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 memory device and a programming method thereof are provided. The programming method includes the following steps. According to a step value, based on an incremental step pulse programming scheme, multiple programming operations are performed for a selected memory page. In a setting mode, multiple program verify operations are respectively performed corresponding to the programming operations to respectively generate multiple pass bit numbers. In the setting mode, a pass bit number difference value of two pass bit numbers corresponding to two programming operations is calculated. In the setting mode, an amount of the step value is adjusted according to the pass bit number difference value.

Abstract: A method for training a deep learning network for face recognition includes: utilizing a face landmark detector to perform face alignment processing on at least one captured image, thereby outputting at least one aligned image; inputting the at least one aligned image to a teacher model to obtain a first output vector; inputting the at least one captured image a student model corresponding to the teacher module to obtain a second output vector; and adjusting parameter settings of the student model according to the first output vector and the second output vector.

Abstract: A method for image compression and a circuit system thereof are provided. In the method, pixel values of an image are obtained. A compression scenario is decided, for example, a uniform-quantization manner or a non-uniform-quantization manner is used for an M-bit image being compressed to an N-bit image so as to decide codeword sections for the image. Every codeword section has a codeword distance. The codeword sections have a fixed codeword distance in the uniform-quantization manner. Alternatively, in the non-uniform-quantization manner, the image can be divided into multiple codeword sections having different codeword distances according to a brightness distribution. Afterwards, a random number is generated for deciding codeword and index for original value of each of the pixels. An index table is accordingly formed. The index table is provided for obtaining the codeword in a decoding process by querying a codebook with the index.

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: The present invention provides a compression method, wherein the compression method includes the steps of: setting a quantization function; setting a plurality of scaling ratios; receiving image data; for a block of a frame of the image data, using a conversion matrix to convert data of the block to generate a plurality of converted data; determining a specific scaling ratio from the plurality of scaling ratios according to the plurality of converted data; and using the quantization function to perform a quantization operation on a plurality of adjusted data to generate compressed data, wherein the plurality of adjusted data are generated according to the plurality of converted data and the specific scaling ratio.

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: 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 and a system for image correction are provided. The system includes an image processor that performs the method for image correction when receiving an input image. In the method, an overexposed area within the image is determined. The channel values of pixels in the overexposed area are applied with corresponding weights. An automatic white balance process is performed on the channel values of pixels in the overexposed area according to an expectation of low saturation in the overexposed area. Besides a first channel value of each of the pixels, a second channel value and a third channel value of the pixel can be estimated according to the channel values of periphery pixels. A correction value overexposed of the first channel value of the current pixel can be calculated according to the corresponding weights of the pixels in the overexposed area.

Abstract: A circuitry for image demosaicing and contrast enhancement and an image-processing method thereof are provided. The circuitry includes a storage device that is used to temporarily store an image and is jointly used by circuits that perform color restoration and brightness reconstruction. The circuitry includes a color restoration circuit for performing image interpolation and a global mapping circuit that performs mapping to obtain brightness of an image according to restored red, green and blue information of every pixel. Further, an edge texture feature decision circuit is provided to obtain each pixel's directionality for color restoration. A brightness estimation circuit utilizes green information of the pixels as the brightness for an area. After that, a color image with the color restoration and brightness reconstruction is outputted.

Abstract: The present invention discloses an image data augmentation method that includes the steps outlined below. At least one distortion operation function is retrieved. A plurality of pixels included in the image are twisted according to the distortion operation function to generate at least one augmented image. Object information of each of at least one object included in the image is converted according to the distortion operation function to generate object information conversion result. The augmented image, a class tag of each of the at least one object and the object information conversion result are fed to a machine learning module to generate a machine learning result.

Abstract: A method for determining deblur filter coefficients includes the following steps: generating an edge profile according to the data of a blurred image; estimating a blur kernel according to the edge profile, wherein the blur kernel indicates how an imaging process blurs original image data and thereby generates blurred image data; and determining coefficients of a deblur filter to make a process result of the deblur filter processing the blurred image data approach the original image data.

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: An object detection model training apparatus, method and non-transitory computer readable storage medium thereof are provided. The apparatus performs a first object detection on a plurality of training images to generate a piece of first label information corresponding to each of the training images by a first teacher model. The apparatus trains a student model based on the training images and the first label information. The apparatus performs a second object detection on the training images to generate a piece of second label information corresponding to each of the training images by a second teacher model. The apparatus trains the student model based on the training images and the second label information.

Abstract: A method for training a deep learning network for face recognition includes: utilizing a face landmark detector to perform face alignment processing on at least one captured image, thereby outputting at least one aligned image; inputting the at least one aligned image to a teacher model to obtain a first output vector; inputting the at least one captured image a student model corresponding to the teacher module to obtain a second output vector; and adjusting parameter settings of the student model according to the first output vector and the second output vector.

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: A pixel value calibration method includes: obtaining input image data generated by pixels, the input image data including a first group of pixel values in a first color plane and a second group of pixel values in a second color plane, generated by a first portion and a second portion of the pixels respectively; determining a difference function associated with filter response values and target values, the filter response values being generated by utilizing characteristic filter coefficients to filter first and second estimated pixel values of estimated pixel data in the first and second color planes, respectively; determining a set of calibration filter coefficients by calculating a solution of the estimated pixel data, the solution resulting in a minimum value of the difference function; and filtering the input image data, by a filter circuit using the set of calibration filter coefficients, to calibrate the first group of pixel values.

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.