6 results about "Edge detection" patented technology

The invention discloses an obstacle feature extraction method based on a millimeter wave radar and a laser radar. The method includes the following steps that: step 1, radar raw data are acquired; step 2, the radar original data are solved according to a radar protocol, so that obstacle position information can be obtained; step 3, the solved millimeter wave radar data are screened; step 4, time and space synchronization is performed on the millimeter wave radar and the laser radar; step 5, regions of interest (ROI) are established in a laser radar coordinate system according to millimeter wave radar information and are denoted as Ri, wherein i is equal to 1, 2, ... n, wherein n is the number of the effective targets of the millimeter wave radar; step 6, the point cloud data of the i-th ROI are processed according to a scattered point cloud data simplification processing method; and step 7, a two-dimensional image is generated on the basis of the point cloud data, edge detection is performed on the image, and obstacle features are extracted. With the method adopted, obstacle edge information can be obtained, and therefore, more references can be provided for a driverless decision-making layer.

The invention discloses an image processing method, an image processing device, an image processing hardware device and a computer readable storage medium. The image processing method comprises the steps of obtaining a brightness image according to a brightness component corresponding to an original image; performing edge detection processing on the brightness image to obtain an edge image; acquiring a tone image of the brightness image according to a predetermined reference image; and generating a pencil drawing style image corresponding to the original image according to the edge image and the tone image. According to the embodiment of the invention, the brightness image is obtained according to the brightness component corresponding to the original image; edge detection processing is performed on the brightness image to obtain an edge image, a tone image of the brightness image is obtained according to a predetermined reference image, and a pencil drawing style image is generated according to the edge image and the tone image, thereby solving the technical problem that a pencil drawing style filter in the prior art cannot give consideration to the effect and the performance at the same time.

The invention discloses a measurement method of a coal dust and in-furnace heat flow mixing effect on the basis of a combustion image, and belongs to the technical field of thermotechnical measurement and image processing. The method comprises the following steps: firstly, carrying out edge detection on an unburned coal powder area in the combination image through an area first order difference algorithm with a fixed search direction, determining the mixing characteristic area of the coal dust and in-furnace heat flow, and carrying out block processing and gray level stage processing on the area. In the characteristic area, a gray parameter and a gray position distribution parameter which reflect a coal dust and in-furnace heat flow mixing situation are extracted, the gray parameter and the gray position distribution parameter construct a mixing coefficient which measurably evaluates the coal dust and in-furnace heat flow mixing effect, and therefore, the measurement of the coal dust and in-furnace heat flow mixing effect can be realized.

The invention provides a plant leaf image extraction method and device. The method is characterized by carrying out threshold processing on subimages on red/green/blue color channels and obtaining binary images; and after obtaining the binary images, carrying out fitting on pixel points in the binary images and extracting target plant leaves. According to the technical scheme, interference of field complex environment can be overcome, and the target plant leaves can be segmented accurately and intactly; the method not only can improve leaf edge detection precision and reduce leaf identification error probability, but also can be applied to mobile terminals, such as a smart phone; the method is suitable for field operation; and the application range of the method is broadened.