Digital color imaging

Sharma, Gaurav; Trussell, H.J.

doi:10.1109/83.597268

Cited by 366 publications

(202 citation statements)

References 249 publications

(255 reference statements)

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“…Since both measures are essential for human perception [19], any color image encryption solution should alter both the magnitude and the orientation characteristics of the original color vectors. Using secret sharing principles for color image encryption [3]- [5], [13], the cryptographic solution generates color shares which contain noise-like, seemingly unrelated information.…”

Section: Color Imaging Basicsmentioning

confidence: 99%

A Cost-Effective Private-Key Cryptosystem for Color Image Encryption

Lukáč

Plataniotis

2005

Lecture Notes in Computer Science

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Abstract. This paper presents a cost-effective private-key cryptosystem for color images. The scheme allows for secret sharing of the color image by generating two color shares with dimensions identical to those of the original. Encryption is performed via simple binary operations realized at the image bit-levels altering both the spectral correlation among the RGB color components and the spatial correlation between the neighboring color vectors. The decryption procedure uses both noise-like color shares as the input and recovers the original image with perfect reconstruction.

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Section: Color Imaging Basicsmentioning

confidence: 99%

A Cost-Effective Private-Key Cryptosystem for Color Image Encryption

Lukáč

Plataniotis

2005

Lecture Notes in Computer Science

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“…8), although the enhancement was preferred more often than the original H&E. A further factor responsible for the preference of enhanced images in normal observers may be related to the ability to discriminate some hue pairs better than others. There are a number of theories that attempt to explain this observation, including Hering's 'opponent colour' theory (Hering, 1964;Sharma & Trussell, 1997). On a colour wheel that maps all hues into a circle, the best contrasted colours are those that appear opposite to each other because they are not elicited simultaneously by a single colour stimulus, for example, red and cyan, green and magenta, blue and yellow (see the hue wheels in Fig.…”

Section: Discussionmentioning

confidence: 99%

Digital enhancement of haematoxylin‐ and eosin‐stained histological images for red–green colour‐blind observers

Landini

Perryer

2009

Journal of Microscopy

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SummaryIndividuals with red-green colour-blindness (CB) commonly experience great difficulty differentiating between certain histological stain pairs, notably haematoxylin-eosin (H&E). The prevalence of red-green CB is high (6-10% of males), including among medical and laboratory personnel, and raises two major concerns: first, accessibility and equity issues during the education and training of individuals with this disability, and second, the likelihood of errors in critical tasks such as interpreting histological images. Here we show two methods to enhance images of H&E-stained samples so the differently stained tissues can be well discriminated by red-green CBs while remaining usable by people with normal vision. Method 1 involves rotating and stretching the range of H&E hues in the image to span the perceptual range of the CB observers. Method 2 digitally unmixes the original dyes using colour deconvolution into two separate images and repositions the information into hues that are more distinctly perceived. The benefits of these methods were tested in 36 volunteers with normal vision and 11 with red-green CB using a variety of H&E stained tissue sections paired with their enhanced versions. CB subjects reported they could better perceive the different stains using the enhanced images for 85% of preparations (method 1: 90%, method 2: 73%), compared to the H&E-stained original images. Many subjects with normal vision also preferred the enhanced images to the original H&E. The results suggest that these colour manipulations confer considerable advantage for those with red-green colour vision deficiency while not disadvantaging people with normal colour vision.

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“…Among these, perceptually uniform color spaces are the most appropriate to de"ne measures of perceptual error [17].…”

Section: Rgb To Opponent-color Encoding Transformmentioning

confidence: 99%

Integral opponent-colors features for computing visual target distinctness

Fdez-Vidal

Rodrı́guez-Sánchez

García

et al. 2000

Pattern Recognition

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This paper presents a computational target distinctness model to predict human visual search performance for a set of color images. Visual distinctness metrics can be used to compare and rank target detectability, and to quantify background or scene complexity. The subjective ranking induced by the psychophysically determined visual target distinctness for 64 human observers is adopted as the reference order. The ranking produced by the proposed measure and several computational models are compared with the subjective rank order.

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Digital color imaging

Cited by 366 publications

References 249 publications

A Cost-Effective Private-Key Cryptosystem for Color Image Encryption

A Cost-Effective Private-Key Cryptosystem for Color Image Encryption

Digital enhancement of haematoxylin‐ and eosin‐stained histological images for red–green colour‐blind observers

Integral opponent-colors features for computing visual target distinctness

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