Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: Disclosed herein is a method and a system for generating a mixed precision quantization model for performing image processing. The method comprises receiving a validation dataset of images to train a neural network model. The method comprises for each image of the validation dataset, generating a union sensitivity list, selecting a group of layers, generating a mixed precision quantization model by quantizing the selected group of layers into a high precision format; computing accuracy of the mixed precision quantization model for comparison with a target accuracy; in response to determining the accuracy is less than the target accuracy, generating another mixed precision model by selecting a next group of layers and computing the accuracy. In response to determining the accuracy is greater than or equal to the target accuracy, storing the mixed precision quantization model as a final mixed precision quantization model for image processing.

Abstract: The present disclosure relates to a system and method of performing quantization of a neural network having multiple layers. The method comprises receiving a floating-point dataset as input dataset and determining a first shift constant for first layer of the neural network based on the input dataset. The method also comprises performing quantization for the first layer using the determined shift constant of the first layer. The method further comprises determining a next shift constant for next layer of the neural network based on output of a layer previous to the next layer, and performing quantization for the next layer using the determined next shift constant. The method further comprises iterating the steps of determining shift constant and performing quantization for all layers of the neural network to generate fixed point dataset as output.

Abstract: Systems and methods are disclosed for predicting one or more medical conditions utilizing digital images and employing artificial intelligent algorithms. The system offers accurate predictions utilizing quantized pre-trained deep learning model. The pre-trained deep learning model is trained on data samples and later refined as the system processes more digital images or new medical conditions are incorporated. One pre-trained deep learning model is used to predict the probability of one or more medical conditions and identify locations in the digital image effected by the one or more medical conditions. Further, one pre-trained deep learning model utilizing additional data and plurality of digital images, forecasts rate of infection and spread of the medical condition over time.

Abstract: A Driver State Monitoring System prevents any casualty on the road because of drowsiness while driving. It is an in-vehicle, vision-based electronic system for automobiles. It utilizes a camera installed on the vehicle facing towards the driver. It captures the edge-based face features of the driver. Thereafter, the real time image processor extracts the desired image from the image and estimates the correct position of the eye, the nose and the head orientation of the driver's face based on the predetermined values. The signal generator generates a warning signal when there is any abnormality detected based on the output of the status examination result generated by the real time image processor. These signals can be can be an acoustic signal, a videft signal, a photonic signal or a haptic signal.

Abstract: Provided is a method of calculating a compensation factor to compensate for lens shading due to the characteristics of an image capturing device, which requires a small amount of memory. A reference image is captured, and a compensation factor is calculated using the characteristics of a lens shading pattern of the captured reference image. A distribution of pixel values is approximated using an exponential spline function, and a compensation factor is calculated using the approximated distribution. In addition, a method and an apparatus for compensating for lens shading by using a calculated compensation factor are provided.

Abstract: Provided is a method of controlling adaptive auto exposure. In the method, a digital photograph captured from an object and a background is divided into an object region where the object is mainly located and a background region where the background is mainly located. Average luminances of the object and background regions are calculated, and a luminance difference between the average luminances of the object and background regions is calculated. If the luminance difference is within a predetermined range, an average luminance of the entire photograph is calculated by applying a weight to the average luminance of the object region, and exposure is controlled based on the calculated average luminance of the entire photograph so as to emphasize the object region.

Abstract: Disclosed is a method of performing contrast enhancement for an image. A mean luminance value is calculated with respect to an image that is read, the type of the image is determined based on the mean luminance value, a contrast reference curve is created by using a hyperbolic tangential function curve according to the type of the image, and the contrast of the image is enhanced by using the contrast reference curve.

Abstract: Provided is a method of controlling adaptive auto exposure. In the method, a digital photograph captured from an object and a background is divided into an object region where the object is mainly located and a background region where the background is mainly located. Average luminances of the object and background regions are calculated, and a luminance difference between the average luminances of the object and background regions is calculated. If the luminance difference is within a predetermined range, an average luminance of the entire photograph is calculated by applying a weight to the average luminance of the object region, and exposure is controlled based on the calculated average luminance of the entire photograph so as to emphasize the object region.

Abstract: Provided is a method of calculating a compensation factor to compensate for lens shading due to the characteristics of an image capturing device, which requires a small amount of memory. A reference image is captured, and a compensation factor is calculated using the characteristics of a lens shading pattern of the captured reference image. A distribution of pixel values is approximated using an exponential spline function, and a compensation factor is calculated using the approximated distribution. In addition, a method and an apparatus for compensating for lens shading by using a calculated compensation factor are provided.

Abstract: The present invention is directed to an eye state detecting apparatus and method, that is, including the step of preliminarily discriminating an eye opening and an eye closure by setting an automatic threshold from an eye region and thus dividing an image, and obtaining boundary points of divided zones and using an ellipse most properly equal to the boundary points and consecutively the step of in a case preliminarily discriminated as the eye closure, if an eye closure time is greater than a preset threshold time, the eye state is discriminated into an eye closure, and if not greater, discriminated into an eye blinking.

Abstract: A Driver State Monitoring System prevents any casualty on the road because of drowsiness while driving. It is an in-vehicle, vision-based electronic system for automobiles. It utilizes a camera installed on the vehicle facing towards the driver. It captures the edge-based face features of the driver. Thereafter, the real time image processor extracts the desired image from the image and estimates the correct position of the eye, the nose and the head orientation of the driver's face based on the predetermined values. The signal generator generates a warning signal when there is any abnormality detected based on the output of the status examination result generated by the real time image processor. These signals can be can be an acoustic signal, a videft signal, a photonic signal or a haptic signal.