Realtek Semiconductor Inc.

Chun-Chang Wu

Shih-Tse Chen

Chao-Hsun Yang

Shang-Lun Chan

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.

Method of data augmentation and non-transitory computer readable media

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.

Object detection model training apparatus, method, and non-transitory computer readable storage medium thereof

An image data augmentation device includes a memory and a processor. The memory is configured to store multiple commands and multiple images; the processor is connected to the memory to read the multiple images and execute multiple commands to: identify at least one object-related image including at least one object from the multiple images; capture at least one object image from the at least one object related image; and superimpose the at least one object image on multiple arbitrary positions in the multiple images to generate multiple training sample images, and using the multiple training sample images to execute machine learning. In addition, an image data augmentation method is also disclosed here.

Image data augmentation device and method

A method for processing image using fully connected convolutional neural network and a circuit system are provided. The method is operated using fully connected convolutional neural network (CNN) and performed by the circuit system. In the method, an image with a length, a width and an aspect ratio is obtained. A reference image closest to the input image can be obtained by querying a lookup table that records multiple reference images with various sizes to be adapted to the fully connected CNN. The input image can be resized as the closest reference image. A convolution operation is then performed onto the resized image, and a feature cube is formed after multiple operations of convolution. The feature cube is transformed to one-dimensional feature values that are configured to be inputted to a fully connected layer for fully connected operation. An output value of the fully connected CNN is generated.

Method for processing image using fully connected convolutional neural network and circuit system

A calculation method using pixel-channel shuffle convolutional neural network is provided. In the method, an operating system receives original input data. The original input data is pre-processed by a pixel shuffle process to be separated into multiple groups in order to minimize dimension of the data. The multiple groups of data are then processed by a channel shuffle process so as to form multiple groups of new input data selected for convolution operation. The unselected data are abandoned. Therefore, the dimension of the input data can be much effectively minimized. A multiplier-accumulator of the operating system is used to execute convolution operation using a convolution kernel and the multiple new groups of input data. Multiple output data are then produced.

Calculation method using pixel-channel shuffle convolutional neural network and operating system using the same

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.

Motion image integration method and motion image integration system capable of merging motion object images

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.

Motion detection method and motion detection system with low computational complexity and high detection accuracy

An image processing circuit includes a receiving circuit, a feature fetching module and a decision circuit. In the operations of the image processing circuit, the receiving circuit is configured to receive image data. The feature fetching module is configured to use a multi-topological-convolutional network to fetch the features of the image data, to generate a plurality of image features determined by the characteristics and weights of the convolution filter, where the image features may be smooth features or edge features. In the present invention, the convolution filters used by the feature fetching module are not limited by a square convention filter, and the convolution filters may include the multiple topological convolutional network having non-square convolution filters. By using the multiple topological convolutional network of the present invention, the feature fetching module can fetch the rich image features for identifying the contents of the image data.

Growing community of inventors

Taichung, Taiwan

Chun-Chang Wu