Perceiving Unknown in Dark from Perspective of Cell Vibration

Low light very likely leads to the degradation of image quality and even causes visual tasks' failure. Existing image enhancement technologies are prone to over-enhancement or color distortion, and their adaptability is fairly limited. In order to deal with these problems, we utilise the mechanism of biological cell vibration to interpret the formation of color images. In particular, we here propose a simple yet effective cell vibration energy (CVE) mapping method for image enhancement. Based on a hypothetical color-formation mechanism, our proposed method first uses cell vibration and photoreceptor correction to determine the photon flow energy for each color channel, and then reconstructs the color image with the maximum energy constraint of the visual system. Photoreceptor cells can adaptively adjust the feedback from the light intensity of the perceived environment. Based on this understanding, we here propose a new Gamma auto-adjustment method to modify Gamma values according to individual images. Finally, a fusion method, combining CVE and Gamma auto-adjustment (CVE-G), is proposed to reconstruct the color image under the constraint of lightness. Experimental results show that the proposed algorithm is superior to six state of the art methods in avoiding over-enhancement and color distortion, restoring the textures of dark areas and reproducing natural colors. The source code will be released at https://github.com/leixiaozhou/CVE-G-Resource-Base.Comment: 13 pages, 17 figure

Low-light Image Enhancement Using Cell Vibration Model

 Low light very likely leads to the degradation of an image's quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost. The image source and code will be released at https://github.com/leixiaozhou/CDEFmethod. </p

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep neural network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%

Deep convolution network based emotion analysis towards mental health care

Facial expressions play an important role during communications, allowing information regarding the emotional state of an individual to be conveyed and inferred. Research suggests that automatic facial expression recognition is a promising avenue of enquiry in mental healthcare, as facial expressions can also reflect an individual’s mental state. In order to develop user-friendly, low-cost and effective facial expression analysis systems for mental health care, this paper presents a novel deep convolution network based emotion analysis framework to support mental state detection and diagnosis. The proposed system is able to process facial images and interpret the temporal evolution of emotions through a new solution in which deep features are extracted from the Fully Connected Layer 6 of the AlexNet, with a standard Linear Discriminant Analysis Classifier exploited to obtain the final classification outcome. It is tested against 5 benchmarking databases, including JAFFE,KDEF,CK+, and databases with the images obtained ‘in the wild’ such as FER2013 and AffectNet. Compared with the other state-of-the-art methods, we observe that our method has overall higher accuracy of facial expression recognition. Additionally, when compared to the state-of-the-art deep learning algorithms such as Vgg16, GoogleNet, ResNet and AlexNet, the proposed method demonstrated better efficiency and has less device requirements. The experiments presented in this paper demonstrate that the proposed method outperforms the other methods in terms of accuracy and efficiency which suggests it could act as a smart, low-cost, user-friendly cognitive aid to detect, monitor, and diagnose the mental health of a patient through automatic facial expression analysis</p