nils-herrmann/clean_scirepeval huggingface dataset:
High dynamic range imaging by a rank-1 constraint

We present a high dynamic range (HDR) imaging algorithm that utilizes a modern rank minimization framework. Linear dependency exists among low dynamic range (LDR) images. However, global or local misalignment by camera motion and moving objects breaks down the low-rank structure of LDR images. The proposed algorithm simultaneously estimates global geometric transforms to align LDR images and detects moving objects and under-/over-exposed regions using a rank minimization approach. In the HDR composition step, structural consistency weighting is proposed to generate an artifact-free HDR image from an user-selected reference image. We demonstrate the robustness and effectiveness of the proposed method with real datasets.
A new camera calibration method for robotic applications

In this paper, we present a camera calibration method using two views of three pairs of concentric circles with known sizes. A new invariant property for circle is introduced to determine the position of the center of the projected circle. Given two image ellipses and their corresponding centers, a cross-ratio based method estimates the center of projected circles using the new invariant property. The accurate center of projected concentric circles provides correct correspondences between ellipses in the image plane and circles in 3D plane. We also demonstrate that two views of three pairs of coplanar concentric circles are enough to determine the intrinsic camera parameters, such as the focal length, the aspect ratio, and the principal point. We validate the performance of the method using both synthetic and real images. Our method shows a comparable performance with respect to similar calibration methods using planes. The use of concentric circles, however, provides correct correspondences between 3D target points and their image points, and greatly simplifies the calibration problem.
Metric localization using a single artificial landmark for indoor mobile robots

We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time.
High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination

We present a novel framework to estimate detailed shape of diffuse objects with uniform albedo from a single RGB-D image. To estimate accurate lighting in natural illumination environment, we introduce a general lighting model consisting of two components: global and local models. The global lighting model is estimated from the RGB-D input using the low-dimensional characteristic of a diffuse reflectance model. The local lighting model represents spatially varying illumination and it is estimated by using the smoothly-varying characteristic of illumination. With both the global and local lighting model, we can estimate complex lighting variations in uncontrolled natural illumination conditions accurately. For high quality shape capture, a shape-from-shading approach is applied with the estimated lighting model. Since the entire process is done with a single RGB-D input, our method is capable of capturing the high quality shape details of a dynamic object under natural illumination. Experimental results demonstrate the feasibility and effectiveness of our method that dramatically improves shape details of the rough depth input.
Light-Field Image Super-Resolution Using Convolutional Neural Network

Commercial light field cameras provide spatial and angular information, but their limited resolution becomes an important problem in practical use. In this letter, we present a novel method for light field image super-resolution (SR) to simultaneously up-sample both the spatial and angular resolutions of a light field image via a deep convolutional neural network. We first augment the spatial resolution of each subaperture image by a spatial SR network, then novel views between super-resolved subaperture images are generated by three different angular SR networks according to the novel view locations. We improve both the efficiency of training and the quality of angular SR results by using weight sharing. In addition, we provide a new light field image dataset for training and validating the network. We train our whole network end-to-end, and show state-of-the-art performances on quantitative and qualitative evaluations.
An approach for local feature evaluation

In this paper, we introduce a unified feature evaluation toolbox, which contains several state of the art local features such as SIFT, SURF, KAZE, ORB, BRISK, and BIO. With our toolbox, we perform standard evaluation metrics (repeatability, precision-recall), but also the more practical assessment in terms of vision applications. In this version, we provide a refined evaluation methodology of image stitching and depth propagation. We will open the toolbox, and hope to make a various state of the art computer vision and robotics applications.
A stabilized snake constraint for tracking object boundaries

A discrete dynamic model for tracking contours in object boundaries is presented. An active contour in this objective is optimized by a dynamic programming algorithm, for which a new constraint that has simple and stable properties is introduced. The internal energy of the model depends on local behavior of the contour, while the external energy is derived from image features. The algorithm is able to stabilize boundary tracking and well maintain position consistency of snake's control points between two consecutive frames. The authors prove the constraint is useful to stabilize not only the normal motion of the tracked curve but also a planar motion of the curve.
Hierarchical 3D line restoration based on angular proximity in structured environments

We present a method based on a hierarchical clustering to restore the 3D lines of structured environments. In previous approaches, the restoration of noisy 3D lines is a challenging problem because it is difficult to define a suitable similarity measure discriminative to other lines. Our motivation to overcome the difficulty is that most structured scenes consist of sets of parallel 3D lines with the same angular proximity, which provides a hierarchical similarity measure for structured 3D lines. Accordingly, our restoration method works in a manner that clustering is hierarchically performed on angular and distance levels. The 3D line restoration is then achieved by finding the center of each cluster. The framework also makes the clustered 3D lines align along the associated angular directions. We compare the proposed algorithm with methods using no knowledge of the angular information, and demonstrate its effectiveness through real-world experiments.
Patch-based robust L1 tracker to dynamic appearance change

In this paper, we propose a robust l1 tracking method based on a two phases sparse representation, which consists of a patch and a global appearance trackers. While recently proposed l1 trackers showed impressive tracking accuracies, tracking the dynamic appearance is not easy to them. To overcome dynamic appearance change and achieve robust visual tracking, we model the dynamic appearance of the object by a set of local rigid patches and enhance the distinctiveness of the global appearance tracker by positive/negative learning. The integration of two approaches makes visual tracking robust to occlusion and illumination variation. We demonstrate the experiments with five challenging video sequences and compare with state-of-art trackers. We show that the proposed method successfully handle occlusion, noise, scale, illumination, and appearance change of the object.
Robust model-based 3D object recognition by combining feature matching with tracking

We propose a vision based 3D object recognition and tracking system, which provides high level scene descriptions such as object identification and 3D pose information. The system is composed of object recognition part and real-time tracking part. In object recognition, we propose a feature which is robust to scale, rotation, illumination change and background clutter. A probabilistic voting scheme maximizes the conditional probability defined by the features in correspondence to recognize an object of interest. As a result of object recognition, we obtain the homography between the model image and the input scene. In tracking, a Lie group formalism is used to cast the motion computation problem into simple geometric terms so that tracking becomes a simple optimization problem. An initial object pose is estimated using correspondences between the model image and the 3D CAD model which are predefined and the homography which relates the model image to the input scene. Results from the experiments show the robustness of the proposed system.
Geometrical calibration of multispectral calibration

In this paper, we introduce a novel calibration pattern board for visible and thermal camera calibration. Our pattern board is easy to make, handy to move and efficient to heat. Also, it preserves a uniform thermal radiance for a long time. Proposed method can be employed in single- and multi- spectral camera system, and also used in the splitter or stereo camera system. As a result, our method shows a good performance comparing with previous works, and we also shows that the calibrated system is enough to use in ADAS systems.
A novel image-based control-law for the visual servoing system under large pose error

In this paper, we analyze and solve the problem of image-based visual servoing system under large initial pose error. In this case we must consider camera field of view and the violation of the assumption of image-based visual servoing. Control-law is derived from linear approximation of the image error between the gripper and the target object. To solve this problem, we propose a unified image-based control strategy with a new image space named as virtual image plane (VIP). And next we propose an image-based control-law by decoupling the translation and rotation motion of the large pose error The remarkable property of our method is that we only use the combination of the image features in the 2D image space. Finally we demonstrate the feasibility of the proposed system through experiments using a six-DOF robot equipped with a stereo camera.
Globally Optimal Manhattan Frame Estimation in Real-Time

Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branchand-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization.
An Autonomous Driving System for Unknown Environments Using a Unified Map

Recently, there have been significant advances in self-driving cars, which will play key roles in future intelligent transportation systems. In order for these cars to be successfully deployed on real roads, they must be able to autonomously drive along collision-free paths while obeying traffic laws. In contrast to many existing approaches that use prebuilt maps of roads and traffic signals, we propose algorithms and systems using Unified Map built with various onboard sensors to detect obstacles, other cars, traffic signs, and pedestrians. The proposed map contains not only the information on real obstacles nearby but also traffic signs and pedestrians as virtual obstacles. Using this map, the path planner can efficiently find paths free from collisions while obeying traffic laws. The proposed algorithms were implemented on a commercial vehicle and successfully validated in various environments, including the 2012 Hyundai Autonomous Ground Vehicle Competition.
Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry

Near-Infrared (NIR) images of most materials exhibit less texture or albedo variations making them beneficial for vision tasks such as intrinsic image decomposition and structured light depth estimation. Understanding the reflectance properties (BRDF) of materials in the NIR wavelength range can be further useful for many photometric methods including shape from shading and inverse rendering. However, even with less albedo variation, many materials e.g. fabrics, leaves, etc. exhibit complex fine-scale surface detail making it hard to accurately estimate BRDF. In this paper, we present an approach to simultaneously estimate NIR BRDF and fine-scale surface details by imaging materials under different IR lighting and viewing directions. This is achieved by an iterative scheme that alternately estimates surface detail and NIR BRDF of materials. Our setup does not require complicated gantries or calibration and we present the first NIR dataset of 100 materials including a variety of fabrics (knits, weaves, cotton, satin, leather), and organic (skin, leaves, jute, trunk, fur) and inorganic materials (plastic, concrete, carpet). The NIR BRDFs measured from material samples are used with a shape-from-shading algorithm to demonstrate fine-scale reconstruction of objects from a single NIR image.
Thermal-infrared based drivable region detection

Drivable region detection is challenging since various types of road, occlusion or poor illumination condition have to be considered in a outdoor environment, particularly at night. In the past decade, Many efforts have been made to solve these problems, however, most of the already existing methods are designed for visible light cameras, which are inherently inefficient under low light conditions. In this paper, we present a drivable region detection algorithm designed for thermal-infrared cameras in order to overcome the aforementioned problems. The novelty of the proposed method lies in the utilization of on-line road initialization with a highly scene-adaptive sampling mask. Furthermore, our prior road information extraction is tailored to enforce temporal consistency among a series of images. In this paper, we also propose a large number of experiments in various scenarios (on-road, off-road and cluttered road). A total of about 6000 manually annotated images are made available in our website for the research community. Using this dataset, we compared our method against multiple state-of-the-art approaches including convolutional neural network (CNN) based methods to emphasize the robustness of our approach under challenging situations.
GMM-based saliency aggregation for calibration-free gaze estimation

A typical gaze estimator needs an explicit personal calibration stage with many discrete fixation points. This limitation can be resolved by mapping multiple eye images and corresponding saliency maps of a video clip during an implicit calibration stage. Compared to previous calibration-free methods, our approach clusters eye images by using Gaussian Mixture Model (GMM) in order to increase calibration accuracy and reduce training redundancy. Eye feature vectors representing eye images undergo soft clustering with GMM as well as the corresponding saliency maps for aggregation. The GMM based soft-clustering boosts the accuracy of Gaussian process regression which maps between eye feature vectors and gaze directions given this constructed data. The experimental results show an increase in gaze estimation accuracy compared to previous works on calibration-free method.
Product search framework with categorization and identification

When people want to find some products in the Internet. They use query words. In this paper, we propose a product search framework with images instead of words. This is helpful when user doesnpsilat know about a product or want to find similar products. The framework is composed of three parts: (1) classify a category of product (2) find a corresponding product (3) retrieve similar products by its shape or color. We use local features as a visual information and adopt visual words based method in the categorization and identification. The representation of image is a histogram which is made from a set of local features from an image and visual words from image set. As a local feature, we adopt SURF. We use grid sampling method in the categorization part and original Fast-Hessian detector in the identification part. In the similar product search, we use a binary resized image and color information. We validate our product search framework with KAIST-104 DB and our product DB.
Radiometric Calibration by Rank Minimization

We present a robust radiometric calibration framework that capitalizes on the transform invariant low-rank structure in the various types of observations, such as sensor irradiances recorded from a static scene with different exposure times, or linear structure of irradiance color mixtures around edges. We show that various radiometric calibration problems can be treated in a principled framework that uses a rank minimization approach. This framework provides a principled way of solving radiometric calibration problems in various settings. The proposed approach is evaluated using both simulation and real-world datasets and shows superior performance to previous approaches.
Background Robust Object Labeling by Voting of Weight-Aggregated Local Features

In this paper, we present a new voting-based object labeling method that is robust to background clutter. The conventional simple voting method shows very poor performance under clutter. To reduce the effect of clutter, first we aggregate the weights between the features and the support features using similarity and proximity. Through the recursive weight aggregation process, features belonging to the same objects get stronger weights, and features belonging to clutter get weaker weights. Then, we vote the weight-aggregated features to get the object labels. We validate the enhancement of the proposed method by using an open database and a real test set
Automatic Content-Aware Color and Tone Stylization

We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose an improved technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.
Asymmetric stereo with catadioptric lens: High quality image generation for intelligent robot

In this paper, we design a new asymmetric stereo system with catadioptric lens for intelligent robot. The main benefit of catadioptric lens is long focal length with short and light lens body compared to common telephoto lenses. However, there are two critical shortcomings in catadioptric lens: narrow depth of field (DOF) and ring bokeh. Narrow DOF is a property of telephoto lens and ring bokeh stems from its special inner structure. With a help of conventional compound lens, we propose a method to overcome limitations of catadioptric lens via defocus estimation using multi-scale patch matching. Experimental results demonstrate that our approach recovers all-in-focus images from a new proposed asymmetric stereo system. Also, we show various applications based on the estimated defocus map and the recovered image. Our new system can be helpful for constructing robust vision system for intelligent robot.
Development of automated image reception, processing and distribution system for KOMPSAT-2

KOMPSAT-2 (Korea Multi-Purpose Satellite) is a new high resolution Earth observation satellite to be launched in 2004 by the Korean Government. This satellite is capable of acquiring 1m panchromatic and 4m multispectral images and will transmit the image to ground at 320 Mbps. This paper will address the work being carried out for the development of image reception, processing and distribution system for the KOMPSAT-2 image data. The development work has been focused on automation, speed, reliability, integrity, cost, expandability and security of the system. The system was divided into three sub-systems and three sub-systems into 20 software modules. An emphasis was put to the integration of value-added processing modules into the whole system. With this, we intended to fully automate the processing chain from image reception to the generation of value-added information. The development work is still undergoing and this paper will report the design of the system, progress and lessons learned.
Fast Separation of Reflection Components using a Specularity-Invariant Image Representation

In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.
High Quality Structure from Small Motion for Rolling Shutter Cameras

We present a practical 3D reconstruction method to obtain a high-quality dense depth map from narrow-baseline image sequences captured by commercial digital cameras, such as DSLRs or mobile phones. Depth estimation from small motion has gained interest as a means of various photographic editing, but important limitations present themselves in the form of depth uncertainty due to a narrow baseline and rolling shutter. To address these problems, we introduce a novel 3D reconstruction method from narrow-baseline image sequences that effectively handles the effects of a rolling shutter that occur from most of commercial digital cameras. Additionally, we present a depth propagation method to fill in the holes associated with the unknown pixels based on our novel geometric guidance model. Both qualitative and quantitative experimental results show that our new algorithm consistently generates better 3D depth maps than those by the state-of-the-art method.
Extrinsic calibration of 2D laser sensors

This paper describes a new methodology for estimating a relative pose of two 2D laser sensors. Two dimensional laser scan points do not have enough feature information for motion tracking. For this reason, additional image sensors or artificial landmarks have been used to find a relative pose. We propose the method to estimate a relative pose of 2D laser sensors without any additional sensor or artificial landmark. By scanning two orthogonal planes, we utilize only the coplanarity of the scan points on each plane and the orthogonality of the plane normals. Experiments with both synthetic and real data show the validity of the proposed method. To the best of our knowledge this works provides the first solution for the problem.