A method of processing an image of a road having a road marker acquired by a vehicle-mounted camera to generate boundary data indicating a boundary of the road marker region of the image which represents the road marker, comprising: generating an LL sub-band image of an M.sup.th level of an (M+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image M times; generating a sub-band image of an (M+1).sup.th level of the (M+1) level DWT decomposition by high-pass filtering the LL sub-band image and down-sampling a result of the high-pass filtering; and determining a boundary of a region of pixels of the sub-band image of the (M+1).sup.th level, the region being surrounded by pixels having pixel values substantially different to the pixel values of the pixels in the region, the determined boundary indicating the boundary of the road marker region.

An apparatus for determining a speed of a vehicle along a road by processing a first image and a second image of the road captured by a camera on the vehicle and comprising respective road marker images of a road marker, the apparatus arranged to: determine a location of the road marker in the first image; predict a location of the road marker in the second image based on the determined location, an estimate of the vehicle speed, and a time period between capture of the images; detect the road marker in a portion of the second image at the predicted location; estimate a distance moved by the vehicle during the time period based on the determined location, and a location of the detected road marker in the portion of the second image; and calculate the speed based on the estimated distance and the time period.

A keyboard file verification method based on image processing comprises controlling a processor to perform following operations: obtaining a keyboard file; generating, according to the keyboard file, a search index and a feature image; obtaining a template image from a template database according to the search index; performing a calibration operation according to the feature image, wherein the calibration operation comprises: adjusting a resolution of the feature image according to a resolution of the template image; performing a shifting operation according to the feature image, to generate a plurality of candidate images; and comparing a key block of each of the plurality of candidate images with a key block of the template image to generate a difference map and a comparison result.

Systems and methods for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance are disclosed.

Disclosed is a Gabor wavelet-fused multi-scale local level set ultrasonic image segmentation method. In the method, non-uniformity of the grayscale of an ultrasonic image is taken as a texture having cluttered directions, the multi-directional property of Gabor wavelets is used to process the image, and intermediate images in different filtering directions are fused by taking maximum values, so as to obtain an intermediate image having a weakened texture effect and an enhanced difference between a foreground and a background. For the feature of a weak edge of an ultrasonic image, a concept of multi-scale is used to improve the conventional LIC method, Gaussian convolution kernels having different variances are set, and a final edge is obtained by means of average fusion.

Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.

A monitoring system implements a method for versatile and efficient detection and grading of deviations in packaging containers for liquid food in a manufacturing plant. The method comprises obtaining image data of a packaging container, or a starting material for use in producing the packaging container; analyzing the image data for detection of a current deviation; processing the current deviation in relation to a set of basis functions, which is associated with a deviation type of the current deviation, to obtain a current set of weights that represent the current deviation; and determining a current grading of the current deviation based on the current set of weights. The set of basis function may be pre-computed based on reproductions of packaging containers or starting material comprising different magnitudes of the deviation type.

Image denoising includes obtaining a saliency map for an image. The saliency map includes respective saliency scores for pixels of the image. Respective noise levels are assigned to the pixels using the respective saliency scores to obtain a noise level map. The image is denoised using the noise level map to obtain a denoised image. The denoised image is output, such as to a display or a storage device.

The present invention relates to a method of providing diagnostic information for brain diseases classification, which can classify brain diseases in an improved and automated manner through magnetic resonance image pre-processing, steps of contourlet transform, steps of feature extraction and selection, and steps of cross-validation. The present invention relates to a diagnostic information providing method capable of providing an optimal diagnostic means. The present invention relates to a method for providing diagnostic information for brain diseases classification, and relates to a method for providing an optimal diagnostic means for classifying brain diseases in an improved and automated manner through the steps of the magnetic resonance imaging pre-processing, contourlet transform, feature extraction and selection, and cross-validation.

A single image deraining method is proposed. A wavelet transforming step processes an initial rain image to generate an i-th stage low-frequency rain image and a plurality of i-th stage high-frequency rain images. An image deraining step inputs the i-th stage low-frequency rain image to a low-frequency deraining model to output an i-th stage low-frequency derain image. A first inverse wavelet transforming step recombines the n-th stage low-frequency derain image with the n-th stage high-frequency derain images to form an n-th stage derain image. A weighted blending step blends a (n−1)-th stage low-frequency derain image with the n-th stage derain image to generate a (n−1)-th stage blended derain image. A second inverse wavelet transforming step recombines the (n−1)-th stage high-frequency derain images with the (n−1)-th stage blended derain image to form a (n−1)-th stage derain image, and sets n to n−1 and repeats the last two steps.