Abstract: An image recognition method, a training system for an object recognition model and a training method for an object recognition model are provided. The image recognition method includes the following steps. At least one original sample image of an object in a field and an object range information and an object type information in the original sample image are obtained. At least one physical parameter is adjusted to generate plural simulated sample images of the object. The object range information and the object type information of the object in each of the simulated sample images are automatically marked. A machine learning procedure is performed to train an object recognition model. An image recognition procedure is performed on an input image.

Abstract: A recognition system and an image augmentation and training method thereof are provided. The image augmentation and training method of a recognition system includes the following steps. A plurality of image frames are obtained, wherein each of the image frames includes an object pattern. A plurality of environmental patterns are obtained. The object pattern is separated from each of the image frames. A plurality of image parameters are set. The image frames, based on the object patterns and the environmental patterns, are augmented according to the image parameters to increase the number of the image frames. A recognition model is trained using the image frames.

Abstract: An integrated system of a physical consumption environment and a network consumption environment and a control method thereof are provided. The control method of the integrated system of the physical consumption environment and the network consumption environment includes the following steps. A biological characteristic image of a consumer in the physical consumer environment is obtained. The biological characteristic image is identified to obtain an identity information from an identity database. An activity of the consumer in the physical consumption environment is detected. A product followed by the consumer and/or a physical behavior information are obtained according to the activity of the consumer in the physical consumption environment. A network behavior information of the consumer in the network consumer environment is obtained according to the identity information. A price of the product is dynamically adjusted according to the physical behavior information and the network behavior information.

Abstract: An electronic device and a method for determining medical images are provided. The method includes: obtaining an input image; inputting the input image into a first machine learning model to generate a labeled image, wherein the labeled image includes a plurality of area images; inputting the labeled image into a second machine learning model to generate a plurality of area determination data corresponding to the plurality of area images; and displaying a determination result including the plurality of area determination data.

Abstract: A training method and training system for a resolution improvement model and a boundary detection method using the resolution improvement model are provided. The training method for the resolution improvement model includes the following steps. A low-resolution image is inputted. Pixels of the low-resolution image are captured and reorganized to generate a high-resolution image according to convolutional features. The resolution of the high-resolution image is higher than that of the low-resolution image. When capturing the low-resolution image, a condition mask is used to filter off the noise content, as well as sharpen the edge. The high-resolution image is compared with a ground-truth target image to output a discrimination result. The convolutional features are updated according to the discrimination result.

Abstract: A recognition system and an image augmentation and training method thereof are provided. The image augmentation and training method of a recognition system includes the following steps. A plurality of image frames are obtained, wherein each of the image frames includes an object pattern. A plurality of environmental patterns are obtained. The object pattern is separated from each of the image frames. A plurality of image parameters are set. The image frames, based on the object patterns and the environmental patterns, are augmented according to the image parameters to increase the number of the image frames. A recognition model is trained using the image frames.

Abstract: An adjusting method and a training system for a machine learning classification model and a user interface are provided. The machine learning classification model is used to identify several categories. The adjusting method includes the following steps. Several identification data are inputted to the machine learning classification model to obtain several confidences of the categories for each of the identification data. A classification confidence distribution for each of the identification data whose highest value of the confidences is not greater than a critical value is recorded. The classification confidence distributions of the identification data are counted. Some of the identification data are collected according to the cumulative counts of the classification confidence distributions. Whether the collected identification data belong to a new category is determined. If the collected identification data belong to a new category, the new category is added.

Abstract: An object identification method includes: establishing a training data base including a photographing distance of a training image and a training camera parameter; in photographing a target test object, obtaining a test image, a depth image, an RGB image and a test camera parameter; and based on the training database, the depth image and the test camera parameter, adjusting the RGB image wherein the adjusted RGB image having a size equivalent to the training image of the training database.

Abstract: An image recognition method, a training system for an object recognition model and a training method for an object recognition model are provided. The image recognition method includes the following steps. At least one original sample image of an object in a field and an object range information and an object type information in the original sample image are obtained. At least one physical parameter is adjusted to generate plural simulated sample images of the object. The object range information and the object type information of the object in each of the simulated sample images are automatically marked. A machine learning procedure is performed to train an object recognition model. An image recognition procedure is performed on an input image.

Abstract: An integrated system of a physical consumption environment and a network consumption environment and a control method thereof are provided. The control method of the integrated system of the physical consumption environment and the network consumption environment includes the following steps. A biological characteristic image of a consumer in the physical consumer environment is obtained. The biological characteristic image is identified to obtain an identity information from an identity database. An activity of the consumer in the physical consumption environment is detected. A product followed by the consumer and/or a physical behavior information are obtained according to the activity of the consumer in the physical consumption environment. A network behavior information of the consumer in the network consumer environment is obtained according to the identity information. A price of the product is dynamically adjusted according to the physical behavior information and the network behavior information.

Abstract: A sight vector detecting method includes: capturing a user image by an image capture unit and a depth capture unit to obtain a first image and a distance information; based on the first image and the distance information, finding an eye center location of the user; predicting a user sight location by a gaze model to find a target sight location of the user on a target; based on the eye center location of the user, finding a first word coordinate of the eye center location of the user; based on the target sight location of the user, finding a second word coordinate of the target sight location of the user; and based on the first word coordinate of the eye center location of the user and the second word coordinate of the target sight location of the user, calculating a sight vector of the user.

Abstract: An object identification method includes: establishing a training data base including a photographing distance of a training image and a training camera parameter; in photographing a target test object, obtaining a test image, a depth image, an RGB image and a test camera parameter; and based on the training database, the depth image and the test camera parameter, adjusting the RGB image wherein the adjusted RGB image having a size equivalent to the training image of the training database.

Abstract: A sight vector detecting method includes: capturing a user image by an image capture unit and a depth capture unit to obtain a first image and a distance information; based on the first image and the distance information, finding an eye center location of the user; predicting a user sight location by a gaze model to find a target sight location of the user on a target; based on the eye center location of the user, finding a first word coordinate of the eye center location of the user; based on the target sight location of the user, finding a second word coordinate of the target sight location of the user; and based on the first word coordinate of the eye center location of the user and the second word coordinate of the target sight location of the user, calculating a sight vector of the user.

Abstract: An image recognition method adapted to recognize a target image is provided. The method includes: providing the target image; training a learning database based on a plurality of feature blocks of the target image; randomly obtaining a plurality of incomplete feature blocks of the target image; adding the plurality of incomplete feature blocks into the learning database to form an enhancement learning database; and recognizing the target image based on the enhancement learning database. In addition, an image recognition device is also provided.

Abstract: An image recognition method adapted to recognize a target image is provided. The method includes: providing the target image; training a learning database based on a plurality of feature blocks of the target image; randomly obtaining a plurality of incomplete feature blocks of the target image; adding the plurality of incomplete feature blocks into the learning database to form an enhancement learning database; and recognizing the target image based on the enhancement learning database. In addition, an image recognition device is also provided.

Abstract: A temperature sensing apparatus configured to measure a temperature distribution of a surface to be measured is provided. The temperature sensing apparatus includes a lens set, a filtering module, a plurality of sensor arrays, and a processing unit. The lens set is configured to receive radiation from the surface to be measured. The filtering module is configured to filter the radiation from the lens set into a plurality of radiation portions respectively having different wavelengths. The sensor arrays are configured to respectively sense the radiation portions. The processing unit is configured to calculate an intensity ratio distribution of the radiation between the different wavelengths according to the radiation portions respectively sensed by the sensor arrays and determine the temperature distribution according to the intensity ratio distribution. A laser processing system and a temperature measuring method are also provided.

Abstract: A temperature sensing apparatus configured to measure a temperature distribution of a surface to be measured is provided. The temperature sensing apparatus includes a lens set, a filtering module, a plurality of sensor arrays, and a processing unit. The lens set is configured to receive radiation from the surface to be measured. The filtering module is configured to filter the radiation from the lens set into a plurality of radiation portions respectively having different wavelengths. The sensor arrays are configured to respectively sense the radiation portions. The processing unit is configured to calculate an intensity ratio distribution of the radiation between the different wavelengths according to the radiation portions respectively sensed by the sensor arrays and determine the temperature distribution according to the intensity ratio distribution. A laser processing system and a temperature measuring method are also provided.

Abstract: A display measuring device for measuring a display, includes a photosensitive unit, a first rotation plane mirror, a second rotation plane mirror, a first lens module, a second lens module, and an optic reflecting unit. The first lens module projects a first incident image from the display to the first rotation plane mirror. The first rotation plane mirror reflects the projected first incident image from the first lens module to the optic reflecting unit. The second lens module projects a second incident image from the display to the second rotation plane mirror. The second rotation plane mirror reflects the projected second incident image from the second lens module to the optic reflecting unit. The optic reflecting unit reflects the reflected first incident image to the photosensitive unit, and reflects the reflected second incident image to the photosensitive unit.

Abstract: A display measuring device for measuring a display, includes a photosensitive unit, a first rotation plane mirror, a second rotation plane mirror, a first lens module, a second lens module, and an optic reflecting unit. The first lens module projects a first incident image from the display to the first rotation plane minor. The first rotation plane minor reflects the projected first incident image from the first lens module to the optic reflecting unit. The second lens module projects a second incident image from the display to the second rotation plane minor. The second rotation plane minor reflects the projected second incident image from the second lens module to the optic reflecting unit. The optic reflecting unit reflects the reflected first incident image to the photosensitive unit, and reflects the reflected second incident image to the photosensitive unit.

Abstract: A system for constructing high resolution images includes a beam splitter assembly, a light intensity modulator, an image capturing module and an image processing module. The beam splitter assembly is utilized to reflect a light beam generated from a light source generating device and generate a splitting beam. The light intensity modulator is utilized to modulate the intensity of the splitting beam to generate a modulating beam, which includes a predetermined noise. The modulating beam is emitted onto an object to generate a modulating image. The image capturing module is utilized to obtain a plurality of modulating images. The image processing module is utilized to analyze the modulating images to generate a high resolution image.