This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

On robust estimation of the location parameter

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

New light steering projectors in cinema form images by moving light away from dark regions into bright areas of an image In these systems, the peak luminance of small features can far exceed full screen white luminance In traditional projectors where light is filtered or blocked in order to give shades of gray (or colors), the peak luminance is fixed The luminance of chromatic features benefit in the same way as white features, and chromatic image details can be reproduced at high brightness leading to a much wider overall color gamut coverage than previously possible Projectors of this capability are desired by the creative community to aid in and enhance storytelling Furthermore, reduced light source power requirements of light steering projectors provide additional economic and environmental benefits While the dependency of peak luminance level on (bright) image feature size is new in the digital cinema space, display technologies with identical characteristics such as OLED, LED LCD and Plasma TVs are well established in the home Similarly, direct view LED walls are popular in events, advertising and architectural markets To enable consistent color reproduction across devices in today’s content production pipelines, models that describe modern projectors and display attributes need to evolve together with HDR standards and available metadata This paper is a first step towards rethinking legacy display descriptors such as contrast, peak luminance and color primaries in light of new display technology We first summarize recent progress in the field of light steering projectors in cinema and then, based on new projector and existing display characteristics propose the inclusion of two simple display attributes: Maximum Average Luminance and Peak (Color) Primary Luminance We show that the proposed attributes allow a better prediction of content reproducibility on HDR displays To validate this assertion, we test professional content on a commercial HDR television system and show that the proposed attributes better predict if a pixel value lies inside the capabilities of a display or not

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8‐3: Invited Paper: Light Steering Projection Systems and Attributes for HDR Displays

Die Erfindung betrifft einen Sonnenkollektor mit einer auf der Ausenseite davor parallel befestigten, den Sonnenstrahlen ausgesetzten Schicht zur Absorption der Sonnenstrahlungsenergie. Zumindest die auserste Schicht (6) ist lichtdurchlassig, und zwischen der ausersten Schicht (6) und der Innenwand (1) oder der Kollektorinnenseite ist mindestens eine Sonnenstrahlen absorbierende Flache (9) angeordnet, an der mindestens ein mit Salzhydrat reziproken Salzpaaren oder einer Mischung aus Salzhydrat und Salz gefullter Raum (4) angrenzt.

Solar collector with a layer fixed on the outer side

In order to detect low mass and mature planets inwards of approximately 5 AU, future direct imaging instruments will require precision wavefront control that operates at relatively high speed. The self-coherent camera (SCC) is a promising technique for measuring the wavefront from science images at the focal plane. We present here results from NRC’s NEW-EARTH lab testing of the Fast Atmospheric SCC Technique, a variant of the SCC and its integration with a Lyot-stop Low-Order Wavefront Sensor. We demonstrate correction of quasi-static speckles in a half dark hole reaching raw 1σ contrasts on the order of 5 × 10−7 at 10 λ/D. We also present a simplified process for extracting measurements and/or DM commands from SCC images using a single matrixvector multiply. This testing and development are important steps on the way to the upcoming Subaru Pathfinder Instrument for Detection of Exoplanets and Removal of Speckles and the Gemini Planet Imager’s CAL2 upgrade.

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Performance of the Fast Atmospheric Self Coherent camera at the NEW-EARTH lab and a simplified measurement algorithm

In recent years there has been a lot of work on interactively displaying global illumination solutions for nondiffuse environments. This is an extremely active field of research, in which a lot of different approaches have been proposed recently. In this State-of-The-Art-Report, we will discuss and compare these. This will hopefully lay the ground for systematically addressing the open questions in the future.

Interactive Display of Global Illumination Solutions for Non-Diffuse Environments.

Incomplete color sampling, noise degradation, and limited resolution are the three key problems that are unavoidable in modern camera systems Demosaicing (DM), denoising (DN), and super-resolution (SR) are core components in a digital image processing pipeline to overcome the three problems above, respectively Although each of these problems has been studied actively, the mixture problem of DM, DN, and SR, which is a higher practical value, lacks enough attention Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality In this paper, we rethink the mixture problem from a holistic perspective and propose a new image processing pipeline: DN $\to$ SR $\to$ DM Extensive experiments show that simply modifying the usual sequential solution by leveraging our proposed pipeline could enhance the image quality by a large margin We further adopt the proposed pipeline into an end-to-end network, and present Trinity Enhancement Network (TENet) Quantitative and qualitative experiments demonstrate the superiority of our TENet to the state-of-the-art Besides, we notice the literature lacks a full color sampled dataset To this end, we contribute a new high-quality full color sampled real-world dataset, namely PixelShift200 Our experiments show the benefit of the proposed PixelShift200 dataset for raw image processing

Wolfgang Heidrich

Papers

8‐3: Invited Paper: Light Steering Projection Systems and Attributes for HDR Displays

Solar collector with a layer fixed on the outer side

Performance of the Fast Atmospheric Self Coherent camera at the NEW-EARTH lab and a simplified measurement algorithm

Interactive Display of Global Illumination Solutions for Non-Diffuse Environments.

Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution