nils-herrmann/clean_scirepeval huggingface dataset:
Recognizing Commercials in Real-Time using Three Visual Descriptors and a Decision-Tree

We present a new approach for classifying mpeg-2 video sequences as `commercial' or `non-commercial' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7. In our method the extracted features from three visual descriptors are logically combined using a decision tree to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 200 representative video sequences (40 `commercials' and 4*40 `non-commercials') gathered from free digital TV-broadcasting in Germany
Crowd analysis in non-static cameras using feature tracking and multi-person density

We propose a new methodology for crowd analysis by introducing the concept of Multi-Person Density. Using a state-of-the-art feature tracking algorithm, representative low-level features and their long-term motion information are extracted and combined into a human detection model. In contrast to previously proposed techniques, the proposed method takes small camera motion into account and is not affected by camera shaking. This increases the robustness of separating crowd features from background and thus opens a whole new field for application of these techniques in non-static CCTV cameras. We show the effectiveness of our approach on various test videos and compare it to state-of-the-art people counting methods.
Monte-Carlo-Based Parametric Motion Estimation Using a Hybrid Model Approach

Parametric motion estimation is an important task for various video processing applications, such as analysis, segmentation, and coding. The process for such an estimation has to satisfy three requirements. It has to be fast, accurate, and robust in the presence of arbitrarily moving foreground objects. We introduce a two-step simplification scheme, suitable for Monte-Carlo-based perspective motion model estimation. For complexity reduction, the Helmholtz tradeoff estimator as well as random sample consensus are enhanced with this scheme and applied on Kanade–Lucas–Tomasi features as well as on video stream macroblock motion vector fields. For the feature-based estimation, good trackable features are detected and tracked on raw video sequences. For the block-based approach, motion vector fields from encoded H.264/AVC video streams are used. Results indicate that the complexity of the whole estimation process can be reduced by a factor of up to 10000 compared to state-of-the-art methods without losing estimation precision.
Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for a Gaussian Mixture PHD filter in Computer Vision applications. We also investigate how an additional benefit can be achieved by using a second human detector and justify an approximation for multiple sensors in low-clutter scenarios.
Robust global motion estimation using motion vectors of variable size blocks and automatic motion model selection

A new approach for highly robust and precise global motion estimation (GME) using motion vectors (MVs) is presented. We show that this approach obtains precise higher-order short-term motion parameters for global motion using motion vectors solely. The approach is general and works for different mathematical methods including least-squares and Newton-Raphson method. We show that the approach is suitable for fixed block sizes from plain full-search block-matching as well as for arbitrary block sizes from video streams compressed with H.264/AVC reference encoder. The proposed approach is compared against four other known MV-based GME (MVGME) methods. Our results show that the approach is significantly more robust and obtains higher precision for global motion parameters in terms of background motion compensation, especially if moving objects occur. In addition, the results are as good as results from precise pixel-based GME methods or even better while the presented MV-GME methods have very low computational costs.
Parametric motion vector prediction for hybrid video coding

Motion compensated prediction still is the main technique for redundancy reduction in modern hybrid video codecs. However, the resulting motion vector fields are highly redundant as well. Thus, motion vector prediction and difference coding are used for compressing. One drawback of all common motion vector prediction techniques is, that they are not able to predict complex motion as rotation and zoom efficiently. We present a novel parametric motion vector predictor (PMVP), based on higher-order motion models to overcome this issue. To transmit the needed motion models, an efficient compression scheme is utilized. This scheme is based on transformation, quantization and difference coding. By incorporating this predictor into the HEVC test model HM 3.2 gains of up to 2.42% are achieved.
Super-Resolution Stereo- and Multi-View Synthesis from Monocular Video Sequences

This paper presents a new approach for generation of super-resolution stereoscopic and multi-view video from monocular video. Such multi-view video is used for instance with multi-user 3D displays or auto-stereoscopic displays with head-tracking to create a depth impression of the observed scenery. Our approach is an extension of the realistic stereo view synthesis (RSVS) approach which is based on structure from motion techniques and image-based rendering to generate the desired stereoscopic views for each point in time. The extension relies on an additional super- resolution mode which utilizes a number of frames of the original video sequence to generate a virtual stereo frame with higher resolution. The algorithm is tested on several TV broadcast videos, as well as on sequences captured with a single handheld camera and sequences from the well known BBC documentation "Planet Earth". Finally, some simulation results will show that RSVS is quite suitable for super-resolution 2D-3D conversion.
Efficient Quadtree Compression for Temporal Trajectory Filtering

Spatial in loop filters are a well established tool to improve the compression performance of today's video codecs. Temporal denoising and deblocking filters have recently also received some attention, because of their ability to stabilize pictures and to reduce flickering artifacts. One such filter, the previously introduced Quad tree-based Temporal Trajectory Filter, can produce good results, provided that the associated quad tree is sufficiently detailed. In this paper a novel, generally applicable scheme to compress such quad tree information is presented. In addition, the performance of the filter within the current HEVC test model HM 8.0 is investigated.
Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation

Lagrangian theory provides a rich set of tools for analyzing non-local, long-term motion information in computer vision applications. Based on this theory, we present a specialized Lagrangian technique for the automated detection of violent scenes in video footage. We present a novel feature using Lagrangian direction fields that is based on a spatio-temporal model and uses appearance, background motion compensation, and long-term motion information. To ensure appropriate spatial and temporal feature scales, we apply an extended bag-of-words procedure in a late-fusion manner as a classification scheme on a per-video basis. We demonstrate that the temporal scale, captured by the Lagrangian integration time parameter, is crucial for violence detection and show how it correlates to the spatial scale of characteristic events in the scene. The proposed system is validated on multiple public benchmarks and non-public, real-world data from the London Metropolitan Police. Our experiments confirm that the inclusion of Lagrangian measures is a valuable cue for automated violence detection and increases the classification performance considerably compared with the state-of-the-art methods.
Prioritized Sequential 3D Reconstruction in Video Sequences of Dynamic Scenes

In this study, an algorithm is proposed to solve the multi-frame structure from motion (MFSfM) problem for monocular video sequences in dynamic scenes. The algorithm uses the epipolar criterion to segment the features belonging to the independently moving objects. Once the features are segmented, the corresponding objects are reconstructed individually by using a sequential algorithm, which is also capable prioritizing the frame pairs with respect to their reliability and information content, thus achieving a fast and accurate reconstruction through efficient processing of the available data. A tracker is utilized to increase the baseline distance between views and to improve the F-matrix estimation, which is beneficial to both the segmentation and the 3D structure estimation processes. The experimental results demonstrate that our approach has the potential to effectively deal with the multi-body MFSfM problem in a generic video sequence
Noise filtering method for color images based on LDA and nonlinear diffusion

The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.
A Lagrangian framework for video analytics

The extraction of motion patterns from image sequences based on the optical flow methodology is an important and timely topic among visual multi media applications. In this work we will present a novel framework that combines the optical flow methodology from image processing with methods developed for the Lagrangian analysis of time-dependent vector fields. The Lagrangian approach has been proven to be a valuable and powerful tool to capture the complex dynamic motion behavior within unsteady vector fields. To come up with a compact and applicable framework, this paper will provide concepts on how to compute trajectory-based Lagrangian measures in series of optical flow fields, a set of basic measures to capture the essence of the motion behavior within the image, and a compact hierarchical, feature-based description of the resulting motion features. The resulting framework will bee shown to be suitable for an automated image analysis as well as compact visual analysis of image sequences in its spatio-temporal context. We show its applicability for the task of motion feature description and extraction on different temporal scales, crowd motion analysis, and automated detection of abnormal events within video sequences.
Message From the Outgoing Editor-in-Chief

M Y three-year term as the Editor-in-Chief (EIC) of the IEEE TRANSACTIONS ON SIGNAL PROCESSING (TSP) will end on December 31, 2011. It has been a tremendous honor for me to serve the IEEE Signal Processing Society in that capacity and to interact with many brilliant Editorial Board members and authors. I was very pleased to see that Dr. Zhi-Quan (Tom) Luo of the University of Minnesota has been appointed to be my successor. Dr. Luo is a highly accomplished researcher in the area of signal processing, and I am confident that he will serve the journal well. The  In 2009, we received 1594 new submissions, 17% more than in 2008. This submission volume put a strain on the Editorial Board of 70 Associate Editors (AEs), as they were given the task of peer reviewing nearly every paper while still remaining selective with their reviewer choices. In order to make the best use of editorial and reviewer resources, we established a new review model that calls for a two-tier review process. According to that model, all submitted manuscripts are prescreened according to IEEE guidelines to determine whether they are comprehensible, fall within the scope of the periodical, and meet a minimum criterion for technical substance. Only the manuscripts that meet these criteria are sent to peer review. This prescreening process is coordinated by eight members of the Editorial Board, called Area Editors, and is conducted within the Editorial Board. Papers that do not successfully pass the prescreening processes are immediately rejected by the EIC on the recommendation of the Area Editor. The rest of the papers are assigned to AEs based on EDICS and AE availability. This new model, now in its second year, sends to peer review only the best two thirds of the submitted papers, thus reducing the number of needed reviews in 2010 by 1125. It also significantly reduced the AEs’ workload and afforded them the time to pay closer attention to the papers that they handled. Meeting our goals, the paper processing time has been significantly shortened. Upon acceptance, a paper is pre-posted on IEEE Xplore immediately. The average time from submission
A naturalistic 3D acceleration-based activity dataset & benchmark evaluations

In this paper, a naturalistic 3D acceleration-based activity dataset, the SCUT-NAA dataset, is created to assist researchers in the field of acceleration-based activity recognition and to provide a standard dataset for comparing and evaluating the performance of different algorithms. The SCUT-NAA dataset is the first publicly available 3D acceleration-based activity dataset and contains 1278 samples from 44 subjects (34 males and 10 females) collected in naturalistic settings with only one tri-axial accelerometer located alternatively on the waist belt, in the trousers pocket, and in the shirt pocket. Each subject was asked to perform ten activities. Benchmark evaluations of the dataset are provided based on FFT coefficients, DCT coefficients, time-domain features, and AR coefficients for the different accelerometer locations.
An Investigation of Imaginary Stroke Techinique for Cursive Online Handwriting Chinese Character Recognition

Imaginary stroke technique has been proved to be an effective solution to the problem of the stroke connection in online handwritten character recognition. However, it may cause confusions among characters with similar but actually different trajectories after adding imaginary strokes. In this paper, we first investigate both the benefit and the defect of the imaginary stroke technique, and then two modified methods are proposed under the framework of feature fusion and local feature enhance respectively. With the proposed methods, the feature of imaginary strokes is employed to unify the writing styles and the feature of real strokes is enhanced to strengthen discriminability. Experimental results for handwritten Chinese character recognition indicate that comparing with feature without imaginary strokes and feature with imaginary strokes, our proposed methods provide about 3%~8% and 1%~4% recognition accuracy improvement respectively.
Automatic classification of Chinese female facial beauty using Support Vector Machine

Beauty is a universal concept which has long been explored by philosophers, artists and psychologists, but there are few implementations of automated facial beauty assessment in computational science. In this paper, we develop an automated Chinese female facial beauty classification system through the application of machine learning algorithm of SVM (Support Vector Machine). We present a simple but effective feature extraction for facial beauty classification. 17 geometric features are designed to abstractly represent each facial image. The experiment is based on 510 facial images, high accuracy of 95.3% is obtained for 2-level classification (beautiful or not), but the accuracy of 4-level classification is 77.9% by SVM. The results clearly show that the notion of beauty perceived by human can also be learned by machine through the employment of supervised learning techniques. Furthermore, the finding of big gap between the accuracy of 2-level classification and 4-level classification is interesting and surprising: the high accuracy naturally leads to the conclusion that there indeed exists simplicity and objectiveness underlying the judgment of aesthetical ideal facial attractiveness; In contrast, the relatively low accuracy for 4-level classification indicates that the presented simple feature vectors is not sufficient for the classification of other levels of facial attractiveness.
A Sequence Labeling Based Approach for Character Segmentation of Historical Documents

As an important prerequisite step of historical document image analysis, character segmentation is fundamental but challenging. In this paper, we propose a novel approach for the handwritten character segmentation of historical documents by treating it as a sequence labeling problem. In more detail, the proposed model first segments document image into lines, then each column in the line image is given a label to indicate it is a segmentation position or not. The segmentation labeling is achieved by a neural model, which combines a CNN for feature extraction, a LSTM for sequence modeling and a CRF for sequence labeling. The performance of our methods has been evaluated on a 300-page dataset including 96,479 characters. The experimental results demonstrate that the proposed methods achieve superior or highly competitive performance compared with other methods.
A Pointing Gesture Based Egocentric Interaction System: Dataset, Approach and Application

With the heated trend of augmented reality (AR) and popularity of smart head-mounted devices, the development of natural human device interaction is important, especially the hand gesture based interaction. This paper presents a solution for the point gesture based interaction in the egocentric vision and its application. Firstly, a dataset named EgoFinger is established focusing on the pointing gesture for the egocentric vision. We discuss the dataset collection detail and as well the comprehensive analysis of this dataset, including background and foreground color distribution, hand occurrence likelihood, scale and pointing angle distribution of hand and finger, and the manual labeling error analysis. The analysis shows that the dataset covers substantial data samples in various environments and dynamic hand shapes. Furthermore, we propose a two-stage Faster R-CNN based hand detection and dual-target fingertip detection framework. Comparing with state-of-art tracking and detection algorithm, it performs the best in both hand and fingertip detection. With the large-scale dataset, we achieve fingertip detection error at about 12.22 pixels in 640px × 480px video frame. Finally, using the fingertip detection result, we design and implement an input system for the egocentric vision, i.e., Ego-Air-Writing. By considering the fingertip as a pen, the user with wearable glass can write character in the air and interact with system using simple hand gestures.
SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

In this paper, a novel face dataset with attractiveness ratings, namely the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self-consistency. Benchmark evaluations for facial attractiveness prediction were performed with different combinations of facial geometrical features and texture features using classical statistical learning methods and the deep learning method. The best Pearson correlation 0.8187 was achieved by the CNN model. The results of the experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.
Image-Based Rendering for Ink Painting

Ink painting is one of the traditional forms of expression in oriental art and it is fascinating to generate the distinctive ink painting effect using modern non-photo realistic rendering (NPR) technique. Since most stroke-based rendering (SBR) methods require much user interaction and professional knowledge about painting, it should be more convenient to employ image-based rendering (IBR) methods to directly produce the ink wash effect from photos. According to our knowledge, however, most current IBR methods are based on ad-hoc algorithm, and they fail to perform well in generical scenario. In this paper, we propose a new IBR framework for ink painting, which could effectively simulate ink diffusion in absorbent paper, and produce various types of black or color ink painting effects. First, significant edges of the original image are detected as the constraint regions. Second, the pixel values of the edges are propagated to the blank regions out of them using an edge-preserving energy minimization model in edit propagation technique. Third, absorbent paper appearance is simulated through texture synthesis and detail manipulation. Finally, we compose the ink diffusion results with the absorbent paper background to generate the whole ink painting. Experiments illustrate that various types of ink diffusion effects and absorbent paper appearance could be effectively produced by our method.
Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA

The effectiveness of kernel fisher discrimination analysis (KFDA) has been demonstrated by many pattern recognition applications. However, due to the large size of Gram matrix to be trained, how to use KFDA to solve large vocabulary pattern recognition task such as Chinese Characters recognition is still a challenging problem. In this paper, a two-stage KFDA approach is presented for handwritten Chinese character recognition. In the first stage, a new modified linear discriminant analysis method is developed to get the recognition candidates. In the second stage, KFDA is used to determine the final recognition result. Experiments on 1034 categories of Chinese character from 120 sets of handwriting samples shows that a 3.37% improvement of recognition rate is obtained, which suggests the effectiveness of the proposed method.