Abstract: A method for predicting sleep apnea from neural networks that mainly includes the following steps: a) retrieving an original signal; b) retrieving at least one snoring signal from the original signal by a snoring signal segmentation algorithm and converting the snoring signal into one with one-dimensional vector; c) applying a feature extraction algorithm to process the snoring signal with one-dimensional vector and transform the snoring signal into a feature matrix of two-dimensional vectors; and d) classifying the feature matrix by a neural network algorithm to obtain the number of times of sleep apnea and sleep hypopnea from the snoring signal. The method thereby is able to decide whether the snoring signal has revealed indications of sleep apnea or sleep hypopnea or not.

Abstract: An evaluation device for tear secretion uses an air nozzle and a thermal camera device to cause minor irritations to the eyes and record the temperature changes of the eyes to evaluate the quantity of the tear secretion and to determine whether the subject is able to perform reflex tearing normally or not.

Abstract: An evaluation device for tear secretion uses an air nozzle and a thermal camera device to cause minor irritations to the eyes and record the temperature changes of the eyes to evaluate the quantity of the tear secretion and to determine whether the subject is able to perform reflex tearing normally or not.

Abstract: A method for predicting sleep apnea from neural networks that mainly includes the following steps: a) retrieving an original signal; b) retrieving at least one snoring signal from the original signal by a snoring signal segmentation algorithm and converting the snoring signal into one with one-dimensional vector; c) applying a feature extraction algorithm to process the snoring signal with one-dimensional vector and transform the snoring signal into a feature matrix of two-dimensional vectors; and d) classifying the feature matrix by a neural network algorithm to obtain the number of times of sleep apnea and sleep hypopnea from the snoring signal. The method thereby is able to decide whether the snoring signal has revealed indications of sleep apnea or sleep hypopnea or not.

Abstract: A convolutional neural network model distinguishes eyelash images, break-up area images, non-break-up images, sclera images and eyelid images corresponding to a first prediction score, a second prediction score, a third prediction score, a fourth prediction score and a fifth prediction score to respectively produce a first label, a second label, a third label, a fourth label and a fifth label, thereby a break-up area can be detected in a tear film image and a tear film break-up time can be quantized for detection.

Abstract: A convolutional neural network model distinguishes eyelash images, break-up area images, non-break-up images, sclera images and eyelid images corresponding to a first prediction score, a second prediction score, a third prediction score, a fourth prediction score and a fifth prediction score to respectively produce a first label, a second label, a third label, a fourth label and a fifth label, thereby a break-up area can be detected in a tear film image and a tear film break-up time can be quantized for detection.

Abstract: A system for object recognition includes an image/spectrum sensing device, to fetch an object image from a real object and sense spectra at sensing regions of the real object. A fetching module obtains a real-object image feature pattern for each ROI of the object image. An analyzing module for object image feature searches for a first candidate object from data bank and analyzes a correlation between the real object and candidate object. A fetching module for object spectrum feature obtains a real-object spectrum pattern for each ROI of the object image. An analyzing module for object spectrum feature is to search for a second candidate object from data bank and to further analyze a match level between the real-object and the second candidate object. A fusion module further analyzes information of image feature and spectrum feature find whether or not having a matched object as an identify object.

Abstract: A system for object recognition includes an image/spectrum sensing device, to fetch an object image from a real object and sense spectra at sensing regions of the real object. A fetching module obtains a real-object image feature pattern for each ROI of the object image. An analyzing module for object image feature searches for a first candidate object from data bank and analyzes a correlation between the real object and candidate object. A fetching module for object spectrum feature obtains a real-object spectrum pattern for each ROI of the object image. An analyzing module for object spectrum feature is to search for a second candidate object from data bank and to further analyze a match level between the real-object and the second candidate object. A fusion module further analyzes information of image feature and spectrum feature find whether or not having a matched object as an identify object.

Abstract: A method for instant recognition of traffic lights countdown image that can quickly scan and confirm the circular feature image of a traffic light, and retrieve the countdown image thereof by calculating the displacement ratio from the circular image, then enhance, cut and converse the countdown image to display a feature image thereof, and proceed similarity comparison with collected data to calculate the percentage of similarity. The method eventually brings out a result from the image comparisons, so as to fulfill the effectiveness of searching and instantly recognizing the countdown image of a traffic light.

Abstract: A method for instant recognition of traffic lights countdown image that can quickly scan and confirm the circular feature image of a traffic light, and retrieve the countdown image thereof by calculating the displacement ratio from the circular image, then enhance, cut and converse the countdown image to display a feature image thereof, and proceed similarity comparison with collected data to calculate the percentage of similarity. The method eventually brings out a result from the image comparisons, so as to fulfill the effectiveness of searching and instantly recognizing the countdown image of a traffic light.

Abstract: The method for automatic key posture information abstraction of this invention comprises the steps of: Abstracting from a series of continuous digitized images spatial features of objects contained in said images; abstracting shape features of said objects using a method of probability calculation; detecting key posture information contained in said series of continuous images using a method of entropy calculation; removing redundant key postures; mating obtained key postures with key posture templates stored in a codebook; and encoding mated key postures.

Abstract: The method for automatic key posture information abstraction of this invention comprises the steps of: Abstracting from a series of continuous digitized images spatial features of objects contained in said images; abstracting shape features of said objects using a method of probability calculation; detecting key posture information contained in said series of continuous images using a method of entropy calculation; removing redundant key postures; mating obtained key postures with key posture templates stored in a codebook; and encoding mated key postures.