nils-herrmann/clean_scirepeval huggingface dataset:
Illumination distribution from brightness in shadows: Adaptive estimation of illumination distribution with unknown reflectance properties in shadow regions

This paper describes a new method for estimating the illumination distribution of a real scene from a radiance distribution inside shadows cast by an object in the scene. First, the illumination distribution of the scene is approximated by discrete sampling of an extended light source. Then the illumination distribution of the scene is estimated from a radiance distribution inside shadows cast by an object of known shape onto another object in the scene. Instead of assuming any particular reflectance properties of the surface inside the shadows, both the illumination distribution of the scene and the reflectance properties of the surface are estimated simultaneously, based on iterative optimization framework. In addition, this paper introduces an adaptive sampling of the illumination distribution of a scene. Rather than using a uniform discretization of the overall illumination distribution, we adaptively increase sampling directions of the illumination distribution based on the estimation at the previous iteration. Using the adaptive sampling framework, we are able to estimate overall illumination more efficiently by using fewer sampling directions. The proposed method is effective for estimating an illumination distribution even under a complex illumination environment.
Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain

Hyperspectral imaging is beneficial to many applications but most traditional methods do not consider fluorescent effects which are present in everyday items ranging from paper to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflection and fluorescence so proper separation of these components is necessary for analyzing them. In recent years, effective imaging methods have been proposed but most require capturing the scene under multiple illuminants. In this paper, we demonstrate efficient separation and recovery of reflectance and fluorescence emission spectra through the use of two high frequency illuminations in the spectral domain. With the obtained fluorescence emission spectra from our high frequency illuminants, we then describe how to estimate the fluorescence absorption spectrum of a material given its emission spectrum. In addition, we provide an in depth analysis of our method and also show that filters can be used in conjunction with standard light sources to generate the required high frequency illuminants. We also test our method under ambient light and demonstrate an application of our method to synthetic relighting of real scenes.
Designing of spectroscopic characteristics in all-ceramic composite with layer-by-layer of Nd:Y/sub 3/ScAl/sub 4/O/sub 12/ and Nd:Y/sub 3/Al/sub 5/O/sub 12/

In this work, we fabricated the all-ceramic composite with layer-by-layer of Nd:Y/sub 3/ScAl/sub 4/O/sub 12/ (YSAG) and Nd:Y/sub 3/Al/sub 5/O/sub 12/ (YAG). We found that the tuning of the pumping wavelength made the spectral profile of fluorescence from the composite ceramics more even than that from single-layered Nd:Y/sub 3/Sc/sub x/Al/sub 5-x/O/sub 12/ ceramics. The technique of composite ceramics is proved to be useful because it can provide not only the compositional tuning but also a new controllability by the selection of the pump source.
Shape-Preserving Half-Projective Warps for Image Stitching

This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation of the overlapping regions into the non-overlapping regions and the resultant warp gradually changes from projective to similarity across the image. The proposed warp has the strengths of both projective and similarity warps. It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps. It can also be combined with more advanced local-warp-based alignment methods such as the as-projective-as-possible warp for better alignment accuracy. With the proposed warp, the field of view can be extended by stitching images with less projective distortion (stretched shapes and enlarged sizes).
Photorefractive effect in iron-doped relaxor Pb(Zn/sub 1/3/Nb/sub 2/3/)O/sub 3/-PbTiO/sub 3/

Large photorefractive effect is observed in Fe-doped 0.91PZN-0.09PT relaxor ferroelectric crystal grown by the flux method. Moreover, the photochromism is induced by the illumination of blue or green laser light.
Person Re-identification via Discriminative Accumulation of Local Features

Metric learning to learn a good distance metric for distinguishing different people while being insensitive to intra-person variations is widely applied to person re-identification. In previous works, local histograms are densely sampled to extract spatially localized information of each person image. The extracted local histograms are then concatenated into one vector that is used as an input of metric learning. However, the dimensionality of such a concatenated vector often becomes large while the number of training samples is limited. This leads to an over fitting problem. In this work, we argue that such a problem of over-fitting comes from that it is each local histogram dimension (e.g. color brightness bin) in the same position is treated separately to examine which part of the image is more discriminative. To solve this problem, we propose a method that analyzes discriminative image positions shared by different local histogram dimensions. A common weight map shared by different dimensions and a distance metric which emphasizes discriminative dimensions in the local histogram are jointly learned with a unified discriminative criterion. Our experiments using four different public datasets confirmed the effectiveness of the proposed method.
High-Speed Digital and Analog Parallel Transmission Technique Over Single Telephone Channel

A new technique for parallel transmission of digital and analog signals over a single telephone channel is presented. The modulation scheme treated in this paper is quadrature amplitude modulation, wherein the digital and analog information are mapped separately as in phase and quadrature components. In the realization of the high-speed parallel transmission, adaptive equalization and carrier phase control are significant problems. In the hybrid QAM modem a two-dimensional error signal cannot be measured accurately at the demodulator output. This causes difficulty in implementing the conventional decision directed circuitry, which is usually employed in the adaptive equalizer and the carrier phase control circuit. The new decision directed schemes which are applicable to parallel transmission are derived by using an error signal measurement defined independent of the analog part. Computer simulation for the parallel transmission scheme of 4.8 kbit/s digital and 2.4 kHz sampled analog sequences is carried out for a typical dispersive telephone channel. Simulation results show that this scheme is useful for high fidelity analog signal transmission.
32*32 shared buffer type ATM switch VLSIs for B-ISDN

A set of 0.8 mu m CMOS VLSIs developed for shared buffer switches in asynchronous transfer mode (ATM) switching systems is described. A unit switch, whose size is 32*32, consists of 8 buffer memory LSIs (BFM-LSIs), 2 memory control LSIs (CTRL-LSIs) and 2 commercially available first in, first out (FIFO) memory LSIs. Using these VLSIs, the 32*32 switch can be mounted on a printed board. This switch is a shared buffer switch. It achieves buffer memory utilization improvement through the use of buffer sharing among all output ports of the switch and has a large 2-Mbit shared buffer which can save 4096 cells. As a result, this switch can satisfy the cell loss rate requirements, not only under random traffic conditions but also under burst traffic conditions. The switch has a priority control function to meet the requirements for various media. It is useful for multimedia ATM networks.<<ETX>>
Eigen-Texture Method: Appearance Compression and Synthesis Based on a 3D Model

Image-based and model-based methods are two representative rendering methods for generating virtual images of objects from their real images. However, both methods still have several drawbacks when we attempt to apply them to mixed reality where we integrate virtual images with real background images. To overcome these difficulties, we propose a new method, which we refer to as the Eigen-Texture method. The proposed 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 generated from a sequence of range images. The Eigen-Texture method is an example of a view-dependent texturing approach which combines the advantages of image-based and model-based approaches. No reflectance analysis of the object surface is needed, while an accurate 3D geometric model facilitates integration with other scenes. The paper describes the method and reports on its implementation.
Appearance-Based Gaze Estimation via Uncalibrated Gaze Pattern Recovery

Aiming at reducing the restrictions due to person/scene dependence, we deliver a novel method that solves appearance-based gaze estimation in a novel fashion. First, we introduce and solve an “uncalibrated gaze pattern” solely from eye images independent of the person and scene. The gaze pattern recovers gaze movements up to only scaling and translation ambiguities, via nonlinear dimension reduction and pixel motion analysis, while no training/calibration is needed. This is new in the literature and enables novel applications. Second, our method allows simple calibrations to align the gaze pattern to any gaze target. This is much simpler than conventional calibrations which rely on sufficient training data to compute person and scene-specific nonlinear gaze mappings. Through various evaluations, we show that: 1) the proposed uncalibrated gaze pattern has novel and broad capabilities; 2) the proposed calibration is simple and efficient, and can be even omitted in some scenarios; and 3) quantitative evaluations produce promising results under various conditions.
Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems

We propose a fast and robust method for tracking a user's hand and multiple fingertips; we then demonstrate gesture recognition based on measured fingertip trajectories for augmented desk interface systems. Our tracking method is capable of tracking multiple fingertips in a reliable manner even in a complex background under a dynamically changing lighting condition without any markers. First, based on its geometrical features, the location of each fingertip is located in each input infrared image frame. Then, correspondences of detected fingertips between successive image frames are determined based on a prediction technique. Our gesture recognition system is particularly advantageous for human-computer interaction (HCI) in that users can achieve interactions based on symbolic gestures at the same time that they perform direct manipulation with their own hands and fingers. The effectiveness of our proposed method has been successfully demonstrated via a number of experiments.
Estimating change in head pose from low resolution video using LBP-based tracking

Head pose estimation from low resolution video can be a difficult task due to the limited quantity and quality of pixels which can be harvested for information. In this work, we propose a method for tracking change in head pose by solving a texture detection problem in an orientation space. In another words, the current head pose can be tracked by employed a local binary pattern based comparison between its texture and several textures estimated from rotating the previous head pose by several known angles. Thus, the selected previous head pose can be regarded as an estimator. To boost the tracking accuracy, multiple estimators, each with different previous head pose, are deployed to produce a combined result. Empirically, the proposed method outperforms the state of the art methods for head pose tracking in low resolution image sequence. Furthermore, the method also performs well on image sequences with varying light.
Uncalibrated Photometric Stereo for Unknown Isotropic Reflectances

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel across multi-illuminance images. We show that for general isotropic materials, the geodesic distance between intensity profiles is linearly related to the angular difference of their surface normals, and that the intensity distribution of an intensity profile conveys information about the reflectance properties, when the intensity profile is obtained under uniformly distributed directional lightings. Based on these observations, we show that surface normals can be estimated up to a convex/concave ambiguity. A solution method based on matrix decomposition with missing data is developed for a reliable estimation. Quantitative and qualitative evaluations of our method are performed using both synthetic and real-world scenes.
Real-Time Fingertip Tracking and Gesture Recognition

Augmented desk interfaces and other virtual reality systems depend on accurate, real-time hand and fingertip tracking for seamless integration between real objects and associated digital information. We introduce a method for discerning fingertip locations in image frames and measuring fingertip trajectories across image frames. We also propose a mechanism for combining direct manipulation and symbolic gestures based on multiple fingertip motions. Our method uses a filtering technique, in addition to detecting fingertips in each image frame, to predict fingertip locations in successive image frames and to examine the correspondences between the predicted locations and detected fingertips. This lets us obtain multiple complex fingertip trajectories in real time and improves fingertip tracking. This method can track multiple fingertips reliably even on a complex background under changing lighting conditions without invasive devices or color markers.
Appearance-Based Gaze Estimation With Online Calibration From Mouse Operations

This paper presents an unconstrained gaze estimation method using an online learning algorithm. We focus on a desktop scenario, where a user operates a personal computer, and use the mouse-clicked positions to infer, where on the screen the user is looking at. Our method continuously captures the user's head pose and eye images with a monocular camera, and each mouse click triggers learning sample acquisition. In order to handle head pose variations, the samples are adaptively clustered according to the estimated head pose. Then, local reconstruction-based gaze estimation models are incrementally updated in each cluster. We conducted a prototype evaluation in real-world environments, and our method achieved an estimation accuracy of 2.9°.
Recovering audio-to-video synchronization by audiovisual correlation analysis

Audio-to-video synchronization (AV-sync) may drift and is difficult to recover without dedicated human effort. In this work, we develop an interactive method to recover the drifted AV-sync by audiovisual correlation analysis. Given a video segment, a user specifies a rough time span during which a person is speaking. Our system first detects a speaker region using face detection. It then does a two-stage search to find the optimum AV-drift that can maximize the average audiovisual correlation inside the speaker region. The correlation is evaluated using quadratic mutual information with kernel density estimation. AV-sync is finally recovered by the detected optimum AV-drift. Experimental results demonstrate the effectiveness of our method.
Novel approach for search engine

This is well known that IoT needs intelligent search engines to manage big data processing. We have been developing such issues with novel algorithms. CAM (Contents Addressable Memory) conducts high speed searching with high power consumption which is not productive. The LUT (Look Up Table) algorithm with certain software is one type of method that is utilized to save power and commonly found in today. However there is possibility of a reduced search speed it using the sequential software method. The method used in our study produces high speed searching with low power due to the address-to-data search algorithm on LUT when compared with the regular data-to-address step. This reversal approach has considered historically. However address collisions occur prolifically and could not be a zero-based probability. In the study of our algorithm developed a zero-based probability process which is accomplished by key data division and only division data searching steps resulted in high speed and low power output.
Fast unsupervised ego-action learning for first-person sports videos

Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering first-person action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster video content. Our approach assumes a completely unsupervised scenario, where labeled training videos are not available, videos are not pre-segmented and the number of ego-action categories are unknown. In our proposed framework we show that a stacked Dirichlet process mixture model can be used to automatically learn a motion histogram codebook and the set of ego-action categories. We quantitatively evaluate our approach on both in-house and public YouTube videos and demonstrate robust ego-action categorization across several sports genres. Comparative analysis shows that our approach outperforms other state-of-the-art topic models with respect to both classification accuracy and computational speed. Preliminary results indicate that on average, the categorical content of a 10 minute video sequence can be indexed in under 5 seconds.
Optimum soft-output detection for channels with intersymbol interference

In contrast to the conventional Viterbi algorithm (VA) which generates hard-outputs, an optimum soft-output algorithm (OSA) is derived under the constraint of fixed decision delay for detection of M-ary digital signals in the presence of intersymbol interference and additive white Gaussian noise. The OSA, a new type of the conventional symbol-by-symbol maximum a posteriori probability algorithm, requires only a forward recursion and the number of variables to be stored and recursively updated increases linearly, rather than exponentially, with the decision delay. Then, with little performance degradation, a suboptimum soft-output algorithm (SSA) is obtained by simplifying the OSA. The main computations in the SSA, as in the VA, are the add-compare-select operations. Simulation results of a convolutional-coded communication system are presented that demonstrate the superiority of the OSA and the SSA over the conventional VA when they are used as detectors. When the decision delay of the detectors equals the channel memory, a significant performance improvement is achieved with only a small increase in computational complexity.
Fast sparse edge-based intrinsic image decomposition guided by chromaticity gradients

In this paper, we proposed a novel edge-based method for intrinsic image decomposition. To be specific, to address this ill-posed problem via single image, we employed the information of chromaticity gradient to guide recovering the reflectance gradient map, and regularized the Retinex assumption by the ℓ0-norm to render the reflectance layer piece-wise smooth. Our method is simple but effective. Qualitative together with quantitative experiments were carried out on some public datasets. The comparison results show that our method runs much faster than the state-of-art methods with comparable performance.
Fast tracking of hands and fingertips in infrared images for augmented desk interface

We introduce a fast and robust method for tracking positions of the centers and the fingertips of both right and left hands. Our method makes use of infrared camera images for reliable detection of a user's hands, and uses a template matching strategy for finding fingertips. This method is an essential part of our augmented desk interface in which a user can, with natural hand gestures, simultaneously manipulate both physical objects and electronically projected objects on a desk, e.g., a textbook and related WWW pages. Previous tracking methods which are typically based on color segmentation or background subtraction simply do not perform well in this type of application because an observed color of human skin and image backgrounds may change significantly due to protection of various objects onto a desk. In contrast, our proposed method was shown to be effective even in such a challenging situation through demonstration in our augmented desk interface. This paper describes the details of our tracking method as well as typical applications in our augmented desk interface.
Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain

Hyper spectral imaging is beneficial to many applications but current methods do not consider fluorescent effects which are present in everyday items ranging from paper, to clothing, to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflectance and fluorescence. So proper separation of these components is necessary for analyzing them. In this paper, we demonstrate efficient separation and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. With the obtained fluorescent emission spectra from our high frequency illuminants, we then present to our knowledge, the first method for estimating the fluorescent absorption spectrum of a material given its emission spectrum. Conventional bispectral measurement of absorption and emission spectra needs to examine all combinations of incident and observed light wavelengths. In contrast, our method requires only two hyper spectral images. The effectiveness of our proposed methods are then evaluated through a combination of simulation and real experiments. We also demonstrate an application of our method to synthetic relighting of real scenes.
Incorporating Long-Term Observations of Human Actions for Stable 3D People Tracking

We propose a method for enhancing the stability of tracking people by incorporating long-term observations of human actions in a scene. Basic human actions, such as walking or standing still, are frequently observed at particular locations in an observation scene. By observing human actions for a long period of time, we can identify regions that are more likely to be occupied by a person. These regions have a high probability of a person existing compared with others. The key idea of our approach is to incorporate this probability as a bias in generating samples under the framework of a particle filter for tracking people. We call this bias the environmental existence map (EEM). The EEM is iteratively updated at every frame by using the tracking results from our tracker, which leads to more stable tracking of people. Our experimental results demonstrate the effectiveness of our method.