Abstract: Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation. (d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

Abstract: Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation.(d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

Abstract: Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation. (d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

Abstract: A vehicle identification system and method comprising (a) Calculating vehicle image and reference image of each vehicle by algorithm, in order to obtain respectively the first feature point set and second feature point set. (b) Comparing first feature point with second feature point to calibrate the vehicle image of each vehicle. (c) Establishing plurality of vehicle model image with a gradient bevel distance of the base of the conversion of such vehicles. (d) Capturing instant image of the vehicle. (e) Calculating instant image and reference image by algorithm to obtain respectively the third feature point and the second feature point. (f) Comparing the second feature point and the third feature point to calibrate the instant vehicle image. (g) The instant judgments greatest body image and the gradient of each pixel in each of the base vehicle model for each pixel of the image of the product to determine the real-time vehicle body image should compare of the vehicle model image of one of them.

Abstract: A vehicle identification system and method comprising (a) Calculating vehicle image and reference image of each vehicle by algorithm, in order to obtain respectively the first feature point set and second feature point set. (b) Comparing first feature point with second feature point to calibrate the vehicle image of each vehicle. (c) Establishing plurality of vehicle model image with a gradient bevel distance of the base of the conversion of such vehicles. (d) Capturing instant image of the vehicle. (e) Calculating instant image and reference image by algorithm to obtain respectively the third feature point and the second feature point. (f) Comparing the second feature point and the third feature point to calibrate the instant vehicle image. (g) The instant judgments greatest body image and the gradient of each pixel in each of the base vehicle model for each pixel of the image of the product to determine the real-time vehicle body image should compare of the vehicle model image of one of them.

Abstract: Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation.(d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.