An Evaluation of Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion and Manual Colour Grading

2019 ◽  
Vol 2019 (1) ◽  
pp. 69-74
Author(s):  
Aldo Barba ◽  
Ivar Farup ◽  
Marius Pedersen
Keyword(s):  
Anisotropic Diffusion ◽  
Motion Pictures ◽  
Current Paper ◽  
Original Image ◽  
Colour Image ◽  
Image Conversion ◽  
Significant Difference ◽  
Greyscale Image

In the paper "Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion of Perceptual Colour Metrics", Farup et al. presented an algorithm to convert colour images to greyscale. The algorithm produces greyscale reproductions that preserve detail derived from local colour differences in the original colour image. Such detail is extracted by using linear anisotropic diffusion to build a greyscale reproduction from a gradient of the original image that is in turn calculated using Riemannised colour metrics. The purpose of the current paper is to re-evaluate one of the psychometric experiments for these two methods (CIELAB L* and anisotropic Δ99) by using a flipping method to compare their resulting images instead of the side by side method used in the original evaluation. In addition to testing the two selected algorithms, a third greyscale reproduction was manually created (colour graded) using a colour correction software commonly used to process motion pictures. Results of the psychometric experiment found that when comparing images using the flipping method, there was a statistically significant difference between the anisotropic Δ99 and CIELAB L* conversions that favored the anisotropic method. The comparison between Δ99 conversion and the manually colour graded image also showed a statistically significant difference between them, in this case favoring the colour graded version.

scholarly journals IMPLEMENTATION OF CRYPTOGRAPHY AND STEGANOGRAPHY FOR TEXT ON COVER IMAGE USING AES AND F5 ALGORITHM

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Ratna Astuti Nugrahaeni ◽  
R. Rumani M. R. Rumani M. ◽  
Surya Michrandi Nasution
Keyword(s):  
Cover Image ◽  
Security Level ◽  
Original Image ◽  
File Size ◽  
Stego Image ◽  
Size Change ◽  
Avalanche Effect ◽  
Aes Algorithm ◽  
Significant Difference

This journal explains about implementation that combine both cryptography and steganography method for texton cover image to increase the security level. Text will be encrypted with AES algorithm, and then it will be embedded to the cover image using F5 algorithm. The implemented AES algorithm has a good performance, with Avalanche Effect value ranges from 0.43 � 0.59. The resulting image, or stego image, has a very similar histogram with the original image, so there is no significant difference between the two of them. However, the file size change about 1.25 � 3.25 times larger than theoriginal image. If noise or disruption is given to stego image, the information can not be extracted.Keywords: cryptography, steganography, AES, F5

scholarly journals The role of diffusion tensor imaging metrics in machine learning-based characterisation of paediatric brain tumors and their practicality for multicentre clinical assessment

2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv1-iv2
Author(s):  
Heather Rose ◽  
Huijun Li ◽  
Christopher D Bennett ◽  
Jan Novak ◽  
Yu Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumours ◽  
Anisotropic Diffusion ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Imaging Biomarkers ◽  
Significant Difference ◽  
Mri Scans ◽  
Leave One Out ◽  
Paediatric Brain Tumours

Abstract Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P<.0001, Kruskal Wallis test). Median MD, p and q values were highest in PA, then EP and lowest in MB (P<.0001, Pairwise Wilcox test). FA median was higher for EP than PA (P=.004) with no significant difference between EP and MB (P=.591). ROC analysis showed that median MD, q and p perform best as a diagnostic marker (AUC= 0.92 to 0.99). LOOCV showed an overall accuracy of the LDA classification, ranging between 67% - 87%. FA values were highly dependent on protocol parameters, whereas pure anisotropic diffusion, q, was not. Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

scholarly journals Individualised Halo-Free Gradient-Domain Colour Image Daltonisation

2020 ◽  
Vol 6 (11) ◽  
pp. 116
Author(s):  
Ivar Farup
Keyword(s):  
Anisotropic Diffusion ◽  
Colour Vision ◽  
Computational Time ◽  
Simulation Algorithm ◽  
Colour Vision Deficiency ◽  
Local Linear ◽  
Colour Image ◽  
Gradient Domain ◽  
The Cost

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known.

scholarly journals On the Information Advantage of Sidescan Sonar Three-Frequency Colour over Greyscale Imagery

2019 ◽  
Vol 7 (8) ◽  
pp. 276
Author(s):  
Duncan Tamsett ◽  
Jason McIlvenny ◽  
James Baxter ◽  
Paulo Gois ◽  
Benjamin Williamson
Keyword(s):  
Image Texture ◽  
Single Frequency ◽  
Sidescan Sonar ◽  
Prototype System ◽  
Black And White ◽  
Sonar Image ◽  
Colour Image ◽  
Underwater Mapping ◽  
Greyscale Image ◽  
Information Advantage

A prototype three-frequency (114, 256, and 410 kHz) colour sidescan sonar system, built by Kongsberg Underwater Mapping Ltd. (Great Yarmouth, UK), was previously described, and preliminary results presented, in Tamsett, McIlvenny, and Watts. The prototype system has subsequently been modified, and in 2017, new data were acquired in a resurvey of the Inner Sound of the Pentland Firth, North Scotland. An image texture characterisation and image classification exercise demonstrates considerably greater discrimination between different seabed classes in a three-frequency colour sonar image of the seabed, than in a multi-frequency colour image reduced to greyscale display, or in a single-frequency greyscale image, with readily twice the number of classes of seabed discriminated between, in the colour image. The information advantage of colour acoustic imagery over greyscale acoustic imagery is analogous to the information advantage of colour television images over black-and-white television images. A three-frequency colour sonar image contains a theoretical maximum of a factor of 3 times the information in a corresponding greyscale image, for independent seabed responses at the three frequencies. Estimates of the average information per pixel (information entropy) in the colour image, and in corresponding greyscale images, reveal an actual information advantage of colour sonar imagery over greyscale, to be in practice approximately a factor of 2.5, empirically confirming the greater information based utility of three-frequency colour sonar over greyscale sonar. Reference: Tamsett, D.; McIlvenny, J.; Watts, A. J. Mar. Sci. Eng. 2016, 4(26).

scholarly journals Control the Information of the Image with Anisotropic Diffusion and Isotropic Diffusion for the Image Classification

2021 ◽  
Author(s):  
Hyun-Tae Choi ◽  
Nahyun Lee ◽  
Jewon No ◽  
Sangil Han ◽  
Jaeho Tak ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Anisotropic Diffusion ◽  
Diffusion Processes ◽  
Original Image ◽  
Learning Framework ◽  
Isotropic Diffusion ◽  
Using Data ◽  
Discriminative Parts ◽  
Similar Classification

Humans can recognize objects well even if they only show the shape of objects or an object is composed of several components. But, most of the classifiers in the deep learning framework are trained through original images without removing complex elements inside the object. And also, they do not remove things other than the object to be classified. So the classifiers are not as effective as the human classification of objects because they are trained with the original image which has many objects that the classifier does not want to classify. In this respect, we found out which pre-processing can improve the performance of the classifier the most by comparing the results of using data through other pre-processing. In this paper, we try to limit the amount of information in the object to a minimum. To restrict the information, we use anisotropic diffusion and isotropic diffusion, which are used for removing the noise in the images. By using the anisotropic diffusion and the isotropic diffusion for the pre-processing, only shapes of objects were passed to the classifier. With these diffusion processes, we can get similar classification accuracy compared to when using the original image, and we found out that although the original images are diffused too much, the classifier can classify the objects centered on discriminative parts of the objects.

scholarly journals Concept of Abetment in the IPC (2013)

2017 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Mahmood Rooholamini
Keyword(s):  
Criminal Law ◽  
Penal Code ◽  
Current Paper ◽  
Legal Term ◽  
Legal Perspective ◽  
Significant Difference ◽  
Definition Of

Undoubtedly, in many cases, the definition of a legal term or an entity provided by lawyers has significant difference with the definition provided by the institution of the legislature. Therefore, it can be distinguished between the concept of abbeting from a legal perspective, i.e., from the perspective of lawyers and the legal perspective as well as that of the legislature. There is no single definition of abetment from a legal standpoint, but there are various definitions of the term. However, the definitions have much in common. Iranian legislator has not defined “abetment” in the Islamic Penal Code (2003) but addressed some cases of behaviors that may constitute complicity in the crime. In addition, the Iranian legislator has extended the scope of the crimes that shall also apply abetment in IPC (2013). This is contrary to the principle of minimumality of criminal law based on the scope of the criminal law must be limited. In addition, the new law has serious flaws that have the current paper tried to remind objections and provide recommendations for amendments.

scholarly journals PENERAPAN ALGORITMA K-MEANS PADA KOMPRESI ADAPTIF CITRA MEDIS MRI

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
I Wayan Angga Wijaya ◽  
Afriliana Kusumadewi
Keyword(s):  
Medical Image ◽  
Storage Capacity ◽  
Medical Image Processing ◽  
Dimensional Vector ◽  
Original Image ◽  
Limited Bandwidth ◽  
Significant Difference ◽  
Last Stage ◽  
And Storage

MRI medical image processing require large amounts of memory. Due to limited bandwidth and storage capacity, the image must be compressed prior to transmission and stored. This paper has the objective to implement the algorithm k means the MRI medical image compression. Implementation begins with the Pre post. At this stage, L-dimensional vector of the image will be made. L is the block - a measure used for clustering technique, but is set back in the form of an array. Then the process of clustering. At this stage, every pixel of the image is represented by the centroid of the cluster. And the last stage is the Main Compression, the pixels that do not contain important information will be removed. The study compared the quality of the original image and compressed image. Based on manual observation, there is no significant difference in quality between the original image and the compressed one.

scholarly journals Individualised Halo-Free Gradient-Domain Colour Image Daltonisation

2020 ◽  
Author(s):  
Ivar Farup
Keyword(s):  
Anisotropic Diffusion ◽  
Colour Vision ◽  
Computational Time ◽  
Simulation Algorithm ◽  
Colour Vision Deficiency ◽  
Local Linear ◽  
Colour Image ◽  
Gradient Domain ◽  
The Cost

Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known.

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