39 results about "Texture gradient" patented technology

A decorative covering for an internal trim component for a vehicle comprises an outer surface that includes a gradient characteristic when referenced to an adjacent trim component. The gradient characteristic may comprise a plurality of texture gradient zones and / or a plurality of color gradients. A method of manufacturing the decorative covering and the internal trim component with the decorative covering is also disclosed.

A method for quantifying the texture homogeneity of a polycrystalline material is described. The method involves selecting a reference pole orientation; scanning in increments a cross-section of the polycrystalline material having a thickness with scanning orientation imaging microscopy or other measuring technique to obtain actual pole orientations of a multiplicity of grains throughout the cross-section of the polycrystalline material. The orientation differences between the reference pole orientation and actual pole orientations of a multiplicity of grains is then determined. A value of misorientation from the reference pole orientation at each grain measured throughout the thickness is then assigned. The average misorientation of each measured increment throughout the thickness is then determined. A texture gradient and / or texture banding can then be obtained by determining the first and / or second derivative, respectively, of the average misorientation of each measured increment through the thickness of the sample used for evaluation. A method to predict the sputtering efficiency of a target is also described as well as a system for quantifying the texture homogeneity of a polycrystalline material.

The invention relates to an infrared weak small target detection method based on space-time characteristics, which comprises the following steps of: obtaining a high-frequency component filter diagramof a target infrared image, and determining a plurality of suspicious targets by the high-frequency component filter diagram; calculating neighborhood energy space distribution characteristics of each suspicious target by sequentially utilizing gray scale distribution nine-grid cells; obtaining a suspicious target, calculating a texture gradient distribution characteristic, judging according to an azimuth pitching scale ratio and target saturation of the suspicious target, finally judging a target time domain distribution characteristic, gradually eliminating a pseudo target in the suspicioustarget, and finally determining a real target. According to the method, cloud layer interference, ground object interference, detector noise interference and the like in a weak small target background can be effectively eliminated, so that a real target is reliably determined, the false alarm rate of target detection can be greatly reduced, and the detection probability is improved.

The invention discloses a method for converting a low-resolution image into a high-resolution image. The method comprises the steps of carrying out concentrated sample matching according to a given input low-resolution image Il<t> and according to similarity measure to obtain an image block set and taking out the best image block with the use of the prior knowledge of texture to generate a texture gradient function Ut (p). According to the method, Il<t> is interpolated to generate an image Is in the direction of a retained image, an edge gradient function Ue (p) is generated with the use of a learned edge function ft (vl), and Ut (p) and Ue (p) are weighted with the use of a weighting function W(p) to generate a gradient function U, thereby obtaining a high-resolution image Ih<t> amplified by S times. The method for converting a low-resolution image into a high-resolution image has the characteristic that a reconstructed high-resolution image is high in quality and is more real.

The invention relates to a bionic compound eye microlens technology-based 3-3-2 dimension object detection method and system. Through a bionic compound eye microlens system-based low-resolution data acquisition method, an object region is subjected to capture imaging, according to microlens array images taken by two microlens devices, constructing a low-resolution image by a linear weighted average method, reconstructing a 3D profile of an object by a forward intersection measurement method, if the object image is effectively captured and is shown in the low-resolution image, reconstructing a high-resolution image of the object region by the microlens array images as base data by a regularization method, and after the high-resolution 2D image of the object region is acquired, carrying out precise recognition on the object by a texture gradient-based GAC model. Through addition of a process for acquiring the 3D profile of an object by a low-resolution image, redundant image unmeaning processing is effectively avoided and real-time processing efficiency and accuracy of the system are improved.

The invention relates to a method for adaptive texture gradient prediction in bandwidth compression, which includes selecting N sampling methods to sample pixel components in the current MB; selecting M prediction methods to predict the current MB; and calculating the current MB respectively. The prediction residual and SAD; select the sampling mode and prediction mode of the current MB according to the SAD. The present invention calculates the prediction residual and SAD of the current prediction macroblock by defining the sampling mode of the MB. Compared with the existing methods, when the texture of the image to be compressed is more complex, for the MB at the texture boundary of the current image, according to the texture gradient principle, it does not depend on the surrounding MB of the current MB, but through the current MB The prediction residual obtained by its own texture characteristics can improve the accuracy of the prediction residual value for complex texture areas, further reduce the theoretical limit entropy, and increase the bandwidth compression rate.