nils-herrmann/clean_scirepeval huggingface dataset:
A multi-object tracking system for surveillance video analysis

We present a novel and robust clustering based multi-object tracking system for surveillance video analysis. It is designed to extract the trajectory data of vehicles in crowded traffic scenes and can be extended to other applications of surveillance and sports video analysis. In our system, a fast accurate fuzzy clustering algorithm is employed, and the feature space is constructed by extracting the position, color and velocity information of foreground pixels. By using growing and predictive adaptation, fixed linkages are expected between meaningful targets and corresponding active cluster centroids. In this way the motion classifier and tracker are combined seamlessly. Experimental results suggest the efficiency and robustness of the proposed method with severe occlusions and clutter effect.
Image tampering detection based on stationary distribution of Markov chain

In this paper, we propose a passive image tampering detection method based on modeling edge information. We model the edge image of image chroma component as a finite-state Markov chain and extract low dimensional feature vector from its stationary distribution for tampering detection. The support vector machine (SVM) is utilized as classifier to evaluate the effectiveness of the proposed algorithm. The experimental results in a large scale of evaluation database illustrates that our proposed method is promising.
An effective watermarking method against valumetric distortions

Most of the quantization based watermarking algorithms are very sensitive to valumetric distortions, while these distortions are regarded as common processing in audio/video analysis. In recent years, watermarking methods which can resist this kind of distortions have attracted a lot of interests. But still many proposed methods can only deal with one certain kind of valumetric distortion as amplitude scaling, and fail in other kinds of valumetric distortions like constant change attack or gamma correction. In this paper, we propose a very simple method to tackle all the three kinds of valumetric distortions. A constant change invariant domain is first constructed by spread transform, in which the watermark is embedded using a certain amplitude scaling invariant based watermarking scheme. Several typical watermarking methods and attacks have been implemented in our experiments to demonstrate the effectiveness of the proposed method.
How to make iris recognition easier?

Iris recognition is regarded as the most reliable biometrics and has been widely applied in both public and personal security areas. However users have to highly cooperate with the iris cameras to make his iris images well captured. In this paper, we aim to discuss whether and how we can make iris recognition easier. Firstly the restricting factors of iris image acquisition are analyzed and the optical formulas are derived. Then the solutions of state-of-the-art iris recognition systems are reviewed and summarized. Finally, we propose two novel iris recognition systems with good human-computer-interface but with two different strategies which respectively meet the requirements of low-end and high-end market.
Code-level information fusion of low-resolution iris image sequences for personal identification at a distance

Iris images captured at a distance usually have low resolution (LR) iris texture regions, which may lose some detailed identity information. The existed approaches try to improve the similarity of these LR iris images to high resolution (HR) gallery samples through pixel-level or featurelevel super-resolution. We argue that binary codes of iris feature templates are more directly relevant to iris recognition performance. This paper proposes a code-level scheme for heterogeneous matching of LR and HR iris images. The statistical relationship between a number of binary codes of LR iris images and a binary code corresponding to the latent HR iris image is established based on an adapted Markov network. Moreover, the cooccurence relationship between neighboring bits of HR iris code is also modeled through this Markov network. So that we can obtain an enhanced iris feature code from the probe set of LR iris image sequences. In addition, a weight mask can also be derived from the Markov model, which can be used to further improve iris recognition accuracy. Experimental results on Quality-Face/Iris Research Ensemble (Q-FIRE) database demonstrate that code-level information fusion performs significantly better than existed pixel-level, feature-level and score-level approaches for recognition of low resolution iris image sequences.
Contact Lens Detection Based on Weighted LBP

Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged. Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper, we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified SIFT descriptor is extracted at each pixel of the image. Secondly, the SIFT descriptor is used to rank the LBP encoding sequence. Then, statistical features are extracted from the weighted LBP map. Lastly, SVM classifier is employed to classify the genuine and counterfeit iris images. Extensive experiments are conducted on a database containing more than 5000 fake iris images by wearing 70 kinds of contact lens, and captured by four iris devices. Experimental results show that the proposed method achieves state-of-the-art performance in contact lens spoof detection.
Invariant texture segmentation via circular Gabor filters

In this paper, we focus on invariant texture segmentation, and propose a new method using circular Gabor filters (CGF) for rotation invariant texture segmentation. The traditional Gabor function is modified into a circular symmetric version. The rotation invariant texture features are achieved via the channel output of the CGF. A new scheme of the selection of Gabor parameters is also proposed for texture segmentation. Experiments show the efficacy of this method.
Hierarchical Shape Primitive Features for Online Text-independent Writer Identification

This paper proposes a novel method to text-independent writer identification from online handwriting. The main contributions of our method include two parts: shape primitive representation and hierarchical structure. Both shape primitive's features are developed to represent the robust and distinctive characteristics of handwriting in two hierarchies. In first hierarchy, the shape primitives probability distribution function (SPPDF)is defined as the static features, to characterize orientation information of writing style. For each shape primitive, the statistics of pressure is defined as the dynamic shape primitives probability distribution function (DSPPDF) and the second hierarchy we build Gaussian model in dynamic attributes (DA) according to curvature of shape primitives. Experiments were conducted on the NLPR handwriting database collected from 242 persons. The results show that the new method achieves high accuracy, fast speed and low requirement of the amount of characters in handwriting samples. We achieve a writer identification rate of 91.5 \% with datasets in Chinese text and 93.6 \% in English text.
Multi-task deep neural network for multi-label learning

This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the multi-label learning problem, in which each label learning is defined as a binary classification task, i.e., a positive class for “an instance owns this label” and a negative class for “an instance does not own this label”. Multi-label learning is accordingly transformed to multiple binary-class classification tasks. Considering that a deep neural nets (DNN) architecture can learn good intermediate representations shared across tasks, we generalize one classification task of traditional DNN into multiple binary classification tasks through defining the output layer with a negative class node and a positive class node for each label. After a similar pretraining process to deep belief nets, we redefine the label assignment error of MT-DNN and perform the back-propagation algorithm to fine-tune the network. To evaluate the proposed model, we carry out image annotation experiments on two public image datasets, with 2000 images and 30,000 images respectively. The experiments demonstrate that the proposed model achieves the state-of-the-art performance.
An effective algorithm for fingerprint matching

Fingerprint matching is one of the most important stages in automatic fingerprint identification systems (AFIS). Traditional methods treat this problem as point pattern matching, which is essentially an intractable problem due to the various linear deformations commonly observed in fingerprint images. We propose an effective fingerprint matching algorithm based on error propagation. Firstly, ridge information and Hough transformation are adopted to find several pairs of matching minutiae, the initial correspondences, which are used to estimate the common region of two fingerprints and the alignment parameters. Then a matched set which includes the correspondence and its surrounding matched minutiae pairs is established. The subsequent matching process is guided by the concept of error propagation: the matching errors of each unmatched minutiae are estimated according to those of its most relevant neighbor minutiae. In order to prevent the process from being misguided by mismatched minutiae pairs, we adopt a flexible propagation scheme. Experimental results demonstrate the robustness of our algorithm to non-linear deformation.
Head pose estimation using Gabor eigenspace modeling

An approach towards head pose estimation is introduced based on Gabor eigenspace modeling. A Gabor filter is used to enhance pose information and eliminate other distractive information like variable face appearance or changing environmental illumination. We discuss the selection of optimal Gabor filter's orientation to each pose, which leads to more compact pose clustering. Then we use a distribution-based pose model (DBPM) to model each pose cluster in Gabor eigenspace. Thus to each pose cluster, a 2D-distance space is established where the distance from centroid (DFC) could be used to estimate head pose. Experimental results demonstrate the algorithm's robustness and generalization. We also try our algorithm on real scene sequences to detect human face and estimate its pose. In this way, user can control an intelligent wheelchair just by his head poses.
Semi-automatic geometric model construction for 3D traffic vision

This paper concerns the construction of 3D geometric object models from monocular intensity images. Two semi-automatic techniques are described. One is based on structure from motion, and the other on structure from symmetry. Both techniques fully exploit a priori knowledge such as the ground-plane constraint and the known topology of the object to be modelled. The techniques require human intervention in solving problems (such as feature detection and matching) which cannot be processed reliably and accurately by the computer. Both techniques have been implemented as interactive user application support tools. The tools have been used to generate tens of object models which are now routinely used in a 3D model-based traffic vision system.<<ETX>>
Texture analysis based on a human visual model

A texture analysis approach superior to previous ones in such aspects as classification/segmentation performance and applicability is presented. It is based on a widely adopted human visual model which hypothesizes that the human visual system (HVS) processes input pictorial signals through a set of parallel and quasi-independent mechanisms or channels. This model is referred to as the multichannel spatial filtering model (MSFM). The core of the MSFM presently applied is the recently formulated cortical channel model (CCM), which attempts to model the process of texture feature extraction in each individual channel in the MSFM. With these models, successful algorithms for both texture classification and segmentation (texture edge detection) have been developed. The algorithm for texture feature extraction and classification is compared with the conventional benchmark, i.e., the gray-level cooccurrence matrix approach, and proves to be superior in many aspects. The algorithm for texture edge detection is tested under a variety of textured images, and good segmentation results are obtained.<<ETX>>
Personal identification based on handwriting

Many techniques have been reported for handwriting-based writer identification. Most techniques assume that the written text is fixed (e.g., in signature verification). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identification. Given that the handwriting of different people can often be visually distinctive, we take a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture. In principle this allows us to apply any standard texture recognition algorithm for the task (e.g., the multichannel Gabor filtering technique). Results of 95.0% accuracy on the classification of 300 test documents front 20 writers are very promising. The method is shown to be robust to noise and contents.
Texture feature extraction via visual cortical channel modelling

A new algorithm is proposed for texture feature extraction and classification. The algorithm is based on the increasingly popular multichannel spatial filtering approach. A computationally convenient model is described for the hypothesized visual cortical channels. Each channel is tuned to a specific narrowband of spatial frequency and orientation, and is realized by two quadrature-phase Gabor filters which are intended to mimic an adjacent pair of simple cells. The means and the standard deviations of the channel output images are shown to be powerful texture features, and perform much better than the benchmark gray level co-occurrence matrix features under noise conditions.<<ETX>>
Boosted local structured HOG-LBP for object localization

Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both feature level and topology level. Following the bottom-up paradigm, we propose a boosted Local Structured HOG-LBP based object detector. Firstly, at feature level, we propose Local Structured Descriptor to capture the object's local structure, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a boosted feature selection and fusion scheme for part based object detector. All experiments are conducted on the challenging PASCAL VOC2007 datasets. Experimental results show that our method achieves the state-of-the-art performance.
Local correlation pattern for image steganalysis

Correlation of pixels is the most important information used for image steganalysis. Current methods often consider some special types of relationships among neighboring pixels. In this paper, we propose a general descriptor to consider the correlation of pixels comprehensively. We consider the correlation of pixels in an adjacency pattern as a local correlation pattern (LCP). The LCP descriptor is proposed to embrace different local correlation patterns and represent each pattern by mapping the relative values of pixels in the pattern to a numerical value. Then, histograms of LCP values are taken as features for steganalysis. The LCP descriptor also can be used for describing the correlation of elements in the residual image obtained by image filtering. Experiments show that our constructed feature set based on the LCP descriptor outperforms a state-of-the-art method on detecting three popular steganographic algorithms.
Object detection and tracking for night surveillance based on salient contrast analysis

Night surveillance is a challenging task because of low brightness, low contrast, low Signal to Noise Ratio (SNR) and low appearance information. Most existing models for night surveillance share the following problems: a lack of adaptability for different scenes and separation between detection and tracking. To solve these problems we propose a model based on Salient Contrast Change (SCC) feature, which applies learning process to enhance adaptability and analyzes trajectories to improve the effectiveness of detection. Empirical studies on several real night videos show that the proposed model is more effective than the original CC model and other traditional models.
Iris recognition using circular symmetric filters

Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.
Tracking people through occlusions

In people tracking, one of the most challenging issues is occlusion handling. To cope with it, we introduce a Bayesian network, which involves human 2D ellipse models and an extra hidden process for occlusion relation. The 2D ellipse model combines color and spatial information simultaneously by creating color histograms for serval sub-regions. The extra hidden process for occlusion indicates the depth information of people through occlusions. The tracking process is performed using a condensation algorithm. Experiments show the effectiveness of the proposed method.
Discovering compact topical descriptors for web video retrieval

Describing videos efficiently is an important task for content based web video retrieval. To solve this problem, we propose an unsupervised approach based on an undirected topic model to learn a compact topical descriptor upon the bag-of-words (BoW) video representation. In our method, words in a BoW are assumed to have different topic features, and the topical descriptor of an entire video is obtained by aggregating those features, which makes the descriptor contain information about relative strength of topics. To improve the descriptor interpretability, an L1 penalty is used to control the topical sparsity. Furthermore, efficient learning and inference algorithms are presented. We evaluate the proposed descriptor on the Columbia Consumer Video dataset. Experimental results demonstrate that compared with the BoW and other topical representations, the proposed compact descriptor has better performance in web video retrieval.
Robust eyelid, eyelash and shadow localization for iris recognition

Eyelids, eyelashes and shadows are three major challenges for effective iris segmentation, which have not been adequately addressed in the current literature. In this paper, we present a novel method to localize each of them. First, a novel coarse-line to fine-parabola eyelid fitting scheme is developed for accurate and fast eyelid localization. Then, a smart prediction model is established to determine an appropriate threshold for eyelash and shadow detection. Experimental results on the challenging CASIA-IrisV3-Lamp iris image database demonstrate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.
Quality-based dynamic threshold for iris matching

Current iris recognition systems usually regard poor quality iris images useless since defocused or partially occluded iris images may cause false acceptance. However, such a strategy may lose an opportunity to correctly report a genuine match with poor-quality samples. This paper proposes an adaptive iris matching method to improve the throughput of iris recognition systems. The core idea of the method is to dynamically adjust the decision threshold of iris matching module based on the quality measure of input iris image. So that the poor quality iris images also have a chance to match template database under the controlled false accept rate. Experiment results on the real system demonstrate the effectiveness of the proposed method and the recognition time is expected to be greatly reduced.
Boosting local feature descriptors for automatic objects classification in traffic scene surveillance

We address the problem of automatic object classification for traffic scene surveillance, which is very challenging for the low resolution videos, large intra-class variations and real-time requirement. In this paper, we propose a new strategy for object classification by boosting different local feature descriptors in motion blobs. We not only evaluate the performance of each local feature descriptor, but also fuse these descriptors to achieve better performance. Numerous experiments are conducted and experimental results demonstrate the effectiveness and efficiency of our approach with robustness to noise and variance of view angles, lighting conditions and environments.
A Design of Iris Recognition System at a Distance

Iris recognition is a powerful biometrics for personal identification, but it is difficult to acquire good-quality iris images in real time. For making iris recognition more convenient to use, we design an iris recognition system at a distance about 3 meters. There are many key issues to design such a system, including iris image acquisition, human-machine-interface and image processing. In this paper, we respectively introduce how we deal with these problems and accomplish the engineering design. Experiments show that our system is convenient to use at the distance of 3 meters and the recognition rate is not worse than the state-of-the-art close-range systems. 1)Iris image acquisition: The human iris is very small and the required resolution for iris recognition is large, so it is diffi- cult to design the optical path for iris imaging at a distance. We carefully calculate the parameters of cameras, lens and illumi- nation intensity and elaborately select their types to set up the optical system. 2)Human-machine-interface: Because users are of differ- ent height, it is impossible for a single camera to cover so large range or for users to cooperate with the camera at a distance. We design a self-adaptive machine to automatically adapt to different people. Moreover, we use screens and audio signals to direct users to stand on the right position and give them mul- timedia feedback. All these devices are installed into a cabinet
Face pose estimation based on integral slice features of single depth images

Automatic estimation of face pose from images has wide applications in human computer interaction, biometrics, etc. This paper proposes a novel method for accurate face pose estimation from single depth images. Integral slice features are developed to characterize the distinctive and robust patterns of different face poses. Firstly the sampling points on the 3D facial surfaces are divided into multiple slices along the depth axis. And then a segment of facial slices is averaged to get an integral center. The vector from the integral center to a reference point is defined as a pose sensitive feature. Two kinds of reference points are tried in our method, i.e. nose tip and the geometric center of any slice. Therefore a large number of feature vectors can be obtained to describe the distinguishing pattern of each face pose by changing the starting and ending points of the integral segment of facial slices. Finally all these integral slice features are sent to a neural network to train a face pose estimator. Experimental results on the ETH database demonstrate that the proposed method is more accurate than state-of-the-art methods for face pose estimation.