nils-herrmann/clean_scirepeval huggingface dataset:
Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation

Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explores their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an ℓ1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an ℓ1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the ℓ1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.
Fusion of static and dynamic body biometrics for gait recognition

Human identification at a distance has recently gained growing interest from computer vision researchers. This paper aims to propose a visual recognition algorithm based upon fusion of static and dynamic body biometrics. For each sequence involving a walking figure, pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations, and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. Also, a model-based approach is presented under a condensation framework to track the walker and to recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues are respectively used for recognition using the nearest exemplar classifier. They are also effectively fused on decision level using different combination rules to improve the performance of both identification and verification. Experimental results on a dataset including 20 subjects demonstrate the validity of the proposed algorithm.
Context Enhancement of Nighttime Surveillance by Image Fusion

In this paper, we propose a novel method of automatically combining images of a scene at different time intervals by image fusion. All the important information of the original low quality nighttime images is combined with the context from a high quality image of the daytime at the same viewpoint. The fused image contains a comprehensive description of the scene which is more useful for human visual and machine perception. Experimental results show that the proposed method is robust and effective
Model based steganalysis

In this paper, we consider a new method for performing steganalysis using a statistical model of the cover medium. Using model based methodology, examples of detecting secret message and estimating the secret message length of bit-streams embedded using JSteg-like steganography and quantization index modulation are proposed. This steganalysis technique is based on the model of statistical distribution of quantized DCT coefficients. The histogram of cover image and "shrinkage histogram" are estimated from stego image using the statistical model. Then the secret message is detected and the secret message length is estimated. The methodology described in this paper is a framework which can also be applied to virtually any type of media such as JPEG2000 file format embedding.
A Multi-Mode Windows/Linux OS-Based Evaluation Scheme and System of Biometrics Algorithm

With the rapidly development of the biometrics market, the importance of quality control of the products should be more and more highlighted. And the evaluation of the biometrics algorithm which is the essential part of the products turned to be particularly important. On this background and according to the requirement of national research item, an evaluation scheme and system which is characterized as multi-mode Windows/Linux OS-based and automatic has been designed and developed. Here will be a introduction of the system on this paper.
A Discriminative Model of Motion and Cross Ratio for View-Invariant Action Recognition

Action recognition is very important for many applications such as video surveillance, human-computer interaction, and so on; view-invariant action recognition is hot and difficult as well in this field. In this paper, a new discriminative model is proposed for video-based view-invariant action recognition. In the discriminative model, motion pattern and view invariants are perfectly fused together to make a better combination of invariance and distinctiveness. We address a series of issues, including interest point detection in image sequence, motion feature extraction and description, and view-invariant calculation. First, motion detection is used to extract motion information from videos, which is much more efficient than traditional background modeling and tracking-based methods. Second, as for feature representation, we exact variety of statistical information from motion and view-invariant feature based on cross ratio. Last, in the action modeling, we apply a discriminative probabilistic model-hidden conditional random field to model motion patterns and view invariants, by which we could fuse the statistics of motion and projective invariability of cross ratio in one framework. Experimental results demonstrate that our method can improve the ability to distinguish different categories of actions with high robustness to view change in real circumstances.
Cast Shadow Removal in a Hierarchical Manner Using MRF

In this paper, we present a novel method for shadow removal using Markov random fields (MRF). In our method, we first construct the shadow model in a hierarchical manner. At the pixel level, we use the Gaussian mixture model to model the behavior of cast shadows for every pixel in the HSV color space. The samples which are used to update the shadow model should satisfy a pre-classifier. This pre-classifier indicates the color feature of shadow in current frame. At the global level, we exploit the statistical features of shadow in the whole scene over several consecutive frames to make this pre-classifier accurate and adaptive to the change of shadow. Then, based on the shadow model, an MRF model is constructed for shadow removal. The main contribution of this paper is twofold. First, although our method is a chroma-based method, we make the pre-classifier accurate and adaptive to the change of shadow by using the statistical features of shadow at the global level. Moreover, tracking information can make this global-level statistical information more robust. Second, we construct an MRF model to represent the dependencies between the label of a pixel and the shadow models of its neighbors. Experimental results show that the proposed method is efficient and robust.
Security enhancement of biometrics, cryptography and data hiding by their combinations

The security of multimedia information in general and the security of visual information in particular have increasingly become an issue of great concern in our networked society. Biometrics, cryptography and data hiding provide effective and often complementary solutions to information security from different perspectives. In this paper, we present a brief overview on the state-of-the-art of research on the security enhancement of biometrics, cryptography and data hiding by their combinations, with focus on the problems of cryptographic key management and biometric template protection. This paper is intended to provide a reference point for newcomers and to promote more activities in these important security issues.
Efficient auto-refocusing of iris images for light-field cameras

Light field photography provides a revolutionary possibility to reconstruct well-focused iris region from a 4D light-field image. However, such a “shoot and refocus” scheme is time-consuming in practice because it commonly needs to render an image sequence for finding the optimally refocused frame. This paper presents an efficient auto-refocusing iris imaging solution for lenselet-based light-field cameras. Firstly, a refocusing point spread function (R-PSF) is derived by detailed analysis of the relationship between refocusing depth and defocus blurriness. Secondly, an initial image is rendered at arbitrary depth. Thirdly, a content independent blurriness assessment method based on SVR (support vector regression) modeling is performed on the rendered image to locate depth shift from optimal focusing plane based on R-PSF. Finally, the optimally focused iris image is selected from a frontal candidate and a back candidate. Because our method only involves three times of image rendering based on precise localization of the optimal focusing plane, it is much more efficient than conventional “rendering and selection” solutions which need to render a large number of refocused images.
Local manifold matching for face recognition

In this paper, we propose a novel classification method, called local manifold matching (LMM), for face recognition. LMM has great representational capacity of available prototypes and is based on the local linearity assumption that each data point and its k nearest neighbors from the same class lie on a linear manifold locally embedded in the image space. We present a supervised local manifold learning algorithm for learning all locally linear manifold structures. Then we propose the nearest manifold criterion for the classification in which the query feature point is assigned to the most matching face manifold. Experimental results show that kernel PCA incorporated with the LMM classifier achieves the best face recognition performance.
CLUMOC: Multiple Motion Estimation by Cluster Motion Consensus

In this paper, we present techniques for robust multiple motions estimation based on dual consensus via clustering in both the image spatial space and the motion parameter space. Starting from traditional Random Samples Consensus algorithm, we novelly propose the CLUster MOtion Consensus (CLUMOC) to extract robust motions. The proposed algorithm has two advantages: (1), instead of random samples, the CLUMOC employs clustering in initial sample selection, which can remove outliers from correct pairs of motion, (2), CLUMOC automatically decides the number of motions, by employing competition among motion and samples, that each motion needs to compete for matching pairs and each pair of matching competes for motions. The experimental results show that the proposed method is effective and efficient under various situations.
Palmprint image synthesis: A preliminary study

In this paper we present a preliminary study of palmprint image synthesis and propose a framework for synthesizing palmprint texture. We first extract principal lines of real palm- prints using edge detection and synthesize wrinkles and ridges of palm using patch-based sampling. Then we incorporate principal lines, wrinkles and ridges to obtain the final synthetic image. After that multiple images are derived from each artificial palm to simulate the intra-class images. Our approach can generate large palmprint databases which preserve inter-class and intra-class variations. Experimental results demonstrate that the synthetic images bear a close resemblance to real palmprints in terms of appearance as well as statistical properties, showing a promising usage in algorithms evaluation and comparison.
Personalized feature combination for face recognition

In this paper a novel personalized feature combination scheme is proposed for face recognition. ANFIS (adaptive neuro-fuzzy inference system) is adopted to form specialized feature representation for each subject by global and local features. For global features, we make a comparison between the two traditional global feature extraction schemes: PCA and LDA. The local features are extracted with wavelet packet decomposition around the areas of facial features. Instead of the common way for different subjects, we realize a new representation that adapts to each individual. Such adaptability in feature selection is inspired by the face recognition mechanism of the human visual system and results in an improved recognition rate.
Depth vs. intensity: which is more important for face recognition?

Both depth and intensity are important information for face recognition. In this paper, based on 3D face modelling, we extract depth features and intensity features respectively, and further compare their capacity to characterize the individuals in our database, 3DPEF. The experimental results show that depth information is more important than intensity information for recognition under the same condition. This work is a good reference to the future work on face recognition.
Robust Low-Rank Representation via Correntropy

Subspace clustering via Low-Rank Representation (LRR) has shown its effectiveness in clustering the data points sampled from a union of multiple subspaces. In original LRR, the noise in data is assumed to be Gaussian or sparse, which may be inappropriate in real-world scenarios, especially when the data is densely corrupted. In this paper, we aim to improve the robustness of LRR in the presence of large corruptions and outliers. First, we propose a robust LRR method by introducing the correntropy loss function. Second, a column-wise correntropy loss function is proposed to handle the sample-specific errors in data. Furthermore, an iterative algorithm based on half-quadratic optimization is developed to solve the proposed methods. Experimental results on Hopkins 155 dataset and Extended Yale Database B show that our methods can further improve the robustness of LRR and outperform other subspace clustering methods.
Bin-based weak classifier fusion of iris and face biometrics

Both high accuracy of iris biometrics and friendly interface of face recognition are important issues to a biometric recognition system. So an open problem is how to combine iris and face biometrics for reliable personal identification. This paper proposes a bin-based weak classifier fusion method for Multibiometrics of Iris and Face. The matching scores of iris and face image patches are partitioned into multiple bins so that the weak classifiers are learned on the bins. Such a non-linear score mapping is simple and efficient but it can discover detailed and distinctive information hidden in matching scores. So that pattern classification performance of the matching scores can be significantly improved. In addition, an ensemble learning method based on boosting is used to select the most discriminant and robust bin-based weak classifiers for identity verification. The excellent performance on the CASIA-Iris-Distance demonstrates the advantages of the proposed method over other multibiometric fusion methods.
Fast recursive mathematical morphological transforms

Since many mathematical morphology operations are recursive transforms of dilation and erosion, this paper proposes fast recursive transforms to reduce computational complexity. The basic idea of the method is to compute the temporary results within a series of adaptive windows and the computing is performed on specific pixels. Each step of the recursive process consists of two parts: 1) computation is limited to the specific pixels (foreground or background pixels) within a window; 2) update the window adoptively and delete those varied pixels. Extensive results show that the time complexity of the method is proportional to the number of the specific pixels.
Semantics-guided multi-level RGB-D feature fusion for indoor semantic segmentation

Indoor RGB-D semantic segmentation is a new and challenging problem. Traditional methods usually apply two-stream convolutional neural networks (CNNs) to represent RGB and depth images respectively, and fuse the two streams on a specific layer. In this paper, we explore several fusion strategies based on this two-stream-CNN framework and point out such a single-layer fusion method cannot exploit the complementary RGB and depth cues well for semantic segmentation. To address this problem, we propose a novel Semantics-guided Multi-level feature fusion approach, which first learns deep feature representation from bottom to up, and then gradually fuses the RGB and depth features from high level to low level under the guidance of the semantic cues. Experimental results on SUN RGB-D dataset demonstrate the advantages of the proposed method over the state of the arts.
An Extended Grammar System for Learning and Recognizing Complex Visual Events

For a grammar-based approach to the recognition of visual events, there are two major limitations that prevent it from real application. One is that the event rules are predefined by domain experts, which means huge manual cost. The other is that the commonly used grammar can only handle sequential relations between subevents, which is inadequate to recognize more complex events involving parallel subevents. To solve these problems, we propose an extended grammar approach to modeling and recognizing complex visual events. First, motion trajectories as original features are transformed into a set of basic motion patterns of a single moving object, namely, primitives (terminals) in the grammar system. Then, a Minimum Description Length (MDL) based rule induction algorithm is performed to discover the hidden temporal structures in primitive stream, where Stochastic Context-Free Grammar (SCFG) is extended by Allen's temporal logic to model the complex temporal relations between subevents. Finally, a Multithread Parsing (MTP) algorithm is adopted to recognize interesting complex events in a given primitive stream, where a Viterbi-like error recovery strategy is also proposed to handle large-scale errors, e.g., insertion and deletion errors. Extensive experiments, including gymnastic exercises, traffic light events, and multi-agent interactions, have been executed to validate the effectiveness of the proposed approach.
Similarity based vehicle trajectory clustering and anomaly detection

In this paper, we proposed a hierarchical clustering framework to classify vehicle motion trajectories in real traffic video based on their pairwise similarities. First raw trajectories are pre-processed and resampled at equal space intervals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical clustering. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented.
Toward Accurate and Fast Iris Segmentation for Iris Biometrics

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.
On estimation of secret message length in JSteg-like steganography

Recenty, image steganalysis has attracted more attention. In this paper, a new method for detecting the secret message and estimating the secret message length of bit-streams embedded using J-Steg like steganography is proposed. First, based on the model of statistical distribution of quantized DCT coefficients, the histogram of the cover image is estimated from the stego image. Then, the secret message is detected as well as the estimation of the secret message length using the estimated cover histogram. Interestingly, our study on steganalysis of J-Steg like embedding offers a proof of Zhang et al. 's method (2003). The methodology described in this paper is a framework which can also be applied to many other stegaographic methods such as JPEG2000 file format embedding.
Reliable detection of BPCS-steganography in natural images

Image steganalysis has attracted increasing attention recently. BPCS steganalysis is currently a hot research topic. Although some BPCS steganalysis methods have been proposed, these methods have their limitations. In this paper, we propose a new approach to detect BPCS-steganography. The approach, based on statistical features, can detect the existence of secret message not only in spatial domain, but also in transform domain. To evaluate the proposed steganalytic framework, two examples of detection are performed. It is shown that the accuracy of detection of the existence of secret message in images is relatively high. Experimental results and theoretical analysis show that the proposed method is an effective steganlytic method of BPCS-steganography.
Automatic gait recognition based on statistical shape analysis

Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.