Abstract: A judgment system, an electronic system, a judgment method, and a display method are provided. The judgment method includes: receiving an image by a feature acquisition module and obtaining a first key point coordinate, a second key point coordinate, and a size of a face box of a user by the feature acquisition module based on the image; and performing following steps by a judgment module: obtaining a judgment value based on an ordinate of the first key point coordinate, an ordinate of the second key point coordinate, and a size of the face box; and sending a rotation signal in response to that the judgment value satisfies a rotation condition.

Abstract: A method used for object tracking includes: using a specific object model to generate a first vector of a first ratio object and a second vector of a second ratio object of an image in an object detection bounding box of a specific frame; generating an identity label of an object within the bounding box according to the first vector, the second vector, and M first ratio reference vectors and M second ratio reference vectors stored in an object vector database.

Abstract: A method of performing person re-identification includes: obtaining a person feature vector according to an extracted image containing a person; obtaining state information of the person according to a state of the person in the extracted image; comparing the person feature vector with a plurality of registered person feature vectors in a database; when the person feature vector successfully matches a first registered person feature vector of the plurality of registered person feature vectors, identifying the person as a first identity corresponding to the first registered person feature vector; and selectively utilizing the person feature vector to update one of the first registered person feature vector and at least one second registered person feature vector that correspond to the first identity according to the state information.

Abstract: A detection system and a detection method are provided. The detection method includes: receiving an image containing a face by an angle acquisition module and obtaining a first angle, a second angle and a third angle of the face based on the image; obtaining a first projection value and a second projection value based on the first angle, the second angle and the third angle by a projection calculation module; and performing by a confidence calculation module: performing an exponentiation calculation on the first projection value based on a first correction value to obtain a third value; performing an exponentiation calculation on the second projection value based on a second correction value to obtain a fourth value; and obtaining a confidence value based on the third value and the fourth value.

Abstract: A character recognition method includes the following operations: determining that the image of character to be identified corresponds to a matching character of several registered characters according to several vector distances to be identified between a vector of an image of character to be identified and several vectors of several registered character images of several registered characters, and storing a matching vector distance between the vector of the image of character to be identified and a vector of the matching character by a processor; and storing a data of the matching character according to the image of character to be identified when the matching vector distance is less than a vector distance threshold by the processor.

Abstract: The present invention provides a processing circuit applied to a face recognition system, which includes a characteristic value calculation module, a determination circuit and a threshold value calculation module.

Abstract: An image recognition system includes a color conversion module and a target recognition module. The color conversion module is configured to convert a gray-level image into a preset color image according to a conversion function. The target recognition module includes a machine learning algorithm, and the machine learning algorithm includes a plurality of functions and a plurality of parameters. The machine learning algorithm receives the preset color image, and outputs a recognition result according to the functions and the parameters, the recognition result including an existent target or a null target.

Abstract: A method used for object tracking includes: using a specific object model to generate a first vector of a first ratio object and a second vector of a second ratio object of an image in an object detection bounding box of a specific frame; generating an identity label of an object within the bounding box according to the first vector, the second vector, and M first ratio reference vectors and M second ratio reference vectors stored in an object vector database.

Abstract: A method of performing person re-identification includes: obtaining a person feature vector according to an extracted image containing a person; obtaining state information of the person according to a state of the person in the extracted image; comparing the person feature vector with a plurality of registered person feature vectors in a database; when the person feature vector successfully matches a first registered person feature vector of the plurality of registered person feature vectors, identifying the person as a first identity corresponding to the first registered person feature vector; and selectively utilizing the person feature vector to update one of the first registered person feature vector and at least one second registered person feature vector that correspond to the first identity according to the state information.

Abstract: A character recognition method includes the following operations: determining that the image of character to be identified corresponds to a matching character of several registered characters according to several vector distances to be identified between a vector of an image of character to be identified and several vectors of several registered character images of several registered characters, and storing a matching vector distance between the vector of the image of character to be identified and a vector of the matching character by a processor; and storing a data of the matching character according to the image of character to be identified when the matching vector distance is less than a vector distance threshold by the processor.

Abstract: A method of data augmentation is provided. The method includes the following operations: selecting an original image from an original dataset including label data configured to indicate a labeled area of the original image; selecting at least part of content, located in the labeled area, of the original image as a first target image; generating a first sample image according to the first target image, in which the first sample image includes the first target image and a first border pattern different from the first target image, and the content, located in the labeled area, of the original image is free from including at least part of the first border pattern; and incorporating the first sample image into a sample dataset, in which the sample dataset is configured to be inputted to a machine learning model.

Abstract: An object detection method and an associated electronic device are provided, wherein the object detection method includes: utilizing an image processing circuit to determine whether motion occurs in an image to generate a determination result; selectively utilizing a specific bounding box to identify a target object to generate an identification result according to the determination result, wherein the specific bounding box represents a location of the target object in a previous image; and selectively updating information of the specific bounding box according to the identification result.

Abstract: The present invention provides a processing circuit applied to a face recognition system, which includes a characteristic value calculation module, a determination circuit and a threshold value calculation module.

Abstract: A motion image integration method includes acquiring a raw image, detecting a first motion region image and a second motion region image by using a motion detector according to the raw image, merging the first motion region image with the second motion region image for generating a motion object image according to a relative position between the first motion region image and the second motion region image, and cropping the raw image to generate a sub-image corresponding to the motion object image according to the motion object image. A range of the motion object image is greater than or equal to a total range of the first motion region image and the second motion region image. Shapes of the first motion region image, the second motion region image, and the motion object image are polygonal shapes.

Abstract: A motion detection method includes acquiring a raw image, detecting a motion object image according to the raw image by using a motion detector, cropping the raw image to generate a sub-image according to the motion object image, and inputting the sub-image to a processor for determining if a motion object of the sub-image matches with a detection category. The processor includes a neural network. The shape of the sub-image is a polygonal shape.

Abstract: An object detection method and an associated electronic device are provided, wherein the object detection method includes: utilizing an image processing circuit to determine whether motion occurs in an image to generate a determination result; selectively utilizing a specific bounding box to identify a target object to generate an identification result according to the determination result, wherein the specific bounding box represents a location of the target object in a previous image; and selectively updating information of the specific bounding box according to the identification result.

Abstract: A method of updating background model for an image processing device is disclosed. The method includes receiving at least a background image, counting a number of occurrences of a color of each pixel of the background image according to a color information of each pixel of the at least a background image, to establish a background model including a statistical color information about the color and the number of occurrences of the color, receiving a current image, and determining whether to update the number of occurrences of the color in the background model corresponding to a color of each pixel of the current image according to a color information of each pixel of the current image and a result of comparing a random number with a threshold.

Abstract: A motion image integration method includes acquiring a raw image, detecting a first motion region image and a second motion region image by using a motion detector according to the raw image, merging the first motion region image with the second motion region image for generating a motion object image according to a relative position between the first motion region image and the second motion region image, and cropping the raw image to generate a sub-image corresponding to the motion object image according to the motion object image. A range of the motion object image is greater than or equal to a total range of the first motion region image and the second motion region image. Shapes of the first motion region image, the second motion region image, and the motion object image are polygonal shapes.

Abstract: A motion detection method includes acquiring a raw image, detecting a motion object image according to the raw image by using a motion detector, cropping the raw image to generate a sub-image according to the motion object image, and inputting the sub-image to a processor for determining if a motion object of the sub-image matches with a detection category. The processor includes a neural network. The shape of the sub-image is a polygonal shape.

Abstract: A method of updating background model for an image processing device is disclosed. The method includes receiving at least a background image, counting a number of occurrences of a color of each pixel of the background image according to a color information of each pixel of the at least a background image, to establish a background model including a statistical color information about the color and the number of occurrences of the color, receiving a current image, and determining whether to update the number of occurrences of the color in the background model corresponding to a color of each pixel of the current image according to a color information of each pixel of the current image and a result of comparing a random number with a threshold.