nils-herrmann/clean_scirepeval huggingface dataset:
Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints

Cell detection in microscopy images is essential for automated cell behavior analysis including cell shape analysis and cell tracking. Robust cell detection in high-density and low-contrast images is still challenging since cells often touch and partially overlap, forming a cell cluster with blurry intercellular boundaries. In such cases, current methods tend to detect multiple cells as a cluster. If the control parameters are adjusted to separate the touching cells, other problems often occur: a single cell may be segmented into several regions, and cells in low-intensity regions may not be detected. To solve these problems, we first detect redundant candidate regions, which include many false positives but in turn very few false negatives, by allowing candidate regions to overlap with each other. Next, the score for how likely the candidate region contains the main part of a single cell is computed for each cell candidate using supervised learning. Then we select an optimal set of cell regions from the redundant regions under nonoverlapping constraints, where each selected region looks like a single cell and the selected regions do not overlap. We formulate this problem of optimal region selection as a binary linear programming problem under nonoverlapping constraints. We demonstrated the effectiveness of our method for several types of cells in microscopy images. Our method performed better than five representative methods, achieving an F-measure of over 0.9 for all data sets. Experimental application of the proposed method to 3-D images demonstrated that also works well for 3-D cell detection.
Comparative study on the spectroscopic properties of Nd:GdVO/sub 4/ and Nd:YVO/sub 4/ with hybrid process

We have proposed a hybrid procedure for determining spectroscopic parameters for uniaxial solid-state laser crystals. Using our procedure, the spectroscopic properties of Nd:GdVO/sub 4/ were evaluated and compared to those of Nd:YVO/sub 4/. As a result, the peaks of absorption and stimulated emission cross sections of Nd:GdVO/sub 4/ in /spl pi/-polarization were determined to be 2.6 and 10.3/spl times/10/sup -19/ cm/sup 2/, respectively, and were smaller than those of Nd:YVO/sub 4/. On the other hand, the fluorescence lifetime of 1 at% Nd:GdVO/sub 4/ was evaluated to be 83.4 /spl mu/s, and was similar to 84.1 /spl mu/s of 1 at% Nd:YVO/sub 4/. Therefore, the product of stimulated emission cross section and fluorescence lifetime (/spl sigma//sub em//spl tau//sub f/ product) of Nd:GdVO/sub 4/ was smaller than that of Nd:YVO/sub 4/ under 1 at% of Nd/sup 3+/ doping concentration. The radiative lifetime of spontaneous emission of Nd:GdVO/sub 4/ was 168 /spl mu/s and was 1.9 times longer than that of Nd:YVO/sub 4/. Because of the low value of radiative quantum efficiency of Nd:GdVO/sub 4/ (50%), careful cavity design is required for creating a well performing solid-state laser with Nd:GdVO/sub 4/, based on the larger /spl sigma//sub em//spl tau//sub f/ product rather than the /spl sigma//sub em//spl tau//sub f/ product of Nd:YAG.
Hierarchical Gaussian Descriptor for Person Re-identification

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification. In this paper, we present a novel descriptor based on a hierarchical distribution of pixel features. A hierarchical covariance descriptor has been successfully applied for image classification. However, the mean information of pixel features, which is absent in covariance, tends to be major discriminative information of person images. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, we model the region as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, unlike the hierarchical covariance descriptor, the proposed descriptor can model both the mean and the covariance information of pixel features properly. The results of experiments conducted on five databases indicate that the proposed descriptor exhibits remarkably high performance which outperforms the state-of-the-art descriptors for person re-identification.
Ego-surfing first person videos

We envision a future time when wearable cameras (e.g., small cameras in glasses or pinned on a shirt collar) are worn by the masses and record first-person point-of-view (POV) videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera - with or without consent. Motivated by these benefits and risks, we develop a self-search technique tailored to first-person POV videos. The key observation of our work is that the egocentric head motions of a target person (i.e., the self) are observed both in the POV video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to uniquely identify instances of the self. We incorporate this feature into our proposed approach that computes the motion correlation over supervoxel hierarchies to localize target instances in observer videos. Our proposed approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, automated video collection and social group discovery.
Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait

In this work, we propose a method for tracking individuals in crowds. Our method is based on a trajectory-based clustering approach that groups trajectories of image features that belong to the same person. The key novelty of our method is to make use of a person's individuality, that is, the gait features and the temporal consistency of local appearance to track each individual in a crowd. Gait features in the frequency domain have been shown to be an effective biometric cue in discriminating between individuals, and our method uses such features for tracking people in crowds for the first time. Unlike existing trajectory-based tracking methods, our method evaluates the dissimilarity of trajectories with respect to a group of three adjacent trajectories. In this way, we incorporate the temporal consistency of local patch appearance to differentiate trajectories of multiple people moving in close proximity. Our experiments show that the use of gait features and the temporal consistency of local appearance contributes to significant performance improvement in tracking people in crowded scenes.
Hyperspectral Image Super-Resolution With a Mosaic RGB Image

Recently, many hyperspectral (HS) image super-resolution methods that merge a low spatial resolution HS image and a high spatial resolution three-channel RGB image have been proposed in spectral imaging. A largely ignored fact is that most existing commercial RGB cameras capture high resolution images by a single CCD/CMOS sensor equipped with a color filter array. In this paper, we account for the common imaging mechanism of commercial RGB cameras, and propose to use a mosaic RGB image for HS image super-resolution, which prevents demosaicing error and thus its propagation into the HS image super-resolution results. We design a proper non-local low-rank regularization to exploit the intrinsic properties—rich self-repeating patterns and high correlation across spectra—within HS images of natural scenes, and formulate the HS image super-resolution task into a variational optimization problem, which can be efficiently solved via the alternating direction method of multipliers. The effectiveness of the proposed method has been evaluated on two benchmark data sets, demonstrating that the proposed method can provide substantial improvement over the current state-of-the-art HS image super-resolution methods without considering the mosaicing effect. Finally, we show that our method can also perform well in the real capture system.
Reflectance estimation from motion under complex illumination

In this paper, we propose a method for recovering the reflectance properties of a moving Lambertian object from an image sequence of the object taken by a fixed camera under unknown, complex illumination. Our proposed method is based on the spherical-harmonic representation of Lambertian reflectance under arbitrary illumination. Then, by combining the geometry reconstructed by shape-from-motion (SFM), we recover the albedo of the object and the illumination distribution only from the image sequence of the moving object. The proposed method enables us to synthesize realistic images of the object in arbitrary poses under arbitrary lighting conditions by using reconstructed shape and albedo. We conducted a number of experiments by using both synthetic and real images to confirm the effectiveness of our proposed method.
Using extended light sources for modeling object appearance under varying illumination

In this study, we demonstrate the effectiveness of using extended light sources for modeling the appearance of an object for varying illumination. Extended light sources have a radiance distribution that is similar to that of the Gaussian function and have the potential of functioning as a low-pass filter when the appearance of an object is sampled under them. This enables us to obtain a set of basis images of an object for variable illumination from input images of the object taken under those light sources without suffering aliasing caused by insufficient sampling of its appearance. Furthermore, extended light sources are useful in terms of reducing high contrast in image intensities due to specular and diffuse reflection components. This helps us observe both specular and diffuse reflection components of an object in the same image taken with a single shutter speed. We have tested our proposed approach based on extended light sources with objects of complex appearance that are generally difficult to model using image-based modeling techniques.
Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Blur is often present in real-world images and significantly affects the performance of face recognition systems. To improve the recognition of blurred faces, we propose a new approach which inherits the advantages of two recent methods. The idea consists of first reducing the amount of blur in the images via deblurring and then extracting blur-tolerant descriptors for recognition. We assess our analysis on real blurred face images (FRGC 1.0 database) and also on face images artificially degraded by focus blur (FERET database), demonstrating significant performance enhancement compared to the state-of-the-art.
Ego-Surfing: Person Localization in First-Person Videos Using Ego-Motion Signatures

We envision a future time when wearable cameras are worn by the masses and recording first-person point-of-view videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera—with or without consent. Motivated by these benefits and risks, we developed a self-search technique tailored to first-person videos. The key observation of our work is that the egocentric head motion of a target person (i.e., the self) is observed both in the point-of-view video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to identify instances of the self uniquely. We incorporate this feature into the proposed approach that computes the motion correlation over densely-sampled trajectories to search for a target individual in observer videos. Our approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, target video retrieval, and social group clustering.
The study of thermo-mechanical and -optical properties of GdVO4 and YVO4

In this work we summarized thermal characteristics of YVO<sub>4</sub> with comparative study of GdVO<sub>4</sub> by using of carefully evaluated kappa, alpha, dn/dT, thermal heat capacity coefficient dC<sub>p</sub>/dT, and thermal conductivity coefficient dkappa/dT. In conclusion, we summarized thermo-mechanical and -optical parameters of GdVO<sub>4</sub> and YVO<sub>4</sub>, and we found that the performance of YVO<sub>4</sub> was thermally comparable to GdVO<sub>4</sub> from the discussion by using of the figures of merits as kappa/alpha and kappa/(dn/dT).
Saturation factors of pump absorption in solid-state lasers

Novel models for the saturation factors of absorption under optical pumping in solid-state lasers are derived, and these factors depend on the pumping scheme. We investigated the pump saturation effect in Nd:YVO/sub 4/ and found that the bleaching of pump absorption due to the depletion of a ground-state level in a direct pumping scheme was more effective than that in a conventional four-level pumping scheme. However, the saturation effect was recovered by lasing.
Arrangement planning for multiple self-moving trays in human supporting production cell "attentive workbench"

We have proposed "attentive workbench (AWB)", a new cell production system in which an intelligent system supports human workers. Recognizing worker's condition and intention, the system supports workers from both physical and information aspects. This paper deals with physical assembly support using self-moving parts trays. The system delivers necessary assembly parts to workers and clears finished products quickly. On transporting large products, multiple trays are subjected to form a rigid body working as a large single tray. In order to realize this arrangement of trays in real-time, a planning method based on the priority scheme and heuristic rule is proposed. The present method is evaluated through simulations. A demonstration of assembly support using real self-moving trays is shown.
High speed, flexible, robust and low power processing approach

We are all longing for high speed, flexible, robust and low power processing system in the world. FPGA architecture is gathering attention due to one order less magnitude of power saving as one of non-Neumann approach. Other approach is like unified block included small logic, small memory and small controller which is announced by eASIC, nextrame-2/3(1) or Synopsis, EVP(2) as examples which are instead of many core Neumann architecture. These trends originate due to extremely lower power operation and elimination of band width wall between logic and memory. Our study is also for the same unified block that makes only memory with look up table (LUT) and small controller configuration(3). Using no logic circuit means LUT instead that is named “Memory-Logic Conjugated System (MLCS)”. The key of the concept is to make minimal function cluster of LUT array by SRAM circuit base that is significant to keep the highest efficiency. This can be seen one time to logic and other time to memory depending on the dynamical change of need. Dynamic reconfiguration can be led in only several clocks, that produces flexible system. Huge or tinny configurations are designed fairly easy to proper block arrangement by each application. These are also coming true redundant systems followed robust. As constructed memory, running power is very low as two orders less of magnitude than Neumann processor. High speed processing can be done by elimination of the band width wall because neighboring block job area is easy to communicate with each other. This will be presented with some of detail.
Early facial expression recognition using early RankBoost

This work investigated a new challenging problem: how to recognize facial expressions as early as possible, in contrast to finding ways to improve the facial expression recognition rate. Unlike conventional facial expression recognition, early facial expression recognition is inherently difficult due to the initial low intensity of the expressions. To overcome this problem, a novel early recognition approach based on RankBoost is used to infer the facial expression category of an input facial expression sequence as early as possible. Facial expression intensity increases monotonically from neutral to apex in most cases, and this observation was elaborated for developing an early facial expression recognition method. To identify the most discriminative features of subtle facial expressions, weak rankers are used to learn the temporal variations of pairwise subtle facial expression features in accordance with their temporal order. Then, a weight propagation method is applied to boost a weak ranker into an early recognizer. Experiments on the Cohn-Kanade database and a custom-made dataset built using a high-speed motion capture system demonstrated that the proposed method has promising performance for early facial expression recognition.
Fast Multi-frame Stereo Scene Flow with Motion Segmentation

We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks &#x2013; stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [30], which is currently ranked second on the KITTI benchmark.
Laser Properties of Composite Nd:GdVO4 Single Crystal Grown by The Double Die EFG Method

Composite structures in GdVO<sub>4</sub> single crystals were directly grown by double die EFG method, which enables 3-mm core of Nd:GdVO<sub>4</sub> inside 5-mm clad of pure GdVO<sub>4</sub>. Laser oscillation of our composite was successfully demonstrated.
Illumination from Shadows

In this paper, we introduce a method for recovering an illumination distribution of a scene from image brightness inside shadows cast by an object of known shape in the scene. In a natural illumination condition, a scene includes both direct and indirect illumination distributed in a complex way, and it is often difficult to recover an illumination distribution from image brightness observed on an object surface. The main reason for this difficulty is that there is usually not adequate variation in the image brightness observed on the object surface to reflect the subtle characteristics of the entire illumination. In this study, we demonstrate the effectiveness of using occluding information of incoming light in estimating an illumination distribution of a scene. Shadows in a real scene are caused by the occlusion of incoming light and, thus, analyzing the relationships between the image brightness and the occlusions of incoming light enables us to reliably estimate an illumination distribution of a scene even in a complex illumination environment. This study further concerns the following two issues that need to be addressed. First, the method combines the illumination analysis with an estimation of the reflectance properties of a shadow surface. This makes the method applicable to the case where reflectance properties of a surface are not known a priori and enlarges the variety of images applicable to the method. Second, we introduce an adaptive sampling framework for efficient estimation of illumination distribution. Using this framework, we are able to avoid a unnecessarily dense sampling of the illumination and can estimate the entire illumination distribution more efficiently with a smaller number of sampling directions of the illumination distribution. To demonstrate the effectiveness of the proposed method, we have successfully tested the proposed method by using sets of real images taken in natural illumination conditions with different surface materials of shadow regions.
Temporal Localization and Spatial Segmentation of Joint Attention in Multiple First-Person Videos

This work aims to develop a computer-vision technique for understanding objects jointly attended by a group of people during social interactions. As a key tool to discover such objects of joint attention, we rely on a collection of wearable eye-tracking cameras that provide a first-person video of interaction scenes and points-of-gaze data of interacting parties. Technically, we propose a hierarchical conditional random field-based model that can 1) localize events of joint attention temporally and 2) segment objects of joint attention spatially. We show that by alternating these two procedures, objects of joint attention can be discovered reliably even from cluttered scenes and noisy points-of-gaze data. Experimental results demonstrate that our approach outperforms several state-of-the-art methods for co-segmentation and joint attention discovery.
Calibration-free gaze sensing using saliency maps

We propose a calibration-free gaze sensing method using visual saliency maps. Our goal is to construct a gaze estimator only using eye images captured from a person watching a video clip. The key is treating saliency maps of the video frames as probability distributions of gaze points. To efficiently identify gaze points from saliency maps, we aggregate saliency maps based on the similarity of eye appearances. We establish mapping between eye images to gaze points by Gaussian process regression. The experimental result shows that the proposed method works well with different people and video clips and achieves 6 degrees of accuracy, which is useful for estimating a person's attention on monitors.
Acquiring a Radiance Distribution to Superimpose Virtual Objects onto Real Scene

This paper describes a new method for superimposing virtual objects with correct shadings onto an image of a real scene. Unlike the previously proposed methods, our method can measure a radiance distribution of a real scene automatically and use it for superimposing virtual objects appropriately onto a real scene. First, a geometric model of the scene is constructed from a pair of omnidirectional images by using an omnidirectional stereo algorithm. Then, radiance of the scene is computed from a sequence of omnidirectional images taken with different shutter speeds and mapped onto the constructed geometric model. The radiance distribution mapped onto the geometric model is used for rendering virtual objects superimposed onto the scene image. As a result, even for a complex radiance distribution, our method can superimpose virtual objects with convincing shadings and shadows cast onto the real scene. We successfully tested the proposed method by using real images to show its effectiveness.