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Fast chromatic adaptation transform utilizing Wpt (Waypoint) based spectral reconstruction

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Fast chromatic adaptation transform utilizing Wpt (Waypoint)...

London Imaging Meeting, LIM 2020

作者： Derhak, Maxim W. Lin Luo, Eric Green, Philip J. Onyx Graphics Inc Salt Lake CityUT United States OmniVision Technologies Inc Santa ClaraCA United States Colour and Visual Computing Laboratory NTNU Norway

ISBN: (纸本)9780892083466

Burns previously showed that a chromatic adaptation transform (CAT) implemented using spectral reconstruction with lightness preserving produces comparable predictive results of corresponding color data sets as optimized linear CATs. A fast spectral reconstruction CAT based on Wpt (Waypoint) spectral reconstruction is proposed that is optimized to take advantage of spectral reconstruction improvements without incurring the performance cost of using spectral reflectance. Comparisons are made, and an implementation using iccMAX profiles is demonstrated. © 2020, Society for Imaging Science and Technology.

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4x4 Census Transform

4x4 Census Transform

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Olivier Rukundo Norwegian Colour and Visual Computing Laboratory Norwegian University of Science and Technology (NTNU) Gjovik Norway

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

This paper proposes a novel 4x4 census transform (4x4CT) to encourage further research in computer vision and visual computing. Unlike the traditional census transform (3x3CT) which only uses a nine pixels kernel or 3x3 window size, the proposed 4x4CT uses a sixteen pixels kernel or 4x4 window size with four overlapped groups of 3x3 kernel size. In each overlapping group, a reference input pixel profits from its nearest eight pixels to produce an eight bits binary string convertible to a grayscale decimal integer corresponding to the 4x4CT's output pixel. Preliminary subjective assessments were more promising with the 4x4CT transformed images than 3x3CT transformed images.

关键词： visualization Computer vision Image recognition Lighting Transforms Feature extraction Kernel

colour image enhancement using perceptual saturation and vividness 27

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Colour image enhancement using perceptual saturation and viv...

27th Color and Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, CIC 2019

作者： Safdar, Muhammad Pozzera, Noemie Hardeberg, Jon Yngve Norwegian Colour and Visual Computing Laboratory NTNU Norwegian University of Science and Technology Gjøvik Norway

ISBN: (纸本)9780892083435

A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported. © 2019 Society for Imaging Science and Technology.

关键词： Color

Morton filters for superior template protection for iris recognition

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arXiv 2020年

作者： Raja, Kiran B. Raghavendra, R. Venkatesh, Sushma Busch, Christoph Norwegian Colour and Visual Computing Laboratory NTNU - Gjøvik Norway Norwegian Biometrics Laboratory NTNU - Gjøvik Norway

In this work, we address the fundamental performance issues of template protection for iris verification. We base our work on the popular Bloom-Filter templates protection and address the key challenges like sub-optimal performance and low unlinkability. Specifically, we focus on cases where Bloom-filter templates results in non-ideal performance due to presence of large degradations within iris images. Iris recognition is challenged with number of occluding factors such as presence of eye-lashes within captured image, occlusion due to eyelids, low quality iris images due to motion blur amongst many other. All of such degrading factors result in obtaining non-reliable iris codes and thereby provide non-ideal biometric performance. These factors further directly impact the protected templates derived from the iris images when classical Bloom-filters are employed. To this end, we propose and extend our earlier ideas of Morton-filters for obtaining better and reliable templates for iris. Morton filter based template protection for iris codes is based on leveraging the intra-class and inter-class distribution by exploiting the low-rank iris codes to derive the stable bits across the iris images for a particular subject and also analyzing the discriminable bits across various subjects. Such low-rank non-noisy iris codes enables realizing the template protection in a superior way which not only can be used in constrained setting, but also can be used in relaxed iris imaging. We further extend the work to analyze the applicability to visible spectrum iris images by employing a large scale public iris image database - UBIRIS (v1 and v2), captured in a unconstrained setting. Through a set of thorough experiments, we demonstrate the applicability of proposed approach and vet the strengths and weakness. Yet another contribution of this work stems in assessing the security of the proposed approach where factors of Unlinkability is studied to indicate the antagonistic nature to

关键词： Biometrics

Evaluation of Rounding Functions in Nearest-Neighbor Interpolation

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arXiv 2020年

作者： Rukundo, Olivier Norwegian Colour and Visual Computing Laboratory Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway

A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image quality assessment (IQA) metrics are used to study and evaluate the influence of rounding functions on NN interpolation image quality. The concept of achieved occurrences over targeted occurrences is used to determine the percentage of achieved occurrences based on the number of test images used. Inferential statistical analysis is applied to deduce from a small number of images and draw a conclusion of the behavior of each rounding function on a bigger number of images. Under the normal distribution and at the level of confidence equals to 95%, the maximum and minimum achievable occurrences by each evaluated rounding function are both provided based on the inferential analysis-based experiments. © 2020, CC BY.

关键词： Interpolation

Compact Models for Periocular Verification Through Knowledge Distillation

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Compact Models for Periocular Verification Through Knowledge...

International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Fadi Boutros Naser Damer Meiling Fang Kiran Raja Florian Kirchbuchner Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany The Norwegian Colour and Visual Computing Laboratory NTNU Gjovik Norway

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

Despite the wide use of deep neural network for periocular verification, achieving smaller deep learning models with high performance that can be deployed on low computational powered devices remains a challenge. In term of computation cost, we present in this paper a lightweight deep learning model with only 1.1m of trainable parameters, DenseNet-20, based on DenseNet architecture. Further, we present an approach to enhance the verification performance of DenseNet-20 via knowledge distillation. With the experiments on VISPI dataset captured with two different smartphones, iPhone and Nokia, we show that introducing knowledge distillation to DenseNet-20 training phase outperforms the same model trained without knowledge distillation where the Equal Error Rate (EER) reduces from 8.36% to 4.56% EER on iPhone data, from 5.33% to 4.64% EER on Nokia data, and from 20.98% to 15.54% EER on cross-smartphone data.

关键词： Smart phones Computational modeling Training Performance evaluation Databases Feature extraction Lighting

On benchmarking iris recognition within a Head-Mounted Display for AR/VR applications

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arXiv 2020年

作者： Boutros, Fadi Damer, Naser Raja, Kiran Ramachandra, Raghavendra Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Norwegian Colour and Visual Computing Laboratory NTNU Gjovik Norway Norwegian Biometrics Laboratory NTNU Gjovik Norway

Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafted feature extraction approaches, and to complement it, we also present the analysis using four deep learning models. While taking into consideration the minimalistic hardware requirements of stand-alone HMD, we employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Further, to account for non-ideal and non-collaborative capture of iris, we define a new iris quality metric that we termed as Iris Mask Ratio (IMR) to quantify the iris recognition performance. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD. Through the experiments on a publicly available OpenEDS dataset, we show that performance with EER = 5% can be achieved using deep learning methods in a general setting, along with high accuracy for continuous user authentication. Copyright © 2020, The Authors. All rights reserved.

关键词： Helmet mounted displays

Challenges of 3D Surface Reconstruction in Capsule Endoscopy

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arXiv 2021年

作者： Rukundo, Olivier Norwegian Colour and Visual Computing Laboratory Department of Computer Science Norwegian University of Science and Technology Teknologiveien 22 Gjøvik2815 Norway Center for Clinical Research University Clinic of Dentistry Medical University of Vienna Sensengasse 2a Vienna1090 Austria

Essential for improving the accuracy and reliability of bowel cancer screening, three-dimensional (3D) surface reconstruction using capsule endoscopy (CE) images remains challenging due to CE hardware and software limitations. This report generally focuses on challenges associated with 3D visualization and specifically investigates the impact of the indeterminate selection of the angle of the line–of–sight on 3D surfaces. Furthermore, it demonstrates that impact through 3D surfaces viewed at the same azimuth angles and different elevation angles of the line–of–sight. The report concludes that 3D printing of reconstructed 3D surfaces can potentially overcome line–of–sight indeterminate selection and 2D screen visual restriction-related errors. © 2021, CC BY.

关键词： Image reconstruction

Changing the Image Memorability: From Basic Photo Editing to GANs

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Changing the Image Memorability: From Basic Photo Editing to...

IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops

作者： Oleksii Sidorov The Norwegian Colour and Visual Computing Laboratory NTNU

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is often crucial. Despite numerous studies on understanding and predicting image memorability, there are almost no achievements in memorability modification. In this work, we study two approaches to image editing - GAN and classical image processing - and show their impact on memorability. The visual features which influence memorability directly stay unknown till now, hence it is impossible to control it manually. As a solution, we let GAN learn it deeply using labeled data, and then use it for conditional generation of new images. By analogy with algorithms which edit facial attributes, we consider memorability as yet another attribute and operate with it in the same way. Obtained data is also interesting for analysis, simply because there are no real-world examples of successful change of image memorability while preserving its other attributes. We believe this may give many new answers to the question "what makes an image memorable?" Apart from that we also study the influence of conventional photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience on memorability. In this case, we start from real practical methods and study it using statistics and recent advances in memorability prediction. Photographers, designers, and advertisers will benefit from the results of this study directly.

关键词： Image Editing gallium nitrate images Image Imagery (Psychotherapy) Educational objectives Advertisements Image processing Use statistics Photographers