Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Capable of automated near real time superpixel detection and quality assessment in an uncalibrated monitor typical red green blue (RGB) image, depicted in either true or false colors, an original low level computer vision (CV) lightight computer program, called RGB Image Automatic Mapper (RGBIAM), is designed and implemented. Constrained by the Calibration Validation (CalVal) requirements of the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, RGBIAM requires as mandatory an uncalibrated RGB image pre processing first stage, consisting of an automated statistical model based color constancy algorithm. The RGBIAM hybrid inference pipeline comprises: (I) a direct quantitative to nominal (QN) RGB variable transform, where RGB pixel values are mapped onto a prior dictionary of color names, equivalent to a static polyhedralization of the RGB cube. Prior color naming is the deductive counterpart of inductive vector quantization (VQ), whose typical VQ error function to minimize is a root mean square error (RMSE). In the output multi level color map domain, superpixels are automatically detected in linear time as connected sets of pixels featuring the same color label. (II) An inverse nominal to quantitative (NQ) RGB variable transform, where a superpixelwise constant RGB image approximation is generated in linear time to assess a VQ error image. The hybrid direct and inverse RGBIAM QNQ transform is: (i) general purpose, data and application independent. (ii) Automated, i.e., it requires no user machine interaction. (iii) Near real time, with a computational complexity increasing linearly with the image size. (iv) Implemented in tile streaming mode, to cope with massive images. Collected outcome and process quality indicators, including degree of automation, computational efficiency, VQ rate and VQ error, are consistent with theoretical expectations.
['Andrea Baraldi', 'Dirk Tiede', 'Stefan Lang']
I am writing a research paper on a idea Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images
[{"from": "human", "value": "I am writing a research paper on a idea Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Capable of automated near real time superpixel detection and quality assessment in an uncalibrated monitor typical red green blue (RGB) image, depicted in either true or false colors, an original low level computer vision (CV) lightight computer program, called RGB Image Automatic Mapper (RGBIAM), is designed and implemented. Constrained by the Calibration Validation (CalVal) requirements of the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, RGBIAM requires as mandatory an uncalibrated RGB image pre processing first stage, consisting of an automated statistical model based color constancy algorithm. The RGBIAM hybrid inference pipeline comprises: (I) a direct quantitative to nominal (QN) RGB variable transform, where RGB pixel values are mapped onto a prior dictionary of color names, equivalent to a static polyhedralization of the RGB cube. Prior color naming is the deductive counterpart of inductive vector quantization (VQ), whose typical VQ error function to minimize is a root mean square error (RMSE). In the output multi level color map domain, superpixels are automatically detected in linear time as connected sets of pixels featuring the same color label. (II) An inverse nominal to quantitative (NQ) RGB variable transform, where a superpixelwise constant RGB image approximation is generated in linear time to assess a VQ error image. The hybrid direct and inverse RGBIAM QNQ transform is: (i) general purpose, data and application independent. (ii) Automated, i.e., it requires no user machine interaction. (iii) Near real time, with a computational complexity increasing linearly with the image size. (iv) Implemented in tile streaming mode, to cope with massive images. Collected outcome and process quality indicators, including degree of automation, computational efficiency, VQ rate and VQ error, are consistent with theoretical expectations."}]