849 results about "Region of background" patented technology

The invention relates to an intelligent assisted driving method and system. A lane line is obtained as follows: an image, shot by a camera, of a road in a vehicle driving direction is obtained; de-distortion processing is carried out on a video frame of the image by using an internal reference matrix of the camera and inverse perspective transformation is carried out on a selected to-be-transformed region src of the video frame; threshold segmentation is carried out on the video frame after inverse perspective transformation and a lane line and a background region are separated; statistics iscarried out on the lower part of the image after threshold segmentation and an intersection point of the lane line and the bottom of the image is obtained by statistics; searching is carried out by starting with the intersection point by using a sliding window, m pixels corresponding to the lane line are determined, and curve equation fitting is carried out on the m pixels to obtain a fitted laneline. With the method and system provided by the invention, the lane line is displayed in a display device inside the vehicle; and various conditions like sudden illumination changing, tree shadow shielding, and pavement blotting are detected accurately. The method and system have advantages of high applicability, low cost, high precision, good real-time performance and high stability.

The sharpness of a digital image is adjusted according to defined aim subject and background sharpness levels. An image segmentation process is used to segment an input digital image into a subject region and a background region. The subject and background regions are analyzed to determine corresponding subject and background sharpness levels. An enhanced digital image is formed wherein the sharpness of the subject region is adjusted responsive to the subject sharpness level and the aim subject sharpness level, and the sharpness of the background region is adjusted responsive to the background sharpness level and the aim background sharpness level. In some embodiments, the input digital image is analyzed to determined a scene type classification and the aim subject and background sharpness levels are defined in accordance with the determined scene type classification.

An algorithm for estimating which pixels belong to the background of a scanned page. The algorithm is particularly designed to handle situations in which the page background varies in color / intensity, as is case when bleed-through artifacts from the reverse side of the page appear in the background. In determining background regions, including properly classifying bleed-through artifacts regions as such, the algorithm uses multiple local and global criteria for making the determination. In addition to being able to find large connected pieces of background, the algorithm is also able to find isolated islands of background by analyzing transition characteristics of neighboring regions. Regions are identified on the basis of similar local features and also by the nature of transitions between foreground regions that do not directly share a boundary. An adaptive white-point adjustment technique based on identified background regions improves the perceived quality of the printed output.

A blur classification module may compute the probability that a given pixel in a digital image was blurred using a given two-dimensional blur kernel, and may store the computed probability in a blur classification probability matrix that stores probability values for all combinations of image pixels and the blur kernels in a set of likely blur kernels. Computing these probabilities may include computing a frequency power spectrum for windows into the digital image and / or for the likely blur kernels. The blur classification module may generate a coherent mapping between pixels of the digital image and respective blur states, or may perform a segmentation of the image into blurry and sharp regions, dependent on values stored in the matrix. Input image data may be pre-processed. Blur classification results may be employed in image editing operations to automatically target image subjects or background regions, or to estimate the depth of image elements.

An image processing apparatus may detect an occlusion boundary between objects within an input depth image by applying an edge detection algorithm to the input depth image. The image processing apparatus may classify the occlusion boundary into a foreground region boundary and a background region boundary using a depth gradient vector direction of the occlusion boundary, and may extract an occlusion region of the input depth image using the foreground region boundary.

An aerial photography transport video vehicle rapid detection method comprises the steps that: step 100, the space-based coding part adopts the global motion estimation to determine the global motion vector of a background; step 200, the residual difference value is calculated according to the global motion vector to divide background regions and motion regions; step 300, whether the regions are the background regions or not is judged, an image of which the regions are all the background regions is changed to the next frame, and the step 200 is executed; otherwise, the motion regions of the image execute the step 400; step 400, the ground part firstly determines a self-adaptive gradient threshold which is lower and can correctly divide various objects to carry out the primary marker extraction; then two parameters of an area and a water collection depth are introduced for further screening of the extracted marker, thus determining a final marker point; then the marker point is taken as the region minimum value for carrying out the VS watershed division; finally, the regions are merged according to the texture information of the regions; step 500, the shadow is detected in an HSV color space, thus filtering out phony targets and finally detecting vehicles. The method of the invention solves the problems that the calculation amount during the decompression of the aerial photography image and the detection of motion targets is greater, and the real-time property and the robustness requirement are difficult to be satisfied at the same time.

The invention discloses a scene text recognition method based on sparse coding characteristics, and relates to computer vision and pattern recognition. The method includes the steps: inputting a natural scene text image to be recognized; by the aid of a multi-scale sliding window method, detecting and recognizing a window area in the image by a character classifier, for each character class, determining a large output area of the classifier as a candidate character area, determining a small output area as a background area, finding the candidate character area in the image, retaining the area with the largest output value of the classifier and the corresponding character class for the area with a large overlapping ratio by the aid of a non-maximum suppression method, and removing the repetitive and redundant candidate character area to obtain a character detection result; combining detected characters into a word or text line; outputting a scene text recognition result. Structural characteristics of the characters can be more effectively expressed and extracted, so that the recognition rate of a scene text is increased.

The invention relates to a kilowatt-hour meter image automatic identification method which comprises the following steps: 1. image preprocessing: detecting vertical texture of a panel image by using Sobel operator, preliminarily removing the background area by a projection method, extracting the area with abundant vertical texture by an expansion method, and carrying out binarization treatment on the image by a adaptive threshold segmentation method based on an integral projection method; 2. precise positioning of indicating value and bar code: by combining an intelligent judgment method on the basis of indicating value intervals and length-width ratio characteristic of numeric characters under the complex image background, adapting to precise positioning of indicating values of different types of kilowatt-hour meters on the basis of vertical edge detection of the Sobel operator and morphological treatment; carrying out horizontal scanning on the bar code area to extract the bar code characteristic area; 3. bar code identification: identifying different character bar codes by using a similar edge distance normalization method; and 4. indicating value identification: extracting the indicating value by a PCA (principal component analysis) method. By using the PCA character recognition method, various character indicating values can be precisely identified, including identification of half-character.

A fast splitting method for image includes the following steps: a) splitting the image into three parts of foreground area, unknown area and background area; b) calculating colour component F and B for foreground and background by setting any point C in unknown area as colour of according to the split areas; and c) adjusting colour value for F and B according to the estimated colour component of foreground and back ground, and finally to estimate alpha value of c point.

A method for automatically determining the region of interest from an image comprises the three steps of (a) analyzing content of the transformed coefficients of an image after a discrete signal transformation and partitioning an image into the interested region and background region that are based on p×p-pixel sub-blocks for performing classification; (b) locating the central point of the interested sub-blocks; and (c) integrating a plurality of the interested sub-blocks from said central point to the boundary of an image to generate a closed and continued region of interest by using a image processing technique with considering the bit-rate requirement.