There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Flow The Psychology Of Optimal Experience

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Methods of manufacturing a liquid crystal device including depositing a layer of liquid crystal material on a substrate and imprinting a pattern on the layer of liquid crystal material using an imprint template are disclosed. The liquid crystal material can be jet deposited. The imprint template can include surface relief features, Pancharatnam-Berry Phase Effect (PBPE) structures or diffractive structures. The liquid crystal device manufactured by the methods described herein can be used to manipulate light, such as for beam steering, wavefront shaping, separating wavelengths and/or polarizations, and combining different wavelengths and/or polarizations.

Virtual and augmented reality systems and methods

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

The Liver Tumor Segmentation Benchmark (LiTS)

A deep ultraviolet plasmonic structure is designed and a surface plasmon interference lithography method using the structure is proposed to generate large-area periodic nanopatterns. By exciting the anti-symmetric coupled surface plasmon polaritons in the structure, ultrahigh resolution periodic patterns can be formed in a photoresist. The resolution of the generated patterns can be tuned by changing the refractive index and thickness of the photoresist. We demonstrate numerically that one-dimensional and two-dimensional patterns with a half-pitch resolution of 14.6 nm can be generated in a 25 nm-thick photoresist by using the structure under 193 nm illumination. Furthermore, the half-pitch resolution of the generated patterns can be down to 13 nm if high refractive index photoresists are used. Our findings open up an avenue to push the half-pitch resolution of photolithography towards 10 nm.

/pdf/pushing-the-resolution-of-photolithography-down-to-15nm-by-vnn0ez1mzj.pdf

Pushing the resolution of photolithography down to 15nm by surface plasmon interference.

A new type of light field display is proposed using a head-mounted display (HMD) and a micro structure array (MSA, lens array or pinhole array). Each rendering point emits abundant rays from different directions into the viewer’s pupil, and at one time the dense light field is generated inside the exit pupil of the HMD through the eyepiece. Therefore, the proposed method not only solves the problem of accommodation and convergence conflict in a traditional HMD, but also drastically reduces the huge data in real three-dimensional (3D) display. To demonstrate the proposed method, a prototype is developed, which is capable of giving the observer a real perception of depth.

Light field head-mounted display with correct focus cue using micro structure array

A display apparatus includes a planar waveguide optical element, a projection optical system, and a microdisplay device. By including free-form optics and waveguide technology, the volume and weight of the display apparatus can be reduced, and an optical system can be realized with improved image quality, structure, and performance parameters.

Display apparatus and system and display method thereof

Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. Considering the characteristics of both Rician noise and the NLM filter, this study proposes a frame for a pre-smoothing NLM (PSNLM) filter combined with image transformation. In the PSNLM frame, noisy MRI is first transformed into an image in which noise can be treated as additive noise. Second, the transformed MRI is pre-smoothed via a traditional denoising method. Third, the NLM filter is applied to the transformed MRI, with weights that are computed from the pre-smoothed image. Finally, inverse transformation is performed on the denoised MRI to obtain the denoising results. To test the performance of the proposed method, both simulated and real patient data are used, and various pre-smoothing (Gaussian, median, and anisotropic filters) and image transformation [squared magnitude of the MRI, and forward and inverse variance-stabilizing trans-formations (VST)] methods are used to reduce noise. The performance of the proposed method is evaluated through visual inspection and quantitative comparison of the peak signal-to-noise ratio of the simulated data. The real data include Alzheimer’s disease patients and normal controls. For the real patient data, the performance of the proposed method is evaluated by detecting atrophy regions in the hippocampus and the parahippocampal gyrus. The comparison of the experimental results demonstrates that using a Gaussian pre-smoothing filter and VST produce the best results for the peak signal-to-noise ratio (PSNR) and atrophy detection.

/pdf/brain-mr-image-denoising-for-rician-noise-using-pre-smooth-26dcoiudub.pdf

Brain MR image denoising for Rician noise using pre-smooth non-local means filter

Accurate segmentation of coronary arteries in X-ray angiograms is an important step for the quantitative study of coronary artery disease. However, accurate segmentation is a challenging task because coronary arteries are thin tubular structures with relatively low contrast and the presence of artifacts. In this paper, a novel deep-learning-based method is proposed to automatically segment the coronary artery from angiograms by using multichannel fully convolutional networks. Since the artifacts appear in both live images (after the injection of contrast material) and mask images (before the injection of contrast material) and the blood vessels appear only in live images, we take the mask images into consideration to distinguish real blood vessel structures from artifacts. Therefore, both live images and mask images are used as multichannel inputs to provide enhanced vascular structure information. The hierarchical features are then automatically learned to characterize the spatial associations between vessel and background and are further used to achieve the final segmentation. In addition, a dense matching between the live image and mask image is processed for a precise initial alignment. The experimental results demonstrate that our method is effective and robust for coronary artery segmentation, compared with several state-of-the-art methods.

Yongtian Wang

Papers

Pushing the resolution of photolithography down to 15nm by surface plasmon interference.

Light field head-mounted display with correct focus cue using micro structure array

Display apparatus and system and display method thereof

Brain MR image denoising for Rician noise using pre-smooth non-local means filter

Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-Ray Angiograms