This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.

/pdf/automatic-panoramic-image-stitching-using-invariant-features-qz6yy9ngai.pdf

Automatic Panoramic Image Stitching using Invariant Features

A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result

Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About

The dynamic range of many real-world environments exceeds the capabilities of current display technology by several orders of magnitude. In this paper we discuss the design of two different display systems that are capable of displaying images with a dynamic range much more similar to that encountered in the real world. The first display system is based on a combination of an LCD panel and a DLP projector, and can be built from off-the-shelf components. While this design is feasible in a lab setting, the second display system, which relies on a custom-built LED panel instead of the projector, is more suitable for usual office workspaces and commercial applications. We describe the design of both systems as well as the software issues that arise. We also discuss the advantages and disadvantages of the two designs and potential applications for both systems.

https://ws.iat.sfu.ca/papers/hdr_demonstration.pdf

High dynamic range display systems

Systems and methods for implementing array cameras configured to perform super- resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. Lens stack arrays in accordance with many embodiments of the invention include lens elements formed on substrates separated by spacers, where the lens elements, substrates and spacers are configured to form a plurality of optical channels, at least one aperture located within each optical channel, at least one spectral filter located within each optical channel, where each spectral filter is configured to pass a specific spectral band of light, and light blocking materials located within the lens stack array to optically isolate the optical channels.

Capturing and processing of images using monolithic camera array with heterogeneous imagers

Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples - parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.

/pdf/objective-quality-assessment-of-tone-mapped-images-3q1qnzs1dr.pdf

Objective Quality Assessment of Tone-Mapped Images

A display has a first array of pixels connected to emit individually-controlled amounts of light onto a second array of pixels. The second array of pixels is connected to pass individually-controllable portions of the light. The display has a controller connected to control the pixels of the first and second arrays of pixels. The controller is configured to control the pixels of the first array of pixels to provide on the second array of pixels an approximation of an image specified by image data; determine a difference between the image and the approximation of the image at individual pixels of the second array of pixels; and control the pixels of the second array of pixels to modulate the approximation of the image according to the corresponding determined differences.

High dynamic range display devices

Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.

Evaluation of tone mapping operators using a High Dynamic Range display

New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

We have developed an emissive high dynamic range (HDR) display that is capable of displaying a luminance range of 10,000cd/m2 to 0.1cd/m2 while maintaining all features found in conventional LCD displays such as resolution, refresh rate and image quality. We achieve that dynamic range by combining two display systems — a high resolution transmissive LCD and a low resolution, monochrome display composed of high brightness light emitting diodes (LED). This paper provides a description of the technology as well as findings from a supporting psychological study that establishes that correction for the low resolution display through compensation in the high resolution display yields an image which does not differ perceptibly from that of a purely high resolution HDR display.

Helge Seetzen

Papers

High dynamic range display systems

High dynamic range display devices

Evaluation of tone mapping operators using a High Dynamic Range display

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

54.2: A High Dynamic Range Display Using Low and High Resolution Modulators