A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.

An image processing method, apparatus, and electronic device relate to the technical field of image processing. In the method, an initial image is acquired; and then a connected region in the initial image is acquired, where the connected region is a region composed of pixels having pixel values within a target threshold range in the initial image. In addition, a region of interest in the initial image is acquired according to the connected region, and the region of interest is preprocessed to obtain a target image. By extracting the region of interest in the initial image, and only preprocessing the region of interest, the resource expenditure during image preprocessing can be reduced, and the preprocessing efficiency can be improved.

One embodiment can provide a system for authenticating an object. During operation, the system configures an environment surrounding the object according to one or more target environmental factors, captures at least a first image of the object while the object is exposed to the target environmental factors, and determines Fan authenticity of the object based on the captured first image and the target environmental factors.

A sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment. The pre-processing functions can adjust for different calibrations and formats across the plurality of cameras.

A text extraction computing method that comprises calculating an estimated character pixel height of text from a digital image. The method may scale the digital image using the estimated character pixel height and a preferred character pixel height. The method may binarizes the digital image. The method may remove distortions using a neural network trained by a cycle GAN on a set of source text images and a set of clean text images. The set of source text images and clean text images are unpaired. The source text images may be distorted images of text. Calculating the estimated character pixel height may include summarizing the rows of pixels into a horizontal projection, and determining a line-repetition period from the projection, and quantifying the portion of the line-repetition period that corresponds to the text as the estimated character pixel height. The method may extract characters from the digital image using OCR.

The disclosure provides a three-dimensional (3D) image classification method and apparatus, a device, and a storage medium. The method includes: obtaining a 3D image, the 3D image including first-dimensional image information, second-dimensional image information, and third-dimensional image information; extracting a first image feature corresponding to planar image information from the 3D image; extracting a second image feature corresponding to the third-dimensional image information from the 3D image; fusing the first image feature and the second image feature, to obtain a fused image feature corresponding to the 3D image; and determining a classification result corresponding to the 3D image according to the fused image feature corresponding to the 3D image.

The present disclosure relates to an unmanned lane keeping method and device, a computer device, and a storage medium. The method includes that: a vehicle road image collected by a data collector of the vehicle is received; the vehicle road image is transmitted to a preset DNN model of the vehicle for processing to obtain a steering wheel angle corresponding to the vehicle road image, wherein the DNN model of the vehicle is established by deep learning, and is used for characterizing a correspondence between the vehicle road image and the steering wheel angle; and the vehicle is controlled to keep driving in a corresponding lane according to the steering wheel angle.

A LED controller includes a power distribution module and an interface to an external data bus. An image frame buffer is connected to the interface to receive image data. A separate logic module is connected to the interface and configured to modify image frame buffer output signals sent to an LED pixel array connected to the image frame buffer. The LED pixel array can project light according to a pattern and intensity defined at least in part by the image held in the image frame buffer.

A method, system and computer program product are configured to analyze an image of a vehicle to determine a characteristic of the vehicle, such as may be represented by or otherwise at least partially defined by the shadow cast by the vehicle. In the context of a method, information is received identifying a vehicle from a raster image and the pixel values of the raster image are evaluated to identify pixels having pixel values representative of a shadow associated with the vehicle. The method also modifies a representation of the shadow by modifying the pixel values of the pixels based upon a shape of the vehicle such that the representation of the shadow, as modified, has a shape corresponding to the shape of the vehicle. The method additionally determines a characteristic of the vehicle based upon the representation of the shadow, as modified, that is associated with the vehicle.

This invention provides an image searching method based on feature extraction to determine at least one sample image similar to the image to be searched, including the following steps: a. feature extraction on all sample images to obtain all corresponding feature information, while all sample images are I.sub.1, I.sub.2, . . . , I.sub.S and all said feature information corresponding to all sample images are V.sub.1, V.sub.2, . . . , V.sub.S; b. creating an index tree based on all of said feature information; c. obtaining the feature information corresponding to the image to be searched based on the feature extraction; d. determining said feature information corresponding to at least one sample image having a minimum distance from the image to be searched based on the index tree and the feature information of the image to be searched. The invention has powerful functions and simple operation, and is able to avoid interference of background, text annotation and image merging during processing and filtering of feature extraction, which has good search results and high commercial value.