nils-herrmann/clean_scirepeval huggingface dataset:
Active learning based automatic face segmentation for kinect video

This paper presents a novel segmentation approach for extracting faces from videos. Under an active learning framework, the segmentation is conducted automatically without human interactions. A small portion of pixels are first labeled as face or non-face. Given these labeled samples, a semi-supervised spline regression model is then applied to obtain the face region. Based on the segmentation result, new pixels are selected and labeled. These two steps perform iterately until convergence. The main novelty is that color and depth data are combined to provide the labeling information. Our approach is validated via comparisons with state-of-the-art methods on real videos captured from the commodity Kinect camera.
Monocular visual localization using road structural features

Precise localization is an essential issue for autonomous driving applications, where GPS-based systems are challenged to meet requirements such as lane-level accuracy. This paper introduces a new visual-based localization approach in dynamic traffic environments, focusing on and exploiting properties of structured roads like straight roads or intersections. Such environments show several line segments on lane markings, curbs, poles, building edges, etc., which demonstrate the road's longitude, latitude and vertical directions. Based on this observation, we define a Road Structural Feature (RSF) as sets of segments along three perpendicular axes together with feature points. At each video frame, the proper road structure (or multiple road structures in case of an intersection) is predicted based on the geometric information given by a 2D map. The RSF is then detected from line segments and points extracted from the image, and used to estimate the pose of the vehicle. Experiments are conducted using video streams collected on major roads in downtown Beijing, which are structured and with intense dynamic traffic. GPS/IMU data have been collected and synchronized with the video streams as a reference in validation. The results show good performance compared with that of a more traditional visual odometry method. Future work will be addressed on using visual approach to improve GPS localization accuracy.
Facial Expression Recognition using Auto-regressive Models

In this paper, we address the analysis and recognition of facial expressions in continuous videos. We introduce a view- and texture-independent approach that exploits the temporal facial action parameters estimated by an appearance-based 3D face tracker. The facial expression recognition is carried out using learned dynamical models based on auto-regressive processes. These learned models can also be utilized for the synthesis and prediction tasks. Experiments demonstrated the effectiveness of the developed method
Facial action tracking using an AAM-based CONDENSATION approach

In this paper, we address the problem of tracking a near-frontal view face and its facial features in a video sequence. For this purpose, a particle filtering scheme is proposed, where the distribution of observations is derived from an active appearance model. The dynamics are adaptive in the sense that they are guided by a deterministic search, and the explored area of the state space is adjusted to the quality of the prediction. The number of particles is adapted accordingly, which enables a substantial gain in computing time. In order to account for occlusions, the observation model uses a robust distance measure. Experiments on real video show encouraging results.
An ICA based algorithm for video watermarking

In this paper, a new video watermarking scheme is proposed. We adapt two watermarking algorithms, originally proposed for still image watermarking, to embed data in a set of statistically independent sources, extracted from a video sequence. We show the interest of applying such an approach to video watermarking. The proposed scheme offers good robustness against MPEG compression attack, as well as an important capacity level. We consider, in this paper, data hiding in digital TV channels where data are compressed using MPEG-2. The main contribution of our study is to compare two different watermarking approaches applied to error prediction frames transformed by ICA.
Hierarchical fusion of descriptor matching and L-K optical flow

Descriptor matching is recently used in optical flow to handle large motion displacements. While doing so is a success, motion jitter often arises from lack of temporal consistency. However, when accounting for the consistency, it may violate our expectation of handling arbitrary motion shifts. In this paper, we propose a new approach to remove this controversy. At its core is a hierarchical fusion of descriptor matching and optical flow to determine the true local motion displacements. Applications to estimating homography and 3D head pose verify that the approach can well adapt to either large or small motion displacements.
SFM for planar scenes using image derivatives

Traditionally, the structure from motion (SFM) problem has been solved using feature correspondences. This approach requires reliably detected and tracked features between images taken from widespread locations. In this paper, we present a new paradigm to the SFM problem for planar scenes. The novelty of the paradigm lies in the fact that instead of image feature correspondences, only image derivatives are used. We use robust statistics in order to get robust solutions in presence of outliers. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed methods.
A system of automated training sample generation for visual-based car detection

This paper presents a system to automatically generate car sample dataset for visual-based car detector training. The dataset contains multi-view car samples labeled with the car's pose, so that a view-discriminative training and car detection is also available. There are mainly two parts in the system: laser-based car detection and tracking generates motion trajectories of on-road cars, and then visual samples are extracted by fusing the detection and tracking results with visual-based detection. A multi-modal sensor system is developed for the omni-directional data collection on a test-bed vehicle. By processing the data of experiment conducted on the freeway of Beijing, a large number of multi-view car samples with pose information were generated. The samples' quality is evaluated by applying it in a visual car detector's training and testing procedure.
Computing object-based saliency in urban scenes using laser sensing

It becomes a well-known technology that a low-level map of complex environment containing 3D laser points can be generated using a robot with laser scanners. Given a cloud of 3D laser points of an urban scene, this paper proposes a method for locating the objects of interest, e.g. traffic signs or road lamps, by computing object-based saliency. Our major contributions are: 1) a method for extracting simple geometric features from laser data is developed, where both range images and 3D laser points are analyzed; 2) an object is modeled as a graph used to describe the composition of geometric features; 3) a graph matching based method is developed to locate the objects of interest on laser data. Experimental results on real laser data depicting urban scenes are presented; efficiency as well as limitations of the method are discussed.
Online appearance-based face and facial feature tracking

We propose a simple framework that utilizes online appearance models for 3D face and facial feature tracking with a deformable model. Adapting the geometrical parameters for each frame adopts a steepest ascent method in the observation likelihood using a local exhaustive and directed search in the parameter space. The observation likelihood is based on the current appearance and the registered images. The developed framework is straightforward and has the following advantages. First, it does not require any a priori statistical facial texture. Second, it does not require any a priori transition model for the 3D motion. Video sequences featuring large head motions, large facial animations, and external illumination variations are successfully tracked, which demonstrate the efficiency of the developed framework.
Motion compensation using adaptive rectangular partitions

We present the partitioning of grey-scale images using rectangular partitions, and its use for image prediction by motion compensation. Generally, a good prediction of a video frame can be made if an accurate motion estimation is computed, most often by using block matching algorithms (BMA). We propose to use an adaptive rectangular partition in order to improve the motion estimation and compensation steps. Rectangular partitioning presents the advantage of being well adapted to the image texture and to generate variable size blocks. Taking into account these properties, we test different adaptations of the classical BMA to the rectangular partitioning, in order to improve the motion compensation performance. We consider the simple translational BMA and extend this algorithm by using spatial transformations in order to cope with rotations and scalings. The last algorithm is furthermore optimized by taking into account the shape of the rectangles in the partition. Simulations show that the last method provides higher PSNR than the two other.
Fractal image compression based on Delaunay triangulation and vector quantization

Presents a new scheme for fractal image compression based on adaptive Delaunay triangulation. Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey level dependent way. The triangulation is thus fully flexible and returns a limited number of blocks allowing good compression ratios. Moreover, a second original approach is the integration of a classification step based on a modified version of the Lloyd algorithm (vector quantization) in order to reduce the encoding complexity. The vector quantization algorithm is implemented on pixel histograms directly generated from the triangulation. The aim is to reduce the number of comparisons between the two sets of blocks involved in fractal image compression by keeping only the best representative triangles in the domain blocks set. Quality coding results are achieved at rates between 0.25-0.5 b/pixel depending on the nature of the original image and on the number of triangles retained.
View and texture-independent facial expression recognition in videos using dynamic programming

In this paper, we address the recognition of facial expressions in continuous videos. We introduce a fast and texture-independent approach that exploits facial action parameters estimated by an appearance-based 3D tracker. We represent the learned facial actions associated with different facial expressions by time series. We adopt a dynamic time warping technique for recognizing the facial expressions using similarity measures between the learned time series and the tracked ones. The developed approach can be used online and can deal with non-frontal face images since it is view-independent. Preliminary experiments demonstrated the effectiveness of the developed method.
Comparison of two wavelet based image watermarking schemes

This paper presents two semi-blind watermarking techniques, based on sets of significant wavelet coefficients, in the three lowest detail subbands of a cover-image. The first method modifies the magnitude of triplets of significant coefficients, according to a sequence of information bits. We show that multiple marks can be embedded in an image. The second method considers rectangular blocks of coefficients, and a uniform quantization-based watermarking scheme. The extraction algorithm requires one to know the position of the sets of watermarked coefficients in order to retrieve the hidden sequence of information bits. Experimental results show the robustness of the two methods, to different kinds of classical signal processing attacks.
Simultaneous facial action tracking and expression recognition using a particle filter

The recognition of facial gestures and expressions in image sequences is an important and challenging problem. Most of the existing methods adopt the following paradigm. First, facial actions/features are retrieved from the images, and then facial expressions are recognized based on the retrieved temporal parameters. Unlike this main strewn, this paper introduces a new approach allowing the simultaneous recovery of facial actions and expression using a particle filter adopting, multiclass dynamics that are conditioned on the expression. For each frame in the video sequence, our approach is split in two consecutive stages. In the first stage, the 3D head pose is recovered using a deterministic registration technique based on online appearance models. In the second stage, the facial actions as well as the facial expression are simultaneously recovered using the stochastic framework with, mixed states. The proposed fast scheme is either as robust as existing ones or more robust with respect to many regards. Experimental results show the feasibility and robustness of the proposed approach
Bilinear factorisation for facial expression analysis and synthesis

This paper addresses the issue of face representations for facial expression recognition and synthesis. In this context, a global active appearance model is used in conjunction with two bilinear factorisation models to separate expression and identity factors from the global appearance parameters. Although active appearance models and bilinear modelling are not new concepts, the main contribution of this paper consists in combining both techniques to improve facial expression recognition and synthesis (control). Indeed, facial expression recognition is performed through linear discriminant analysis of the global appearance parameters extracted by active appearance model search. Results are compared to the ones obtained for the same training and test images using classification of the expression factors extracted by bilinear factorisation. This experiment highlights the advantages of bilinear factorisation. Finally, it is proposed to exploit bilinear factorisation to synthesise facial expressions through replacement of the extracted expression factors. This yields very interesting synthesis performances in terms of visual quality of the synthetic faces. Indeed, synthetic open mouths reconstruction either with or without appearing teeth is of better quality than with classical linear-regression-based synthesis.
Orthogonal ICA representation of images

Firstly, this paper answered one interesting question: what is orthogonal in ICA? We discussed the non-orthogonality of ICA transformation and claimed that the independent components (ICs) by the FastICA algorithm are orthogonal. Based on this orthogonality, an orthogonal ICA representation (OICA) of images was proposed. Experiments on ORL database, Yale database and CMU database with OICA were performed. More work is needed to discover any advantage of OICA.
Detection of pedestrians at far distance

Pedestrian detection is a well-studied problem. Even though many datasets contain challenging case studies, the performances of new methods are often only reported on cases of reasonable difficulty. In particular, the issue of small scale pedestrian detection is seldom considered. In this paper, we focus on the detection of small scale pedestrians, i.e., those that are at far distance from the camera. We show that classical features used for pedestrian detection are not well suited for our case of study. Instead, we propose a convolutional neural network based method to learn the features with an end-to-end approach. Experiments on the Caltech Pedestrian Detection Benchmark showed that we outperformed existing methods by more than 10% in terms of log-average miss rate.
Learning lane change trajectories from on-road driving data

Lane change is one of the most principle driving behaviors on structure roads. It frequently happens in daily driving. A key issue in lane change technique is trajectory planning, where a set of trajectories describing possible vehicle motions are generated by applying a parametric function, and by uniformly sampling the end states in configuration space; the trajectories are then examined to find an optimal one for execution. However, such a trajectory set has poor efficiency due to the large sample number. Many trajectories in this set seldom happen in real human driving behaviors. In this research, lane change trajectories are collected from real driving data of different drivers. Their statistics are analyzed, through which, a simplified trajectory set is generated. Experiment results show that the trajectory set has much less number of samples but can still guarantee to cover usual lane change behaviors of human being.
A mixed triangular and quadrilateral partition for fractal image coding

This paper presents a new partitioning scheme for fractal image coding, based on triangles and quadrilaterals. The aim is to have the advantage of the triangles over the square and rectangular blocks, in terms of adaptivity to the image (reduction of the block effect), and to reduce the blocks number by grouping neighboring triangles into quadrilaterals. Quadrilaterals permit a reduction of the number of local contractive affine transformations composing the fractal transform, and thus to increase the compression ratio, while preserving the visual quality of the decoded image.
Omni-directional detection and tracking of on-road vehicles using multiple horizontal laser scanners

This research aims at generating an omnidirectional perception at the host vehicle's surroundings, extracting accurate and continuous motion trajectories of the nearby vehicles using low cost laser scanners. A system of detecting and tracking on-road vehicles using multiple laser scanners is developed, where focuses are cast on solving data association of simultaneous measurements from multiple sensors at different viewpoints, and state estimation in case of partial observations in dense dynamic situations. Experimental results in freeways in Beijing are presented, system efficiency is demonstrated, where motion trajectories describing driving behaviors such as overtaking, lane changing and other interactions between driving objects are captured. In addition, the accuracy in vehicle detection and tracking is examined using a reference vehicle with a ground truth GPS.
On Appearance Based Face and Facial Action Tracking

In this work, we address the problem of tracking faces and facial actions in a single video sequence. The main contributions of the paper are as follows. First, we develop a particle filter based framework for tracking the global 3-D motion of a face using a statistical facial appearance model. Second, we propose a framework for tracking the 3-D face pose as well as the local motion of inner features of the face due for instance to spontaneous facial actions, using an adaptive appearance model. We allow the statistics of the facial appearance as well as the dynamics to be adaptively updated during tracking. Third, we propose a variant of the second framework based on a heuristic search. Tracking real video sequences demonstrated the effectiveness of the developed methods. Accurate tracking was obtained even in the presence of perturbing factors including significant head pose and facial expression variations, occlusions, and illumination changes
Softferns for homography estimation

Ferns was recently proposed to address the problem of fast keypoint matching [1]. In estimating homography, it often yields many similar matching scores under large viewpoint change or when the object is highly repetitive. However, we observe that keypoints having similar scores are usually far away from each other. Based on this fact, we propose a Softferns approach, in which Ferns and homography can alternatively refine each other. Extensive experiments prove that Softferns is a very general improvement over Ferns.
Local or Global 3D Face and Facial Feature Tracker

We present in this paper a solution for 3D face and facial feature tracking using canonical correlation analysis and a 3D geometric model. This model is controlled with 17 parameters (6 for the 3D pose, and 11 for facial animation), and is used to crop out reference 2D shape free texture maps from the incoming input frames. Model parameters are updated via image registration in the texture map space. For registration, we use CCA to learn and exploit the dependency between texture residuals and model parameter corrections. We compare tracking results using two kinds of texture maps: one local (image patches around selected vertices of the 3D model), and one global (the whole image patch under the 3D model). Experiments evaluating the effectiveness on the approaches are reported.
Turbo-equalization applied to trellis-coded-modulations

In this article, a turbo-equalizer for TCM is investigated. The receiver is composed of a symbol detector or an equalizer realizing soft-output equalization, a deinterleaver, a soft-input soft-output channel decoder based on the a posteriori probability (APP) algorithm. A soft information provided by the decoder is then available and is re-used in the equalization step as is done in the classical scheme of turbo-equalization. The case of a APP algorithm used as an equalizer is investigated. A specific computation, iterating probabilities rather than extrinsic information, allows the application of turbo equalization to TCM. Simulations of this iterative receiver are done.
Calculating the performance degradation of a MMSE/IC turbo-equalization scheme due to SNR estimation errors.

This paper proposes a semi-analytical method for predicting the performance degradation of a turbo-equalization scheme due to signal-to-noise ratio (SNR) estimation errors. In other words, sensitivity of turbo-equalization to an imperfect knowledge of the SNR (or, equivalently, channel noise variance) is analyzed. The considered turbo-equalizer uses the Wang and Poor's soft-in/soft-out (SISO) Minimum Mean Square Error (MMSE) / Interference Cancellation (IC) equalizer and a SISO convolutional decoder. The proposed method is applied to BPSK data modulation and single-user context but may be extended to the multi-user case. This paper shows that the equalizer behavior may be very reliably predicted totally by calculations (no simulations are needed) in the presence of imperfectly known SNR at the receiver. As far as the prediction of the decoder behavior is concerned, it requires simulations for only one independent input parameter. Long frames and perfect channel knowledge at the receiver are assumed in the paper.
Mismatched sparse denoiser requires overestimating the support length

A well-known result [1, Lemma 3.4] states that, without noise, it is better to overestimate the support of a sparse signal, since, if the estimated support includes the true support, the reconstruction is perfect. In this paper, we investigate whether this result holds also in the presence of noise. First, we derive the covariance matrix of the signal estimate when the observation matrix is Gaussian, generalizing existing results. Then, we show that, even in the noisy case, overestimating the support length is the preferred solution, as the error incurred by missing some signal components dominates the overall error variance. Finally, an upper bound of the estimated support length is provided to avoid excessive noise amplification.
Design methods for irregular repeat accumulate codes

We optimize the low-complexity random-like ensemble of irregular repeat accumulate (IRA) codes for binary-input symmetric channels in the large blocklength limit. IRA codes Jin, H et al. (2000) are special subclasses of both irregular LDPCs and irregular turbo codes. We present numerical results for the BIAWGNC and formulate the density evolution (DE) Richardson, TJ et al. (2001) for IRA codes. We study the stability condition of the fixed-point corresponding to zero BER.