mechanism and ROI Region Extraction

Facial landmarks encode critical information about face, which plays an important role in human communications. Accurate detecting and tracking facial landmarks have great potential value in intelligent user interfaces such as human-computer interactions. However, for face images with sever occlusions which may happen in real life such as hand occlusion, gesture occlusion and etc, detecting the facial landmarks is still a challenging problem. In this paper, we present a robust facial landmark detection method for image with occlusions based on Restricted Boltzmann Machine (RBM). We first present a face shape prior model which is constructed based on RBM to model the spatial shape patterns of the face. The detection process is accomplished by combining the prior shape model with the image measurements of facial landmarks. The low accuracy image measurements can be refined by the shape information embedded in the prior model. For the landmarks with severe occlusions, we firstly evaluate and determine the facial landmark occlusions, and replace their image measurements. The new image measurements are then fed into the prior model as evidence to predict the true locations. Evaluation on 3 databases demonstrates that the proposed method can detect facial landmarks accurately under severe occlusion, and achieved significant improvement over the current state of the art methods.

/pdf/facial-landmarks-detection-under-occlusions-via-extended-4ti9ubxmun.pdf

Bing Liu

Papers

Method of Target Recognition in High-resolution Remote Sensing Image Based on Visual Saliency Mechanism and ROI Region Extraction

Facial Landmarks Detection under Occlusions via Extended Restricted Boltzmann Machine