nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Medieval and Ancient Coins over Complex Backgrounds: A Fast Automatic Method

Coin segmentation, or separating the coin area from its background, is inevitably the first step in any robust classification method. Yet, the fact that almost every research relies exclusively on grayscale images taken in controlled environments, with uniform illumination and backgrounds, wastes a vast asset of images commonly taken by numismatists, sellers and collectors. Admittedly, very often these are not easy images to work with, combining problems ranging from high cast shadows to highly textured backgrounds. In this paper we propose a fast and automatic method to segment ancient coins over complex backgrounds using a Histogram Backprojection approach. Results are compared against a proposed automation of GrabCut algorithm, and its benefits are demonstrated. Although the present article is oriented to ancient coin segmentation, the method can also be used in other contexts presenting thin objects with uniform colors.
Accurate depth-color scene modeling for 3D contents generation with low cost depth cameras

In this paper, we present a depth-color scene modeling strategy for indoors 3D contents generation. It combines depth and visual information provided by a low-cost active depth camera to improve the accuracy of the acquired depth maps considering the different dynamic nature of the scene elements. Accurate depth and color models of the scene background are iteratively built, and used to detect moving elements in the scene. The acquired depth data is continuously processed with an innovative joint-bilateral filter that efficiently combines depth and visual information thanks to the analysis of an edge-uncertainty map and the detected foreground regions. The main advantages of the proposed approach are: removing depth maps spatial noise and temporal random fluctuations; refining depth data at object boundaries, generating iteratively a robust depth and color background model and an accurate moving object silhouette.
Depth-Color Fusion Strategy for 3-D Scene Modeling With Kinect

Low-cost depth cameras, such as Microsoft Kinect, have completely changed the world of human-computer interaction through controller-free gaming applications. Depth data provided by the Kinect sensor presents several noise-related problems that have to be tackled to improve the accuracy of the depth data, thus obtaining more reliable game control platforms and broadening its applicability. In this paper, we present a depth-color fusion strategy for 3-D modeling of indoor scenes with Kinect. Accurate depth and color models of the background elements are iteratively built, and used to detect moving objects in the scene. Kinect depth data is processed with an innovative adaptive joint-bilateral filter that efficiently combines depth and color by analyzing an edge-uncertainty map and the detected foreground regions. Results show that the proposed approach efficiently tackles main Kinect data problems: distance-dependent depth maps, spatial noise, and temporal random fluctuations are dramatically reduced; objects depth boundaries are refined, and nonmeasured depth pixels are interpolated. Moreover, a robust depth and color background model and accurate moving objects silhouette are generated.
Adaptive spatio-temporal filter for low-cost camera depth maps

In this paper we present an adaptive spatio-temporal filter that aims to improve low-cost depth camera accuracy and stability over time. The proposed system is composed by three blocks that are used to build a reliable depth map of static scenes. An adaptive joint-bilateral filter is used to obtain consistent depth maps by jointly considering depth and video information and by adapting its parameters to different levels of estimated noise. Kalman filters are used to reduce the temporal random fluctuations of the measurements. Finally an interpolation algorithm is used to obtain consistent depth maps in the regions where the depth information is not available. Results show that this approach allows to considerably improve the depth maps quality by considering spatio-temporal information and by adapting its parameters to different levels of noise.
An algorithm for FOE localization

An analytical procedure for the estimation of the focus of expansion (FOE) location is introduced. The method is applied to a vector field that has been obtained from the analysis of the relative translational movement of a rigid body with respect to the acquiring camera (monocular system). Two basic properties of complex differentiation theory are applied, assuming that each vector of the optic flow is a real-valued complex function of the form v(x,y)=v/sub x/(x,y)+iv/sub y/,(x,y). These properties provide the set of points for which both components of the vector field v/sub x/(x,y) and v/sub y/(x,y) are harmonic and, as a subset, the set of points for which both components are null. The use of perspective projection states that the FOE is obtained as the locus where v/sup x/(x,y)=0=v/sub y/(x,y) and, therefore, where the conditions stated by both properties meet.
Automatic antibiograms inhibition halo determination through texture and directional filtering analysis

A new segmentation and analysis strategy to automatically measure the inhibition halos (IHs) in antibiograms is presented. It is based on the application of a combined texture and directional filtering analysis technique on the result of a segmentation process which incorporates colour analysis and Hough transform applications. The computational efficiency is highly improved by applying the different processing stages to selected areas of the acquired images and exploiting a priori information about the IHs shape and distribution.
Automatic car plate detection and recognition through intelligent vision engineering

License plate recognition systems are becoming the key to many traffic related applications such as road traffic monitoring or parking lot access control. In this paper, a software and hardware architecture implemented in the ESPRIT 5184 LOCOMOTIVE project for automatic vehicle plate number identification is presented. The system, developed in order to dynamically recognize symbol clusters on moving objects, integrates different computer vision techniques related to intrusion detection and automatic pattern recognition in slightly controlled environments, keeping real time operation.
Target Detection Through Robust Motion Segmentation and Tracking Restrictions in Aerial Flir Images

An efficient automatic moving target detection and tracking system in airborne forward looking infrared (FLIR) imagery is presented in this paper. Due to camera ego-motion, these detection and tracking tasks are challenging problems. Besides, previously proposed techniques are not suitable for aerial images, as the predominant regions are non-textured. The proposed system efficiently estimates not only the camera motion but also the target motion, by means of an accurate motion vector field computation and robust motion parameters estimation technique. This information allows accurately to segment each target, and tracking them with ego-motion compensation. Verification of tracking restrictions helps detecting true targets while reducing very significantly the false alarm rate. Excellent results have been obtained over real FLIR sequences.
Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature.
Model-based global motion estimation and compensation for very low bit-rate video coding in dynamic monocular systems

A segmentation-based hybrid video codec is introduced, that is able to work with sequences acquired by a moving camera. The global movement of the camera is compensated, allowing a working procedure in the codec that is closer to the case of a static background. The segmentation scheme consists of a relaxation algorithm followed by a bottom-up merging procedure. The region contour description is dramatically reduced through forward and backward region based motion estimation and compensation, applied over an image where the camera motion has already been compensated. Structural image formation is used to carry out a selective displacement field difference (DFD) coding. Although the encoded bit-rate is highly reduced, the reconstruction of image objects is improved, avoiding the characteristic blocking effects.
Non-linear optimization for robust estimation of vanishing points

A new method for robust estimation of vanishing points is introduced in this paper. It is based on the MSAC (M-estimator Sample and Consensus) algorithm and on the definition of a new distance function between a vanishing point and a given orientation. Apart from the robustness, our method represents a flexible and efficient solution, since it allows to work with different type of image data, and its iterative nature makes better use of the available information to obtain more accurate estimates. The key issue of the work is the proposed distance function, that makes the error to be independent from the position of an hypothesized vanishing point, which allows to work with points at the infinity. Besides, the estimation process is guided by a non-linear optimization process that enhances the accuracy of the system. The robustness of our proposal, compared with other methods in the literature is shown with a set of tests carried out for both synthetic data and real images. The results show that our approach obtain excellent levels of accuracy and that is definitely robust against the presence of large amounts of outliers, outperforming other state of the art approaches.
Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.
Region-based control points determination for multivector motion description

Motion description is a key clement in region-based video-coding approaches, becoming essential for low and very low bit-rate transmissions. Among the different advanced motion description approaches, multivector motion description (MMD) models the region motion through a variable number of motion vectors (MVs) applied on specific region points called control points (CPs). However, its application for region-based video coding would require the transmission of both MVs and CPs in order to adequately compensate each region motion. Here, a region-based control point determination strategy for MMD is proposed, which is strictly based on region contour information and the application of motion-interpolation accuracy constraints to drive the CP's location procedure. This approach offers two main advantages while keeping a reasonable computational cost: (1) it is able to operate on arbitrary-shaped regions of the image and (2) it allows the complete elimination of the transmission of any information related to the number and position of the CPs.
Sequence Independent very Fast Mode Decision Algorithm on H.264/AVC Baseline Profile

In this paper we propose a new fast mode decision (FMD) algorithm for H.264/AVC to reduce the computational load of the motion estimation (ME) process. It is oriented to dramatically reduce the encoding time regardless of the level of motion present at the sequences (high or low-motion). The algorithm decides as the best mode the skip mode or mode 1 without need to compute the rest of coding alternatives if some conditions are satisfied. These conditions ensure these modes to be used appropriately for low motion and high motion sequences respectively to achieve an appropriate rate distortion (RD) cost based on the results of the previous encoded frames. Tests have shown reductions of the encoding time around-81% for all kinds of sequences, while moderate loss of coding efficiency.
Contourless region-based video coding for very low bit-rates

In spite of their advantages, region-based approaches present a heavy burden in the required amount of information to describe region contours. To avoid these limitations, a new region-based motion estimation and compensation strategy is proposed, which allows the operation on arbitrary shaped regions and the ability to reconstruct them without any contour information. The strategy is enhanced by the use of multivector motion estimation and compensation. Results showing the quality and advantages of the strategy are provided.
HOG-like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.
Spoken information retrieval for multimedia databases

This document describes the realization of a spoken information retrieval system and its application to word search into indexed multimedia databases. The multimedia database is build from a multiformat set of text, audio and video documents. The whole archive collection is indexed using preprocessing techniques to produce transcripts and indexing software tools to catalog them. The system uses a Java-based distributed client-server architecture. A Java applet is used to capture the audio signal for a spoken query, then it is transmitted to a server where an automatic speech recognition (ASR) software is applied to convert the signal into a transcripted hypothesis. Later, a query tool process the transcript sentence along with the indexed multimedia database and a set of pointers to documents are generated. Finally, a Web page with links to the resulting documents, where queried words appear, is presented to the user.
Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results.
Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.
Robust Vehicle Detection Through Multidimensional Classification for on Board Video Based Systems

This paper presents a new in-vehicle real-time vehicle detection strategy which hypothesizes the presence of vehicles in rectangular sub-regions based on the robust classification of features vectors result of a combination of multiple morphological vehicle features. One vector is extracted for each region of the image likely containing vehicles as a multidimensional likelihood measure with respect to a simplified vehicle model. A supervised training phase set the representative vectors of the classes vehicle and non-vehicle, so that the hypothesis is verified or not according to the Mahalanobis distance between the feature vector and the representative vectors. Excellent results have been obtained in several video sequences accurately detecting vehicles with very different aspect-ratio, color, size, etc, while minimizing the number of missing detections and false alarms.
Adaptive morphological time stamp segmentation based on efficient global motion estimation

An efficient segmentation technique of regular shaped sudden temporal intensity changes in image sequences is presented. It is based on morphological analysis of binarized difference images, and its parameters are dynamically modified according to an efficient estimation of the global motion of the scene. Motion analysis is carried out with the combination of a multiresolution flow estimation technique and a closed formula over the resulting motion vectors based on a simplified motion model. Specially suited for applications demanding a temporal reference, the new segmentation technique will be described in the context of a time stamp detection application.
Measurement-based reclustering for multiple object tracking with particle filters

Multiple object tracking is a main research area in the computer vision field. Particle filters have shown their performance as a powerful tool allowing to track visual objects giving temporal coherence to incoming observations, as well as offering an excellent framework for this task due to its inherent multimodality. However, traditional algorithms for particle filters do not cope directly with multiple objects and several considerations have to be addressed. In this work, an efficient reclustering strategy is proposed, which takes into account new measurements according to a novelty function, and provides a criterium to determine the minimum required number of particles to be drawn for each tracked object. To show its performance, this strategy has been used as a multiple 2D object tracking for video-surveillance applications. Excellent results are obtained, in terms of efficiency and accuracy.
Plane rectification through robust vanishing point tracking using the Expectation-Maximization algorithm

This paper introduces a new strategy for plane rectification in sequences of images, based on the Expectation-Maximization (EM) algorithm. Our approach is able to compute simultaneously the parameters of the dominant vanishing point in the image plane and the most significant lines passing through it. It is based on a novel definition of the likelihood distribution of the gradient image considering both the position and the orientation of the gradient pixels. Besides, the mixture model in which the EM algorithm operates is extended, compared to other works, to consider an additional component to control the presence of outliers. Some synthetic data tests are described to show the robustness and efficiency of the proposed method. The plane rectification results show that the method is able to remove the perspective and affine distortion of real traffic sequences without the need to compute two vanishing points.
Robust 3D people tracking and positioning system in a semi-overlapped multi-camera environment

People positioning and tracking in 3D indoor environments are challenging tasks due to background clutter and occlusions. Current works are focused on solving people occlusions in low-cluttered backgrounds, but fail in high-cluttered scenarios, specially when foreground objects occlude people. In this paper, a novel 3D people positioning and tracking system is presented, which shows itself robust to both possible occlusion sources: static scene objects and other people. The system holds on a set of multiple cameras with partially overlapped fields of view. Moving regions are segmented independently in each camera stream by means of a new background modeling strategy based on Gabor filters. People detection is carried out on these segmentations through a template-based correlation strategy. Detected people are tracked independently in each camera view by means of a graph-based matching strategy, which estimates the best correspondences between consecutive people segmentations. Finally, 3D tracking and positioning of people is achieved by geometrical consistency analysis over the tracked 2D candidates, using head position (instead of object centroids) to increase robustness to foreground occlusions.