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Using Multivariate Statistics

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In this paper, we analyse two well-known objective image quality metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM), and we derive a simple mathematical relationship between them which works for various kinds of image degradations such as Gaussian blur, additive Gaussian white noise, jpeg and jpeg2000 compression. A series of tests realized on images extracted from the Kodak database gives a better understanding of the similarity and difference between the SSIM and the PSNR.

Image Quality Metrics: PSNR vs. SSIM

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Many contemporary visualization tools comprise some image filtering approach. Since image filtering approaches are very computationally demanding, the acceleration using graphics-hardware (GPU) is very desirable to preserve interactivity of the main visualization tool itself. In this article we take a close look on GPU implementation of two basic approaches to image filtering - Fast Fourier Transform (frequency domain) and convolution (spatial domain). We evaluate these methods in terms of the performance in real time applications and suitability for GPU implementation. Convolution yields better performance than Fast Fourier Transform (FFT) in many cases; however, this observation cannot be generalized. In this article we identify conditions under which the FFT gives better performance than the corresponding convolution and we assess the different kernel sizes and issues of application of multiple filters on one image.

/pdf/fft-and-convolution-performance-in-image-filtering-on-gpu-49drcjgbte.pdf

FFT and Convolution Performance in Image Filtering on GPU

The visualization of color images in gray scale has high practical and theoretical importance. Neither the existing local, gradient based methods, nor the fast global techniques give a satisfying result. We present a new color to grayscale transformation, based on the experimental background of the Coloroid system observations. We regard the color and luminance contrasts as a gradient field and we introduce a new simple, yet very efficient method to solve the inconsistency of the field. Having a consistent gradient field, we obtain the resultant image via fast direct integration. The complexity of the method is linear in the number of pixels, making it fast and suitable for high resolution images.

An efficient perception-based adaptive color to gray transformation

We present a system for the annotation and augmentation of mountain photographs. The key issue resides in the registration of a given photograph with a 3D geo-referenced terrain model. Typical outdoor images contain little structural information, particularly mountain scenes whose aspect changes drastically across seasons and varying weather conditions. Existing approaches usually fail on such difficult scenarios. To avoid the burden of manual registration, we propose a novel automatic technique. Given only a viewpoint and FOV estimates, the technique is able to automatically derive the pose of the camera relative to the geometric terrain model. We make use of silhouette edges, which are among most reliable features that can be detected in the targeted situations. Using an edge detection algorithm, our technique then searches for the best match with silhouette edges rendered using the synthetic model. We develop a robust matching metric allowing us to cope with the inevitable noise affecting detected edges (e.g. due to clouds, snow, rocks, forests, or any phenomenon not encoded in the digital model). Once registered against the model, photographs can easily be augmented with annotations (e.g. topographic data, peak names, paths), which would otherwise imply a tedious fusion process. We further illustrate various other applications, such as 3D model-assisted image enhancement, or, inversely, texturing of digital models.

https://graphics.tudelft.nl/Publications-new/2011/BCES11a/photo-to-terrain_popularEN.pdf

Automatic photo-to-terrain alignment for the annotation of mountain pictures

Numerous current Computer Graphics methods produce video sequences as their outcome. The merit of these methods is often judged by assessing the quality of a set of results through lengthy user studies. We present a full-reference video quality metric geared specifically towards the requirements of Computer Graphics applications as a faster computational alternative to subjective evaluation. Our metric can compare a video pair with arbitrary dynamic ranges, and comprises a human visual system model for a wide range of luminance levels, that predicts distortion visibility through models of luminance adaptation, spatiotemporal contrast sensitivity and visual masking. We present applications of the proposed metric to quality prediction of HDR video compression and temporal tone mapping, comparison of different rendering approaches and qualities, and assessing the impact of variable frame rate to perceived quality.

/pdf/video-quality-assessment-for-computer-graphics-applications-2elf08xc0w.pdf

Video quality assessment for computer graphics applications

We present a new approach for generating global illumination renderings of hand-drawn characters using only a small set of simple annotations. Our system exploits the concept of bas-relief sculptures, making it possible to generate 3D proxies suitable for rendering without requiring side-views or extensive user input. We formulate an optimization process that automatically constructs approximate geometry sufficient to evoke the impression of a consistent 3D shape. The resulting renders provide the richer stylization capabilities of 3D global illumination while still retaining the 2D hand-drawn look-and-feel. We demonstrate our approach on a varied set of hand-drawn images and animations, showing that even in comparison to ground-truth renderings of full 3D objects, our bas-relief approximation is able to produce convincing global illumination effects, including self-shadowing, glossy reflections, and diffuse color bleeding.

/pdf/ink-and-ray-bas-relief-meshes-for-adding-global-illumination-3mcnv8x4f0.pdf

Martin Čadík

Papers

FFT and Convolution Performance in Image Filtering on GPU

An efficient perception-based adaptive color to gray transformation

Automatic photo-to-terrain alignment for the annotation of mountain pictures

Video quality assessment for computer graphics applications

Ink-and-ray: Bas-relief meshes for adding global illumination effects to hand-drawn characters