nils-herrmann/clean_scirepeval huggingface dataset:
Facial Expression Recognition from World Wild Web

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.
Automatic detection of non-posed facial action units

Automatic facial expression recognition has received great attention in the past two decades due to many applications, such as developmental psychology and human-computer interface design. In most of the current studies, less attention has been paid to the recognition of non-posed facial expressions or measuring their intensity levels. In this paper, we first introduce a novel spontaneous facial expression database called DISFA, which contains videos of 27 young adults, expressing non-posed facial expressions. In this database, the absence and presence of 12 action units (AUs) as well as their intensity levels (i.e., 0-5 scale) were coded by a facial action coding system (FACS) coder. Then, we present an automatic system which can detect facial AUs described by FACS. We compared different facial representation techniques and classifiers for automatic AU detection. As a result of our experiments, we achieved 97% average detection rate using localized Gabor features with SVM classifiers.
A heterogeneous modules interconnection architecture for FPGA-based partial dynamic reconfiguration

This paper proposes and analyzes a novel FPGA-based System-on-Chip (SoC) module interconnection architecture called the Morphing Crossbar, which enables time-efficient partial dynamic reconfiguration of embedded systems built with programmable logic. By combining local Crossbar and its companion peripheral interconnection bus, the Morphing Bus, compact embedded systems can be developed with both static and dynamic reconfigurability. The Morphing Crossbar decreases the overhead of interconnection and remapping by allowing the system components to be modularized into relocatable modules. This method increases the flexibility of dynamic partial reconfigurable system. The Morphing Crossbar, which allows adding, removing, or swapping module blocks inside the system on the fly, was implemented on the Xilinx Virtex-5 architecture. Our experimental results demonstrate that by using Morphing Crossbar, adding, removing, and swapping modules can be performed 3.6 times faster compared to architectures without Morphing Crossbar.
Congestive heart failure home monitoring pilot study in urban denver

With a growing number of low-income patients developing Congestive Heart Failure in urban Denver, accessible and affordable solutions are needed to provide home management options. A multidisciplinary team evaluated currently available options for telemonitoring and developed a solution for an initial pilot study. This system is currently used in the Denver Metro area (Colorado) for 44 CHF patients. Preliminary results show this approach is effective and has reduced the patients' average length of stay at the hospital compared to historical data and control patients who do not use a remote monitoring system.
3D Face Mesh Modeling from Range Images for 3D Face Recognition

We present an algorithm for 3D face deformation and modeling using range data captured by a 3D scanner. Using only three facial feature points extracted from the range images and a 3D generic face model, the algorithm first aligns the 3D model to the entire range data of a given subject's face. Then each aligned triangle of the mesh model, with three vertices, is treated as a surface plane which is then fitted to the corresponding interior 3D range data, using least squares plane fitting. Via triangular vertices subdivisions, a higher resolution model is generated from the coordinates of the aligned and fitted model. Finally the model and its triangular surfaces are fitted once again resulting in a smoother mesh model that resembles and captures the surface characteristic of the face. Application of the final deformed model in 3D face recognition, using a publicly available database, shows promising results.
Early interactive emotional development

Early infant emotional development concerns the interactive emergence of emotional states that motivate approach and withdrawal. These are indexed by different patterns of infant facial expressions, vocalization, and gazing that emerge within parent-infant interactions in the first 10 months of life. Specifically, the interface of a limited number of interactive parameters creates complex real-time patterns which change over developmental time. These phenomena are described below using techniques from our laboratory such as statistical simulations, continuous ratings, and computer vision modeling.
A lp-norm MTMKL framework for simultaneous detection of multiple facial action units

Facial action unit (AU) detection is a challenging topic in computer vision and pattern recognition. Most existing approaches design classifiers to detect AUs individually or AU combinations without considering the intrinsic relations among AUs. This paper presents a novel method, lp-norm multi-task multiple kernel learning (MTMKL), that jointly learns the classifiers for detecting the absence and presence of multiple AUs. lp-norm MTMKL is an extension of the regularized multi-task learning, which learns shared kernels from a given set of base kernels among all the tasks within Support Vector Machines (SVM). Our approach has several advantages over existing methods: (1) AU detection work is transformed to a MTL problem, where given a specific frame, multiple AUs are detected simultaneously by exploiting their inter-relations; (2) lp-norm multiple kernel learning is applied to increase the discriminant power of classifiers. Our experimental results on the CK+ and DISFA databases show that the proposed method outperforms the state-of-the-art methods for AU detection.
Video stabilization using SIFT-ME features and fuzzy clustering

We propose a digital video stabilization process using information that the scale-invariant feature transform (SIFT) provides for each frame. We use a fuzzy clustering scheme to separate the SIFT features representing global motion from those representing local motion. We then calculate the global orientation change and translation between the current frame and the previous frame. Each frame's translation and orientation is added to an accumulated total, and a Kalman filter is applied to estimate the desired motion. We provide experimental results from five video sequences using peak signal-to-noise ratio (PSNR) and qualitative analysis.
Facial features extraction in color images using enhanced active shape model

In this paper, we present an improved active shape model (ASM) for facial feature extraction. The original ASM method developed by Cootes et al. highly relies on the initialization and the representation of the local structure of the facial features in the image. We use color information to improve the ASM approach for facial feature extraction. The color information is used to localize the centers of the mouth and the eyes to assist the initialization step. Moreover, we model the local structure of the feature points in the RGB color space. Besides, we use 2D affine transformation to align facial features that are perturbed by head pose variations. In fact, the 2D affine transformation compensates for the effects of both head pose variations and the projection of 3D data to 2D. Experiments on a face database of 50 subjects show that our approach outperforms the standard ASM and is successful in facial feature extraction
Motion compensation using geometry and an elastic motion model

To progress the compression performance of standard video coding algorithms, emerging motion compensation techniques will need to be integrated with the current standard techniques such as those used in the H.264. Higher order motion models, geometry-adaptive partitioning and motion-assisted merging are such techniques that can be considered for the next generation of video coders. In this paper, we examine how geometry information can benefit the use of elastic motion models to accomplish better prediction. Relative complexity issues are also discussed which is important in the standardization process. Experimental results suggest that geometry-adaptive block partitioning can add to the performance of elastic motion models to a certain extent, although the increased complexity is of some concern for real-time coding applications.
Impact of audio on subjective assessment of video quality in videoconferencing applications

In the real world, we commonly receive information simultaneously through two or more senses, with the brain fusing this data to produce a single coherent message. Lip-reading is one example of this phenomenon. Laboratory studies, on the other hand, often measure the response to a stimulus by a single sense and extrapolate these results to predict real-world behavior. In this paper, we show that semantics have a significant impact on viewers' sensitivity to the quality of a video sequence for spatially separated parts of the sequence and, more importantly, that this difference in sensitivity can be changed by the presence of an audio signal. This result is important for any testing of subjects' responses to visual material. One example is the subjective assessment of the quality of video in an audio-visual communications system (such as television or videoconferencing).
Rate-Distortion Optimized Video Coding Using Automatic Sprites

Object-based video coding has been of interest for many years. Recent work by the authors has shown that a video coding system using background Sprites with automatic segmentation and background subtraction can indeed perform better than the H.264/AVC coder. In this paper, we extend this work by developing a rate-distortion optimization approach for an object-based coder. A key issue addressed by this approach is the joint choice of quantization parameters for the foreground and background. The performance of the resulting rate-distortion-optimized coder is far superior to that of H.264/AVC for source material with dominant global motion, across a range of bit rates.
Improved packetization of motion parameters for error resilient transmission of wavelet video

Resilient delivery of the motion parameters is critical in video transmission over unreliable networks. Several previous approaches to motion compensated wavelet video coding have failed to achieve good video quality due to the absence of a resilient transmission method for the motion vectors (MV). A codec employing a wavelet transform with no motion compensation is also not attractive, since it suffers from poor compression performance. This paper therefore investigates packetization methods for the motion parameters. A multiple description (MD) codec is used to encode the video for lossy networks. The motion packetization techniques are incorporated in the MD framework. We compare the performance of the investigated techniques in both lossless and lossy networks and conclude by proposing the best packet structure for motion transmission. Our proposed approach provides better performance than a recently proposed MD variant of H.264.
Video Coding Using Elastic Motion Model and Larger Blocks

Motion-compensated prediction is the key to high-performance video coding. Previous works have explored alternatives to the classical translational motion model in video coding, but the cumulative rate-distortion performance has not been significant enough to see such approaches adopted in mainstream standards. In this paper, we propose a new extended prediction strategy that incorporates non-translational motion prediction. This method uses an elastic motion model with 2-D cosine basis functions to estimate non-translational motion between the blocks. To achieve superior performance, the proposed scheme takes advantage of larger blocks with multi-level partitioning. Experimental results show that this combined framework outperforms the existing techniques, including those available in the recent H.264 standard.
Optimally efficient estimation of the statistics of rare events in queueing networks

Because of their rarity, the estimation of the statistics of buffer overflows in networks of queues by direct simulation is very costly. An asymptotically optimal (as the overflow recurrence time becomes large) scheme has been proposed by others, using importance sampling. Two aspects of this scheme are addressed. First, in the existing approach, a numerical minimization is required to generate the simulation network. An equivalent analytic minimization is described. A simple procedure for constructing the optimal simulation network is included. Second, it is shown that the average behaviour of the simulation system is the same as the average behavior of the original network in the period leading up to a buffer overflow. For a sufficiently large buffer size, the optimal simulation system depends only on the statistics of the service rate of one queue (that of the least serviced buffer) and the arrival process, assuming that no two service rates are actually equal, and does not depend in any way on the statistics of the service rates of buffers other than the one dominating the overflow statics.
A cell-loss concealment technique for MPEG-2 coded video

Audio-visual and other multimedia services are seen as important sources of traffic for future telecommunication networks, including wireless networks. A major drawback with some wireless networks is that they introduce a significant number of transmission errors into the digital bitstream. For video, such errors can have the effect of degrading the quality of service to the point where it is unusable. We introduce a technique that allows for the concealment of the impact of these errors. Our work is based on MPEG-2 encoded video transmitted over a wireless network whose data structures are similar to those of asynchronous transfer mode (ATM) networks. Our simulations include the impact of the MPEG-2 systems layer and cover cell-loss rates up to 5%. This is substantially higher than those that have been discussed in the literature up to this time. We demonstrate that our new approach can significantly increase received video quality, but at the cost of a considerable computational overhead. We then extend our technique to allow for higher computational efficiency and demonstrate that a significant quality improvement is still possible.
A demonstration of absorption controlled isolation for underwater networks

Isolation is important in communication networks. Absorption has been proposed as a means to provide improved isolation in underwater acoustic networks. In underwater acoustic systems, absorption is a strong function of frequency, so absorption, and hence isolation, can be selected through choice of carrier frequency. The use of absorption to provide isolation, while well developed in theory, has not previously been practically demonstrated. We have developed a test methodology and equipment to demonstrate the variation in isolation at a range of operating frequencies in both fresh and salt water. We have demonstrated that isolation is indeed controllable in practical systems through choice of carrier frequency.
An automatic and robust approach for global motion estimation

In recent years, global motion estimation (GME) has become an important tool in the fields of video coding, video compression and computer vision. Estimating the correct global motion is often more difficult when the video scene contains large foreground objects. Previous approaches to addressing this problem have required the application of algorithm parameters that are sequence dependent or give inconsistent results for different video sequences. In this paper, we propose a fully automatic approach that can successfully estimate global motion in the presence of large foreground objects. The proposed algorithm determines an initial estimate of the foreground pixels and then reduces the effect of the remaining foreground by using a modified Lorentzian estimator. Experimental results show the proposed method produces superior and more consistent performance than some recent approaches for a wide range of sequences.
A novel quality assessment of transmitted 3D videos based on binocular rivalry impact

Binocular rivalry, a challenging visual phenomenon occurs frequently for dissimilar monocular stimuli in 3D video communication particularly in error concealment. The quality assessment of distorted 3D video is a crucial element in designing and arranging advanced immersive media distribution platforms. In this paper we propose a quality assessment (QA) criterion that can be measured by considering the correlation of binocular rivalry impact. At first, subjective assessment is done for different frame freezing stereoscopic error concealment techniques and is compared with 2D to approximate the binocular rivalry characteristics. Secondly, a QA approach is proposed based on the perceptual characteristics of the Human Visual System (HVS). Finally, by incorporating the approximation with the perceptual measure, an assessment metric is generated for the transmitted 3D videos. Experimental analysis with common video datasets and comparison with different algorithms shows the efficiency of the proposed algorithm for video that is affected by asymmetric distortion and so is subject to binocular rivalry for stereoscopic views.
Optimizing Sensor Identification in Long-delay Networks to Account for Maximum Frame Size and Variations in Propagation Speed

Long-delay networks (LDNs) are networks in which the propagation wave speed is lower than that of radio waves, such as in underwater acoustic networks (UANs). The number of nodes is normally large in such a sensor network and the number may very due to different factors such as power failure or environmental disasters. An identification procedure is needed in this network to observe which nodes are currently operational and a large amount of time can be wasted in every probe of the procedure due to the long propagation delay. Optimizing the number of probes improves the identification time and power consumption by 75% and 60% respectively in both the slotted and un-slotted cases . While optimizing the number of probes for the long delay, the frame size (the time within which the nodes send their packets) increases due to the lower offered load. In this work we show that even with the limitation of the maximum frame size our procedure works well. One of the limitations of LDNs is the variation of the propagating wave speed. We observe that if the standard deviation of the propagation speed is approximately less than 1/e of the packet size then the identification procedure for the slotted case is better than that for the un-slotted case. In order to alleviate the effect of variation in propagation speed we use a guard time in the slotted case.
Design of a high frequency FPGA acoustic modem for underwater communication

Contemporary underwater acoustic networks use low frequency modems. While these modems can provide long range communication, their low operating frequencies mean that only low channel bandwidth is available, which results in slow data rates. This motivates our development of a high frequency modem which offers the potential for large channel bandwidth, and hence greater link capacity. There is a range-frequency trade-off because absorption becomes very high at high frequency. Our intended operating frequencies from 100 kHz to 1 MHz would only support link ranges perhaps from 1 km down to under 100m, with communication ranges longer than this requiring forwarding over a network. Reconfigurable computing based Field Programmable Gate Arrays (FPGAs) are used to accelerate product development and support evolution of fielded systems. Given the immaturity of the field of underwater communication, a reconfigurable modem is a valuable tool for development and testing modem techniques. We present a design idea to implement an acoustic modem solely in FPGA, whereas most existing modems are implemented as a combination of FPGA and DSP processors. Aside from simple anti-aliasing filters, which could be incorporated in the preamplifier stage, the modem does all of its processing in the digital domain - maximising flexibility. In this work, we describe the initial design and architecture of our software based acoustic modem that avoids the monetary cost or time investment required to design a commercial modem or custom hardware for many applications. Our demodulator is implemented using a Costas loop which performs both suppressed carrier reconstruction and synchronous data detection within the loop. Results from initial implementation are also reported in this paper.
Improved resilience for video over packet loss networks with MDC and optimized packetization

We investigate the problem of robust video transmission over packet loss networks. A resilient video coding framework is important to ensure quality video over these networks. We propose a combination of multiple description coding and an optimized packetization method to constitute the error resilient codec. Multiple description coding technique utilizes path-diversity (multiple paths between sender and receiver) in networks by sending the descriptions along different paths. An optimized strategy for packetizing the descriptions is also proposed which guarantees that each packet is self-contained and efficient. Experimental results show that the proposed method achieves improved video quality over lossy networks.
High-frequency underwater acoustic communication development system

Underwater acoustic links have low capacity because that operate at low frequencies. These low frequencies are used because they allow long range communication. Networks allow short range links to provide long range communication, eliminating the need for these long link ranges. Therefore high frequencies can be used for underwater acoustic communication. Such high-frequency underwater acoustic communication systems have not been widely studied or developed before. Specialist hardware is required to support such development. This paper explains the development of such specialist hardware.
Wireless video: the impact of the multiplexing layer on error resilience

The interest that has been shown in the provision of mobile communications systems has led to the study of real time video traffic over wireless and other error prone networks. Many of the papers in this area employ the MPEG-2 video coding algorithm and provide experimental results of the decoded image quality derived from using only the effect of cell loss on the MPEG-2 video layer. In a practical transmission scheme, this video information is combined with the audio and synchronisation information in the MPEG-2 systems layer. Results presented in this paper show that the omission of the effect of cell loss on the MPEG-2 systems layer may lead to a significant underestimation of the degradation in the quality of the decoded video.