An image recognition apparatus includes an acquisition unit and a controller. The acquisition unit acquires an image which captures by photography a pattern indicative of an object. The controller is configured to specify a pattern area from a first image which the acquisition unit acquires, to recognize a pattern which the specified pattern area includes, to acquire a second image from the acquisition unit, to determine whether a disposition of the object of the first image and a disposition of the object of the second image coincide, and to specify a pattern area from the second image, if determining that the disposition of the object of the first image and the disposition of the object of the second image are non-coincident, and to recognize a pattern which the specified pattern area includes.

The current document is directed to a computationally efficient method and system for assessing the suitability of a text-containing digital image for various types of computational image processing, including optical-character recognition. A text-containing digital image is evaluated by the disclosed methods and systems for sharpness or, in other words, for the absence of, or low levels of, noise, optical blur, and other defects and deficiencies. The sharpness-evaluation process uses computationally efficient steps, including convolution operations with small kernels to generate contour images and intensity-based evaluation of pixels within contour images for sharpness and proximity to intensity edges in order to estimate the sharpness of a text-containing digital image for image-processing purposes.

A handwriting input apparatus and method are provided. The handwriting input apparatus includes: a touch screen configured to display a handwriting corresponding to a touch input; and a processor configured to generate a skeleton for the handwriting by performing text recognition with respect to the handwriting, correct a style of the handwriting based on the generated skeleton, apply a beautification effect to the corrected style of the handwriting, and control the touch screen to display the beautified handwriting.

A lane identification method for identifying a lane whereon a vehicle is traveling, which includes: providing a drive video; generating an identification result of recognizing a plurality of lane lines corresponding to the line according the drive video, wherein the identification result is generated based on inside borderlines of the lane lines; and determining the lane according to the identification result.

An example method is of image processing provided in according with one implementation of the present disclosure. The method includes receiving an image, placing a window across the image, and computing a set of all occurring grayscale values within the window. The method further includes computing a threshold value based on the set of all occurring grayscale values within the window and determining an output pixel value of at least one pixel from the window based on the threshold value.

A multimodal and real-time method for filtering sensitive content, receiving as input a digital video stream, the method including segmenting digital video into video fragments along the video timeline; extracting features containing significant information from the digital video input on sensitive media; reducing the semantic difference between each of the low-level video features, and the high-level sensitive concept; classifying the video fragments, generating a high-level label (positive or negative), with a confidence score for each fragment representation; performing high-level fusion to properly match the possible high-level labels and confidence scores for each fragment; and predicting the sensitive time by combining the labels of the fragments along the video timeline, indicating the moments when the content becomes sensitive.

Techniques are disclosed for generating logical sensors for an image driver. The image driver monitors values corresponding to at least a first feature in one or more regions of a first image in a stream of images received by a first sensor. The image driver identifies at least a first correlation between at least a first and second value of the monitored values. The image driver generates a logical sensor based on the identified correlations. The logical sensor samples one or more features corresponding to the identified correlation from a second image in the stream of images.

A method for performing registration plate detection includes: performing image processing on image data of a predetermined region in an image to generate an edge image; and performing registration plate detection according to the edge image and the image data of the predetermined region to determine a location of a registration plate image within the predetermined region, for performing a post-processing corresponding to the registration plate image. The step of performing the image processing includes: performing gradient calculations on image data of the predetermined region to generate a gradient image, where the gradient image includes gradient data of the predetermined region; performing edge threshold estimation on the gradient data of the predetermined region, to update an edge threshold through a set of iterative calculations; and generating the edge image according to the edge threshold and according to selective gradient data of the predetermined region.

A method for creating an overall image of an object from a plurality of recorded images that each depict a small area of the object, the method including: dividing the overall image into fields; assigning a part of the plurality of recorded images to each of the fields; and composing the plurality of recorded images into the overall image.

Methods and devices are provided for classifying pictures. In the method, the device obtains a picture to be classified. The device determines a class of the picture according to feature information of the picture, in which the class includes a text picture class and a non-text picture class. The device classifies the picture according to the class of each picture.