Image conversion

About: Image conversion is a research topic. Over the lifetime, 2490 publications have been published within this topic receiving 19077 citations.

Image correction device and image correction method

Abstract: PROBLEM TO BE SOLVED: To overcome the problem of a conventional image correction device that has had difficulty in keeping a color characteristic of an object constant when a photographing condition is changed. SOLUTION: An image input means 1 photographs an image of an object including a color chart, an image inverse conversion means 5 inversely converts digital image data of the object into 3-stimulus data, a color correction matrix application means 6 applies a color correction matrix introduced on the basis of the 3-stimulus data as to the color chart included in the object and the 3-stimulus data of the color chart in a standard state to the 3-stimulus data derived through inverse conversion and an image conversion means 7 converts the 3-stimulus data to which the color correction matrix is applied into digital image data.

Detection and Recognition of Pavement Cracks Based on Computer Vision Technology

Abstract: The article deals with the image crack analysis issues in pavement management systems based on the computer vision technology. The development of pavement management systems is necessary to predict the long-term performance of roads and prioritize maintenance policies. An algorithm for detecting cracks in pavement images is proposed. The algorithm includes the following steps: enhancing the image contrast; the operation of the morphological opening; image conversion to halftone; Bottom-hat transform; threshold conversion; image cleaning from noise by opening operation; the operation of connecting potentially fractured cracks; search for the edges of cracks; crack edges classification; visualization of the found cracks edges. The article also describes the implementation of the proposed algorithm in the form of experimental automated system software. This software is developed in the C ++ using the computer vision library OpenCV and operates on the Jetson TX2 modular supercomputers hardware platform. The results of studies of the proposed approach are given.

Image conversion system using edge information

Abstract: In accordance with at least some embodiments of the present disclosure, a process for converting a two-dimensional (2D) image based on edge information is described. The process may include partitioning the 2D image to generate a plurality of blocks, segmenting the plurality of blocks into a group of regions based on edges determined in the plurality of blocks, assigning depth values to the plurality of blocks based on a depth gradient hypothesis associated with the group of regions, wherein pixels in each of the plurality of blocks are associated with a same depth value, and generating the depth map based on the depth values of the plurality of blocks.

Projection image display system

Abstract: PROBLEM TO BE SOLVED: To make adjustment easy when vertical and horizontal trapezoidal distortions are compensated individually. SOLUTION: The projection image display system includes image display devices 9r to 9b, circuits 2 to 7 for displaying a prescribed image by driving the image display device on the basis of an input image signal, and a light source for projecting the display image of the image display device through light. The circuit includes a circuit 5 for carrying out image conversion process according to the outer operation by fixing the compensation ratio of trapezoidal distortion in one direction (for example, vertical direction) out of vertical and horizontal directions of the projection image, and changing the compensation ratio of trapezoidal distortion in the other direction (for example, horizontal direction).

Image adaptive enhancement method based on histogram compactness transformation

Abstract: The present invention provides an image adaptive enhancement method based on histogram compactness transformation. The method comprises a step 1 of gathering the gray scale frequency histograms of the input images, and calculating the characteristic values of the image histograms; a step 2 of calculating a search starting point and the left and right reference frequencies; a step 3 of carrying out the histogram compactness transformation; a step 4 of establishing a gray scale mapping relation; a step 5 of carrying out the input image gray scale reconstruction. The method of the present invention considers the own characteristics of the input images, so that the insufficiencies of the enhancement methods, such as a classic linear stretching transformation method, a histogram truncation stretching method, etc., are overcome effectively. Experiments show that the method is obvious in enhancement effect, has good adaptability and practicality, can be widely used for the enhancement of various low contrast images and the high bit wide quantization image-to-low bit wide quantization image conversion processing, and is a rapid and effective image automatic enhancement method.