nils-herrmann/clean_scirepeval huggingface dataset:
Appearance compression and synthesis based on 3D model for mixed reality

Rendering photorealistic virtual objects from their real images is one of the main research issues in mixed reality systems. We previously proposed the Eigen-Texture method (K. Nishino et al., 1999), a new rendering method for generating virtual images of objects from their real images to deal with the problems posed by past work in image based methods and model based methods. Eigen-Texture method samples appearances of a real object under various illumination and viewing conditions, and compresses them in the 2D coordinate system defined on the 3D model surface. However, we had a serious limitation in our system, due to the alignment problem of the 3D model and color images. We deal with this limitation by solving the alignment problem; we do this by using the method originally designed by P. Viola (1995). The paper describes the method and reports on how we implement it.
Inferring human gaze from appearance via adaptive linear regression

The problem of estimating human gaze from eye appearance is regarded as mapping high-dimensional features to low-dimensional target space. Conventional methods require densely obtained training samples on the eye appearance manifold, which results in a tedious calibration stage. In this paper, we introduce an adaptive linear regression (ALR) method for accurate mapping via sparsely collected training samples. The key idea is to adaptively find the subset of training samples where the test sample is most linearly representable. We solve the problem via l1-optimization and thoroughly study the key issues to seek for the best solution for regression. The proposed gaze estimation approach based on ALR is naturally sparse and low-dimensional, giving the ability to infer human gaze from variant resolution eye images using much fewer training samples than existing methods. Especially, the optimization procedure in ALR is extended to solve the subpixel alignment problem simultaneously for low resolution test eye images. Performance of the proposed method is evaluated by extensive experiments against various factors such as number of training samples, feature dimensionality and eye image resolution to verify its effectiveness.
Efficient 1.3-/spl mu/m laser oscillation and heat generation characteristics in Nd:GdVO/sub 4/ laser under direct pumping into /sup 4/F/sub 3/2/ emitting level

We demonstrated a near quantum-limited slope efficiency in 1.3-mum cw Nd:GdVO4 laser under a 879-nm (Ti:Sapphire laser) pumping. Large heat generation due to non-radiative decay in Nd:GdVO4 crystal under non-lasing condition was discussed
Appearance modeling for mixed reality: photometric aspects

Presents an overview of our efforts on mixed reality. These efforts span two aspects: how to create models of virtual objects and how to integrate such virtual objects with real scenes. For model creation, we have developed two methods (the model-based and eigen-texture rendering methods), both of which automatically create models by observing real objects. For the integration of a virtual object with a real scene, we have developed a method that renders virtual objects based on the real illumination distribution. We have successfully tested the proposed methods by using real images to demonstrate their effectiveness.
Learning motion patterns and anomaly detection by Human trajectory analysis

In this paper, we propose a novel method to learn motion patterns and detect anomalies by human trajectory analysis. Human trajectories are various, for example, moving, roaming, pausing, and so on. But, current approaches for the analysis of motion patterns are effective only in understanding simple trajectories. We aim to understand complicated human trajectories with long-term observation. To deal with spatial and temporal features of trajectories, we employ HMM (Hidden Markov Model) to model time-series features of human positions. Next, a similarity matrix of HMM mutual distances is formed. MDS (Multi-Dimensional Scaling) based on eigenvector decomposition provides projected coordinates of trajectories in low-dimensional space. Then we apply k-means clustering to projected data in order to acquire human motion patterns. Anomalies can be detected by the use of likelihood scores for HMM representing motion patterns. We tested the proposed method by real-world trajectories data observed in a small store. Experimental result shows that our method accurately finds typical motion patterns and unusual trajectories.
Object recognition based on photometric alignment using RANSAC

For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models both diffuse reflection components and attached shadows under a distant point light source by using three basis images. However, in order to reliably reproduce these components in a test image, we have to take into account outliers such as specular reflection components and shadows in the test image. Accordingly, our proposed method utilizes Random Sample Consensus (RANSAC), which has been used successfully for estimating basis images. In the present study, we have conducted experiments using the Yale Face Database B and confirmed that a combination of the photometric alignment and RANSAC provides a simple but effective method for object recognition under varying illumination conditions.
Adaptive Linear Regression for Appearance-Based Gaze Estimation

We investigate the appearance-based gaze estimation problem, with respect to its essential difficulty in reducing the number of required training samples, and other practical issues such as slight head motion, image resolution variation, and eye blinking. We cast the problem as mapping high-dimensional eye image features to low-dimensional gaze positions, and propose an adaptive linear regression (ALR) method as the key to our solution. The ALR method adaptively selects an optimal set of sparsest training samples for the gaze estimation via ℓ1-optimization. In this sense, the number of required training samples is significantly reduced for high accuracy estimation. In addition, by adopting the basic ALR objective function, we integrate the gaze estimation, subpixel alignment and blink detection into a unified optimization framework. By solving these problems simultaneously, we successfully handle slight head motion, image resolution variation and eye blinking in appearance-based gaze estimation. We evaluated the proposed method by conducting experiments with multiple users and variant conditions to verify its effectiveness.
Recovering the Basic Structure of Human Activities from a Video-Based Symbol String

In recent years stochastic context-free grammars have been shown to be effective in modeling human activities because of the hierarchical structures they represent. However, most of the research in this area has yet to address the issue of learning the activity grammars from a noisy input source, namely, video. In this paper, we present a framework for identifying noise and recovering the basic activity grammar from a noisy symbol string produced by video. We identify the noise symbols by finding the set of non-noise symbols that optimally compresses the training data, where the optimality of compression is measured using an MDL criterion. We show the robustness of our system to noise and its effectiveness in learning the basic structure of human activity, through an experiment with real video from a local convenience store.
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often times suffer from degradation due to the limited light, which introduces noise into the imaging process. In this paper, we propose an effective model for hyperspectral image (HSI) denoising that considers underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the noisy HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI denoising model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. Experimental results on HSI denoising show that the proposed method can provide substantial improvements over the current state-of-the-art HSI denoising methods in terms of both objective metric and subjective visual quality.
Community detection using random-walk similarity and application to image clustering

The technology used to detect community structures in graphs, or graph clustering technology, is important in a wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community detection methods have relied on the optimization of a quantity referred to as modularity, which is a quality index for the partition of a graph into communities. However, such methods suffer from a key drawback, namely, the inability to identify relatively small communities. To overcome this drawback, we propose a novel community detection method that can detect small communities. This is based on the property that a random walker will not readily leave a community even if it is small. The work presented in this paper demonstrates that our method detects both small and large communities in the practical application of clustering tourist attraction images obtained from Flickr.
Deep learning consideration with novel approach — look-up-table based processing conjugated memory

This paper is the progressing study of our previous one(1) on the IMPACT 2015 named as the MLcS (Memory Logic Conjugated System) that is used for LUT processing. It is researched about how to fit the MLCS for Deep Learning (DL). Nvidia(2), Google(3), Fujitsu(4) Intel-Altera, Intel-Nervana(5) and Renesas(6) recently said that 8bits processing keeps efficient and flexible execution for AI, especially in DL on which we already discussed in the previous study. Perceptron method for DL is examined with algorithmic approach and consequently resulted fairly fitting for DL. A person(6) and common sense have doubted LUT based execution could not replace processing as conventional CPU. Our study also has done by LUT processing to apply to DL that is just memory operation. Therefore all memory system can be achieved the highest IO band and lowest power consumption, high speed.
Radiometric Compensation in a Projector-Camera System Based Properties of Human Vision System

We introduce a novel technique for performing radiometric compensation for a projector-camera system that projects images onto a textured planar surface, which is designed to minimize perceptual artifacts visible to observers according to a model of the human vision system. A projector-camera system has previously been proposed for projecting images onto an arbitrary surface using radiometric compensation, however the dynamic range of a projector is physically limited there are some textures that cannot be compensated correctly. Also, human eyes are sensitive to artifacts introduced in this way. Our technique is designed to provide compensated images with perceptually less noticeable artifacts while preserving enough brightness and contrast in the output. We develop an optimization framework based on a perceptually-based physical error metric to minimize perceptible artifacts in the final compensated images by compressing the contrast of the input images.
SymPS: BRDF Symmetry Guided Photometric Stereo for Shape and Light Source Estimation

We propose uncalibrated photometric stereo methods that address the problem due to unknown isotropic reflectance. At the core of our methods is the notion of “constrained half-vector symmetry” for general isotropic BRDFs. We show that such symmetry can be observed in various real-world materials, and it leads to new techniques for shape and light source estimation. Based on the 1D and 2D representations of the symmetry, we propose two methods for surface normal estimation; one focuses on accurate elevation angle recovery for surface normals when the light sources only cover the visible hemisphere, and the other for comprehensive surface normal optimization in the case that the light sources are also non-uniformly distributed. The proposed robust light source estimation method also plays an essential role to let our methods work in an uncalibrated manner with good accuracy. Quantitative evaluations are conducted with both synthetic and real-world scenes, which produce the state-of-the-art accuracy for all of the non-Lambertian materials in MERL database and the real-world datasets.
Driving skill analysis using machine learning The full curve and curve segmented cases

Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.
Steerable Projector Calibration

A steerable projector is a digital projector whose beam can be moved under computer control to illuminate different objects in its environment. Various projects have explored the possibilities of steerable projectors but they have not addressed the calibration of a generalized optical and mechanical system. We present a method for calibrating a device comprised of a projector and a pan-tilt mirror. It starts by obtaining the internal parameters of a camera and the projector, then the pan-tilt mirror is placed in a series of positions and for each one the reflected projector position is calculated from a pattern projected onto a planar surface. From those readings a coarse version of the steerable projector parameters is obtained, which is then iteratively refined. We describe real results from our physical setup and a range of results from simulated data to characterize the performance of our algorithm.
Thermal properties of Y3Al5O12, GdVO4, and YVO4

We have measured thermal conductivity of Y<sub>3</sub>Al<sub>5</sub>O<sub>12</sub>, GdVO<sub>4</sub>, and YVO<sub>4</sub>. It was found that YVO<sub>4</sub> has larger thermal conductivity than GdVO<sub>4</sub>. Nd<sup>3+</sup>-doping effect in thermal conductivity and thermal expansion ratio have been also discussed.
Camera spectral sensitivity estimation from a single image under unknown illumination by using fluorescence

Camera spectral sensitivity plays an important role for various color-based computer vision tasks. Although several methods have been proposed to estimate it, their applicability is severely restricted by the requirement for a known illumination spectrum. In this work, we present a single-image estimation method using fluorescence with no requirement for a known illumination spectrum. Under different illuminations, the spectral distributions of fluorescence emitted from the same material remain unchanged up to a certain scale. Thus, a camera's response to the fluorescence would have the same chromaticity. Making use of this chromaticity invariance, the camera spectral sensitivity can be estimated under an arbitrary illumination whose spectrum is unknown. Through extensive experiments, we proved that our method is accurate under different illuminations. Moreover, we show how to recover the spectra of daylight from the estimated results. Finally, we use the estimated camera spectral sensitivities and daylight spectra to solve color correction problems.
Surface Reconstruction in Photometric Stereo with Calibration Error

A method is described for surface reconstruction that accounts for the calibration errors in photometric stereo. The angular errors in calibrated light directions due to noise cause errors in surface normal estimates. Investigation of the effect of these calibration errors on the surface normals revealed that errors in the estimated light directions and in the surface normal estimates follow a Fisher distribution. By accounting for the Fisher noise in surface normals, the proposed method reconstructs a surface using maximum likelihood estimation. Extensive comparison with previous methods using synthetic and real scenes demonstrated that the proposed method outperforms them in the presence of calibration errors.
Gaze Estimation From Eye Appearance: A Head Pose-Free Method via Eye Image Synthesis

In this paper, we address the problem of free head motion in appearance-based gaze estimation. This problem remains challenging because head motion changes eye appearance significantly, and thus, training images captured for an original head pose cannot handle test images captured for other head poses. To overcome this difficulty, we propose a novel gaze estimation method that handles free head motion via eye image synthesis based on a single camera. Compared with conventional fixed head pose methods with original training images, our method only captures four additional eye images under four reference head poses, and then, precisely synthesizes new training images for other unseen head poses in estimation. To this end, we propose a single-directional (SD) flow model to efficiently handle eye image variations due to head motion. We show how to estimate SD flows for reference head poses first, and then use them to produce new SD flows for training image synthesis. Finally, with synthetic training images, joint optimization is applied that simultaneously solves an eye image alignment and a gaze estimation. Evaluation of the method was conducted through experiments to assess its performance and demonstrate its effectiveness.
Joint Recovery of Dense Correspondence and Cosegmentation in Two Images

We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images. Our method parameterizes the correspondence field using piecewise similarity transformations and recovers a mapping between the estimated common "foreground" regions in the two images allowing them to be precisely aligned. Our formulation is based on a hierarchical Markov random field model with segmentation and transformation labels. The hierarchical structure uses nested image regions to constrain inference across multiple scales. Unlike prior hierarchical methods which assume that the structure is given, our proposed iterative technique dynamically recovers the structure along with the labeling. This joint inference is performed in an energy minimization framework using iterated graph cuts. We evaluate our method on a new dataset of 400 image pairs with manually obtained ground truth, where it outperforms state-of-the-art methods designed specifically for either cosegmentation or correspondence estimation.
Deleted Interpolation Using a Hierarchical Bayesian Grammar Network for Recognizing Human Activity

From the viewpoint of an intelligent video surveillance system, the high-level recognition of human activity requires a priori hierarchical domain knowledge as well as a means of reasoning based on that knowledge. We approach the problem of human activity recognition based on the understanding that activities are hierarchical, temporally constrained and temporally overlapped. While stochastic grammars and graphical models have been widely used for the recognition of human activity, methods combining hierarchy and complex queries have been limited. We propose a new method of merging and implementing the advantages of both approaches to recognize activities in real-time. To address the hierarchical nature of human activity recognition, we implement a hierarchical Bayesian network (HBN) based on a stochastic context-free grammar (SCFG). The HBN is applied to digressive substrings of the current string of evidence via deleted interpolation (DI) to calculate the probability distribution of overlapped activities in the current string. Preliminary results from the analysis of activity sequences from a video surveillance camera show the validity of our approach.
Robust transmission method in KHz-band PLC

This paper describes a robust transmission method for KHz-band PLC. The method is robust for heavy interference from electric appliances. Interference is not always stochastic in nature, as it is an artificial event. Thus, any transmission method based on Shannon's theory will not always yield reliable results. The robust transmission method proposed in this paper is to detect the correct symbol when the interference generated by many electric appliances fades out. However, interference-free zones cannot be determined within drastically time-variant interference envelopes, and they cannot be detected directly from the shape of the received signal. Interference-free zones can only be identified if an adaptive receiver outputs the true symbol. In order to solve this dilemma, we introduce a blind equalizer with rapid convergence. In addition, we employ a code that has symbol-position information built into the symbol sequence of the data frame. Through the combination of these two methods, we can maintain connections even when fading-out intervals are very short, by exploiting speed-reductions in the information rate. Implementing this method on a FPGA, we demonstrate that robustness can be realized even under the most problematic case of real KHz-band PLC networks. Application of this technique is vital for building dependable systems to control for electric appliances.
Development of touch panel system by single layer without pattern: Capable large touch panel due to large S/N and simple structure

We propose a novel touch panel structure, sensing method, and system which composes a low-cost transparent electrically-conductive thin film with higher Rs than the ITO film by two orders of magnitude. Relatively small number of extraction electrodes is directly attached upon the thin film without any circuit pattern. The proposed panel can realize even the wall tapestry-type touch panel of larger area as well as the smaller size one, and also can play single and multi-touch operations even through gloved fingers. In this paper, we will mainly mention about this packaging technology.
Appearance-Based Gaze Estimation Using Visual Saliency

We propose a gaze sensing method using visual saliency maps that does not need explicit personal calibration. Our goal is to create a gaze estimator using only the eye images captured from a person watching a video clip. Our method treats the saliency maps of the video frames as the probability distributions of the gaze points. We aggregate the saliency maps based on the similarity in eye images to efficiently identify the gaze points from the saliency maps. We establish a mapping between the eye images to the gaze points by using Gaussian process regression. In addition, we use a feedback loop from the gaze estimator to refine the gaze probability maps to improve the accuracy of the gaze estimation. The experimental results show that the proposed method works well with different people and video clips and achieves a 3.5-degree accuracy, which is sufficient for estimating a user's attention on a display.