Community Research and Development Information Service - CORDIS


MONOTONE Report Summary

Project ID: G1RD-CT-2002-00783
Funded under: FP5-GROWTH
Country: United Kingdom

Tone analysis and Luminance correction

Tone analysis for plain tiles with uniform colour:
Consistency of tonality is usually more stringent than textured cases as less information is present to ‘mask’ variations. Proposed method inspects spatial uniformity in separate opponent colour channels: black/white, red/green, blue/yellow. A linear transformation is utilised to decrease computation costs. Initial set of ideal tonality tiles are acquired and tonality distributions of the set established as the reference model. Each channel is split into small, non-overlapping patches and the patches ordered according to relative intensity to form a distribution profile. The distribution range is constrained via a spatial uniformity test. Given a new tile, the tonality profile distributions are extracted and compared to the respective reference model profiles. A tonality difference is derived via a weighted sum of the Mean Squared Error of the relative profiles. Good results in both synthetic tests and from images acquired during trials with the SI prototypes were reported. An online version was implemented in real-time on the latest SI prototype.

Tone analysis for pseudo-random textured tiles via colour histograms:
Colour histograms provide a simple, low-level representation of texture space, invariant to translation, rotation and spatial distribution of pixels. They offer good candidates for colour shade discrimination irrespective of textured pattern present. A set of ideal images is required to construct a concept of acceptable tonality. Multiple colour spaces investigated including Lab (high computation cost due to non-linear mapping) and RGB (computationally fast and results suggest sufficient for representation). Histograms are stored via a binary tree distribution to minimise storage requirements and increase distribution comparison performance. Distributions compared using Normalised Cross Correlation (NCC) and Chi squared statistical methods. NCC method selected due to its bounded output values (-1,1). A bounded output range aids the selection and application of defect detection thresholds. Results illustrated the ability to detect missing print defects in addition to tonal defects. An online version was implemented in real-time on the latest SI prototype.

Tone analysis for pseudo-random textured tiles via eigenspace features:
Multi-dimensional histogram based approach for colour tonality defects on textured tiles combining local and global colour distributions to characterise tonality. A voting scheme to extract a reference tile from the initial defect free sample set was employed. Vector directional processing method applied to compute Local Common Vector amongst pixels in RGB space, eliminates noise and perceptually smooth acquired images. For each pixel a 9D feature vector was computed and Principal Component Analysis (PCA) performed on the resultant 9D feature space. The first few eigenvectors displaying the largest eigenvalues were selected to form the reference eigenspace. Colour features then projected into reference space and a multi-dimension histogram created. New tiles processed in a similar manner and projected into the reference space. Histogram distribution comparison performed utilising NCC as in the previously outlined histogram based technique. Method produced better results than the original colour histogram approach but computationally unsuitable for real-time application at this time.

Spatial and Temporal Luminance Correction via Histogram Specification:
Radial camera used for image acquisition in the UoB laboratory. Image acquisition is non-linear by nature, spatial and temporal variations in illumination occur: cosine-4th fall off, vignetting effects, non-uniform illumination of the target etc. Acquired image is transformed into Luv space to separate the luminance and chromatic channels. The luminance channel is then split into patches and a single patch selected as a reference, the remaining patches are corrected in relation to the reference. Method can correct spatial luminance variations with minor negative effects and may be detrimental to the task of defect detection if applied to remove temporal variations. Method is applicable to radial cameras only; line-scan cameras require an alternate method as each column of pixels are derived from a single CCD element. Therefore a column based luminance profile is generated utilising a plain, matt white tile to give a balanced response in RGB. Given a plain tile the colour can be assumed to be constant, any variations observed are due to external influence. Colour channels are modelled independently and a median based profile is generated for robustness to noise. Resulting profiles are then converted into normalised corrective forms and applied as a scalar correction factor to acquired image colour channels.
An online version was implemented in real-time on the latest SI prototype and forms one of the initial processing steps prior to higher level processing.


Majid MIRMEHDI, (Reader in Image Analysis)
Tel.: +44-11-79545139
Fax: +44-11-79545208
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