nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation framework on translation-invariant representation for cumulative foot pressure image

Ground reaction force can be used to distinguish different human gait like limb movement. Cumulative foot pressure image is a 2-D data that recorded the spatial and temporal change of ground reaction force during one gait cycle. However, when putting it into practice as a new biometric for gait recognition, it suffers from the problem of large translation variation within class caused by wearing different shoes and walking in different speed. In this paper, an evaluation framework is proposed to address the problem. The framework consists of a database containing cumulative foot pressure images and a well designed benchmark. The data are collected from 118 subjects. A locality-constrained sparse coding scheme is developed to be compared with the benchmark PCA approach. Experimental results show the potential of evaluation framework for evaluating the translation-invariant power of image representation algorithms.
A comparison study on kernel based online learning for moving object classification

Most visual surveillance and video understanding systems require knowledge of categories of objects in the scene. One of the key challenges is to be able to classify any object in a real-time procedure in spite of changes in the scene over time and the varying appearance or shape of object. In this paper, we explore the applications of kernel based online learning methods in dealing with the above problems. We evaluate the performance of recently developed kernel based online algorithms combined with the state-of-the-art local shape feature descriptor. We perform the experimental evaluation on our dataset. The experimental results demonstrate that the online algorithms can be highly accurate to the problem of moving object classification.
Matching tracking sequences across widely separated cameras

In this paper, we present a new solution to the problem of matching tracking sequences across different cameras. Unlike snapshot-based appearance matching which matches objects by a single image, we focus on sequence matching to alleviate the uncertainties brought by segmentation errors and partial occlusions. By incorporating multiple snapshots of the same object, the influence of the variation is alleviated. At the training stage, given the sequence of a queried person under one camera, the appearance model is formulated by concatenating feature vectors with the majority of votes over the sequence. At the testing stage, Bayesian inference is incorporated into the identification framework to accumulate the temporal information in the sequence. Experimental results demonstrate the effectiveness of the proposed method.
Spatio-temporal segmentation for video surveillance

Automatic extraction of moving objects and construction of site models are key problems in surveillance systems. We present an approach to segmenting moving objects from static scenes as well as building site models automatically. With the video sequence captured by a static camera, we describe a robust algorithm to estimate the background image as the 2D site environment map. Then, we can label each pixel in an image volume formed by a video sequence as either "foreground" or "background". Clustered foreground pixels are used as a cue to seed selection when we perform 3D segmentation in the image volume. Unlike some conventional segmentation approaches, our algorithm utilizes the spatio-temporal information instead of spatial information only. By a uniform 3D segmentation we can refine the estimate of the static environment map and detect the moving objects automatically. Experimental results on veal video sequences demonstrate the robustness and accuracy of the algorithm.
Gait analysis for human identification in frequency domain

In this paper, we analyze the spatio-temporal human characteristic of moving silhouettes in frequency domain, and find key Fourier descriptors that have better discriminatory capability for recognition than the other Fourier descriptors. A large number of experimental results and analysis show that the proposed algorithm based on the key Fourier descriptors can not only greatly reduce the gait data dimensionality, but also lighten the computation cost, with a satisfactory CCR. Besides that, classification performance can be further improved using feature fusion.
Enhanced usability of iris recognition via efficient user interface and iris image restoration

In this paper, we investigate the possibility of enhancing the usability of iris recognition via exploration of the specular spots in iris images. Firstly, the spatial configuration of the specular spots in iris images is utilized to estimate the distance between the user and the camera. Based on this a friendly user interface is established to assist users for their range adjustment. Furthermore, the estimated distance is used by an adaptive image restoration scheme to restore the blurred iris image, thereby increasing the depth of field of the iris camera. Experimental results show that the proposed method significantly enhances the usability of iris recognition without noticeable computation cost.
Fusion of Face and Palmprint for Personal Identification Based on Ordinal Features

In this paper, we present a face and palmprint multimodal biometric identification method and system to improve the identification performance. Effective classifiers based on ordinal features are constructed for faces and palmprints, respectively. Then, the matching scores from the two classifiers are combined using several fusion strategies. Experimental results on a middle-scale data set have demonstrated the effectiveness of the proposed system.
Human Behavior Analysis Based on a New Motion Descriptor

Human behavior analysis is an important area of research in computer vision and is also driven by a wide spectrum of applications, such as smart video surveillance and human-computer interface. In this paper, we present a novel approach for human behavior analysis. Two research challenges, motion representation and behavior recognition, are addressed. A novel motion descriptor, which is an improved feature based on optical flow, is proposed for motion representation. Optical flow is improved with a motion filter, and feature fusion with the shape and trajectory information. To recognize the behavior, the support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results on the Weizmann behavior database and the Institute of Automation, Chinese Academy of Science real-world multiview behavior database demonstrate the robustness and effectiveness of our method.
Image Forensics Based on Planar Contact Constraints of 3D Objects

Standing objects on planar surfaces are common to see in images, e.g., people on the ground. For most objects to stay stable on the plane, planar contact is a necessary requirement. However, 2D image splicing usually disregards this physical constraint of 3D world, leading to a potential artifact of object not attached to the plane. This paper is the first attempt to use the contact constraint of standing objects as a new clue for image forensics. Accordingly, we propose a novel approach to first reconstruct the 3D poses of standing objects and their supporting plane and then measure the contact conditions for splicing detection. To tackle the problem of unknown object shape for pose estimation, we effectively employ the prior knowledge of 3D morphable model to simultaneously estimate both shape and pose parameters by fitting to image observations. The 3D normal orientation of the supporting plane is estimated given its vanishing line. Dealing with uncertainty factors in estimations, we approximate a distribution of estimates using sampling strategies and then make the final decision. Particularly, we focused our method on the important scenario of human figure splicing detection, and comprehensive experiments on multiple data sets and typical images proved the encouraging effectiveness of the new forensic clue and the proposed approach.
A convergent solution to two dimensional linear discriminant analysis

The matrix based data representation has been recognized to be effective for face recognition because it can deal with the undersampled problem. One of the most popular algorithms, the two dimensional linear discriminant analysis (2DLDA), has been identified to be effective to encode the discriminative information for training matrix represented samples. However, 2DLDA does not converge in the training stage. This paper presents an evolutionary computation based solution, referred to as E-2DLDA, to provide a convergent training stage for 2DLDA. In E-2DLDA, every randomly generated candidate projection matrices are first normalized. The evolutionary computation method optimizes the projection matrices to best separate different classes. Experimental results show E-2DLDA is convergent and outperforms 2DLDA.
A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition

Gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms
Local salient patterns — A novel local descriptor for face recognition

Feature extraction is critical to the success of a face recognition system. Local Binary Patterns (LBP), with its different extensions, is one of the most popular texture descriptors, because of its demonstrated accuracy and efficiency. A LBP code is Jointly determined by a number of local comparisons between a central pixel and its surrounding pixels. Therefore even a single flipping of any comparison results will dramatically change the resulting LBP code. This paper proposes a novel feature descriptor, named Local Salient Patterns (LSP), which aims to only encode the most robust local comparisons, with the largest positive or negative contrast magnitude in LBP feature representation. Therefore LSP is expected to be more robust than the conventional LBP descriptor. In addition, LSP can be further extended to high order cases which explore more local relationships among multiple pixels. Extensive experimental results demonstrate that LSP outperforms the uniform LBP in most cases, when encoding using different radii and number of sampling points. LSP also achieves better performance than some advanced variants of LBP descriptors such as Local Ternary Patterns (LTP). We show that multi-order LSP achieves state-of-the art face recognition performance.
Semi-supervised Learning on Semantic Manifold for Event Analysis in Dynamic Scenes

Events can be considered as obvious changes of important properties with semantic meanings. Usually, all these properties are measurable and continual in complex formats and higher dimensions. It is hard to define and measure semantic events on the original observed data. However, according to the perception process of human being, these spatial-temporal continuous data can be mapped onto corresponding smooth manifolds, and different appearances on manifolds can indicate different semantic meanings. In this paper, we propose a semi-supervised learning method, which is based on partially labeled data, to map original observed data onto semantic manifolds for events definition and analysis in dynamic scenes. Furthermore we also perform semantic representations for various events in real world scenes. Finally, we present experimental results to evaluate the performance of our method.
Learning occlusion patterns using semantic phrases for object detection

Occlusion inference in image is a classical as well as difficult problem in computer vision. Most approaches model occlusion with different occlusion patterns in complex ways. In this paper, we propose a simple and efficient way to represent occlusion patterns called `occlusion pattern phrase', for example `dog occlude person'. These phrases model the occlusion patterns between two occluded objects, which can be used in applications like object detection and object classification. Here, we focus on using the occlusion pattern phrases in object detection. DPM is used to learn appearance models and a inference procedure is introduced to infer occlusion patterns with structural outputs. Unlike other methods, our method produces not only location boxes for different objects, but also demonstrates their occlusion relations. A new dataset with well annotated occlusion pattern images collected from Pascal VOC2007 and search engines like Bing and Google is introduced in this paper. Experiments show that our method outperforms the baseline and the occlusion pattern phrases can describe the relations between objects as we expect. In the future, we will explore the use of occlusion pattern phrases in scene understanding.
Affine invariant texture signatures

We develop a new approach for texture classification independent of affine transforms. Based on a spectral representation of texture images under affine transform, anisotropic scale invariant signatures of the orientation spectrum distribution are extracted. A peaks distribution vector (PDV) obtained on the distribution of these signatures captures texture properties invariant to affine distortion. The PDV is used to measure the similarity between textures. Experimental results show the efficiency of the PDV for affine invariant texture classification.
Albedo assisted high-quality shape recovery from 4D light fields

Over the past decade, shape reconstruction methods have been limited to Lambertian reflectance with uniform albedo and controlled lighting environment. In this paper, we present an approach for recovering high-quality shapes from 4D light fields, which can handle non-Lambertian and multi-albedo scenes with shadows and inter-reflections. 4D light fields represent all light rays that hit the sensor plane from different directions, and the depth map from light fields is robust to non-Lambertian objects. Specifically, we estimate the albedos by eliminating shadows and inter-reflections with the edge and chromaticity. Then the lighting environment is analyzed from albedos and shading. Finally, the high-quality surface geometry is exactly recovered through normal refinement. We evaluate the effectiveness and robustness on the public 4D light fields database with both synthetic and real-world scenes.
Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection

This paper proposes a novel method to address the problem of estimating the number of people in surveillance scenes with people gathering and waiting. The proposed method combines a MID (mosaic image difference) based foreground segmentation algorithm and a HOG (histograms of oriented gradients) based head-shoulder detection algorithm to provide an accurate estimation of people counts in the observed area. In our framework, the MID-based foreground segmentation module provides active areas for the head-shoulder detection module to detect heads and count the number of people. Numerous experiments are conducted and convincing results demonstrate the effectiveness of our method.
3-D model-based vehicle tracking

This paper aims at tracking vehicles from monocular intensity image sequences and presents an efficient and robust approach to three-dimensional (3-D) model-based vehicle tracking. Under the weak perspective assumption and the ground-plane constraint, the movements of model projection in the two-dimensional image plane can be decomposed into two motions: translation and rotation. They are the results of the corresponding movements of 3-D translation on the ground plane (GP) and rotation around the normal of the GP, which can be determined separately. A new metric based on point-to-line segment distance is proposed to evaluate the similarity between an image region and an instantiation of a 3-D vehicle model under a given pose. Based on this, we provide an efficient pose refinement method to refine the vehicle's pose parameters. An improved EKF is also proposed to track and to predict vehicle motion with a precise kinematics model. Experimental results with both indoor and outdoor data show that the algorithm obtains desirable performance even under severe occlusion and clutter.
Combination of Iris Recognition and Cryptography for Information Security

The combination of biometrics and cryptography is a promising information security technique which offers an efficient way to protect the biometric template, as well as to facilitate user authentication and key management. We propose a key-binding scheme based on iris data, in which reliable region is selected to reduce the intra-class variation, and error control technique that combines RS and convolutional codes is used to increase the key length. The scheme does not reveal any significant information about the key and the original iris template, and the system achieves a false rejection rate (FRR) of less than 0.5% with the key length of 218 bits.
View-Independent Behavior Analysis

The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance. Most research in behavior (or gesture) representation focusses on view-dependent representation, and some research on view invariance considers only information from 3-D models, which is effective under considerable changes of viewpoint. This paper introduces a view-independent behavior-analysis framework based on decision fusion in which distance and view angle factors are analyzed. This is a first effort to tackle the problem of behaviors under significant changes in view angle, and a first corresponding video database is built.
Position Determines Perspective: Investigating Perspective Distortion for Image Forensics of Faces

This paper points out a new telltale trace – the characteristic of perspective distortion (CPD), for the image forensics of faces. The perspective distortion is determined by the position of image shooting, and it is often overlooked when creating a forgery, which results in the inconsistency between the claimed camera parameters and the CPD in the face image. To investigate this consistency problem, we cast it to the consistency between the claimed camera intrinsic parameters and the estimated ones from the CPD. Our parameter estimation approach is based on geometric observations that are related to CPD, like facial landmarks and contours. We analyze the estimation uncertainty caused by indeterminacy of observation to obtain a more reliable forensic decision. Experiments on synthetic datasets and real forgery examples demonstrate the effectiveness of the proposed method.
Pyramidal Statistics of Oriented Filtering for robust pedestrian detection

We study the problem of robust pedestrian detection. A new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for shape representation. Unlike one-scale gradient-based methods, the PSOF descriptor constructs an image pyramid and uses a Gabor filter bank to obtain multi-scale pixel-level orientation information. Then, locally normalized pyramidal statistics of these Gabor responses are used to represent object shape. After feature extraction, the AdaBoost training algorithm is adopted to train a classifier for the final pedestrian detector. We show experimentally that the PSOF descriptor is much more robust to image blur and noise than the HOG (Histograms of Oriented Gradients) descriptor, as well as possesses excellent detection performance in normal imaging condition as HOG does. We also study the influence of various parameter settings, concluding that multi-scale information and statistic combination are two important factors for the robustness of the PSOF descriptor.
People counting using combined feature

In this paper, we present a new people counting approach in visual surveillance scenes. The features adopted in previous methods are all extracted at pixel-level or based on local area, which are severely affected by factors such as occlusion. To cover the shortage, we introduce a new feature which describes a people crowd as a whole. Because pedestrian behaviors change when the degree of crowdedness varies, we can capture motion information to model a crowd and characterize the pedestrian behaviors based on statistic analysis. Afterwards we combine together the two kinds of features presented above as the final people counting feature. Experiments conducted in real world scenes demonstrate the superior effectiveness of the proposed method.
Effective image splicing detection based on image chroma

A color image splicing detection method based on gray level co-occurrence matrix (GLCM) of thresholded edge image of image chroma is proposed in this paper. Edge images are generated by subtracting horizontal, vertical, main and minor diagonal pixel values from current pixel values respectively and then thresholded with a predefined threshold T. The GLCMs of edge images along the four directions serve as features for image splicing detection. Boosting feature selection is applied to select optimal features and Support Vector Machine (SVM) is utilized as classifier in our approach. The effectiveness of the proposed method has been demonstrated by our experimental results.
A new iris segmentation method for recognition

As the first stage, iris segmentation is very important for an iris recognition system. If the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which result in poor recognition performance. This paper proposes a new noise-removing approach based on the fusion of edge and region information. The whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information. Experimental results on a set of 2,096 images show that the proposed method has encouraging performance for improving the recognition accuracy.
Computational primitives of visual perception

Great stride has been made in psychological research about primitives of visual perception, which is important to computer vision and image processing. In this paper, we propose a computational model to imitate the primitives of visual perception based on the pyschological theory of topological perceptual organization. First, we adopt geodesic distance based descriptor to describe an independent topological structure. Then, we consider the spatial relationship of two independent structures. Experiments on structures classification demonstrates that the propose model is consistent with the psychological theory. Further experiments on patches clustering prove that our approach can be used to enhance other algorithms.