nils-herrmann/clean_scirepeval huggingface dataset:
Manifold learning for simultaneous pose and facial expression recognition

Research on facial expression recognition has steadily been moving from analysis of deliberative frontal expressions to analysis of unconstrained spontaneous expressions. This shift has spawned complex 3D models and computationally expensive geometric methods that prevent usage on resource constrained platforms such as smart phones. This paper presents manifold learning techniques for accurate multi-view facial expression on low resolution 2D images. Our results indicate that mixed class local pose and expression manifold methods perform better than global expression techniques and work just as well as fusing together results from multiple manifolds.
Key frame extraction using MPEG-7 motion descriptors

We address the problem of key frame extraction in the compressed domain that is of great importance in content-based system applications. A novel MPEG-7 motion activity descriptor is discussed that is a combination of temporal and spatial descriptors. These descriptors represent both temporal motion intensity as well as spatial distribution of motion activity. It is assumed that the apriori information about the shot boundaries is available. The temporal descriptors are obtained by classifying the shots into five different intensity levels based on fuzzy membership functions. A high value of intensity indicates high activity and a low value of intensity indicates low activity. The spatial descriptors are obtained using motion vectors. The individual frames are characterized into spatial regions depending on the change in motion activity between successive frames. The main motivation behind this approach is to pick those frames as key frames that have maximum centralized spatial activity and high motion intensity. The motion intensity and spatial distribution are then fed to a neural network that decides the key frames based on maximum temporal activity and centralized spatial distribution. Results illustrate that the proposed approach is computationally less intensive once the network is trained and works much better than selecting the first frame and middle frame of the shot as key frame for a wide range of video sequences.
Guest Editorial Special Issue on Extreme Imaging

The papers in this special section focus on the technology of extreme imaging. Includes papers presented at the Extreme Imaging Workshop at the International Conference on Computer Vision (ICCV 2015) as well as other selected papers. AS IMAGING matures and computing power increases, new methodologies and imaging modalities are emerging that can be labeled as “extreme.” The characterization “extreme” is in consideration to time scale, spatial extent, object size, illumination, dynamic range, processing speed, unconventional environments, data size, etc. What qualifies as extreme depends on the current state of the art, but the term captures the surprising jumps in scope or capabilities that are most exciting in imaging and vision.
Operator decomposition using the wavelet transform: fundamental properties and image restoration applications

A novel formulation of image processing operations in the wavelet domain is presented which directly associates multiresolution with multichannel image processing. The formation of the multiresolution image is expressed as an operator on the image domain that transforms block-circulant structures into partially-block-circulant structures. The proposed implementation relaxes the stationarity and space-invariance assumptions in the image domain and introduces new operator structures for the implementation of single-channel algorithms which take advantage of the correlation structure in the wavelet domain. Based on this structure, the authors discuss the estimation of the power spectrum in the wavelet domain. Image restoration examples using the linear minimum mean square error filter show significant improvement achieved by the proposed approach over the conventional discrete Fourier transform (DFT) implementation.<<ETX>>
SVM with OpenCL: High performance implementation of support vector machines on heterogeneous systems

Support Vector Machines (SVM) are effective classification engines used in a large number of applications that stand to benefit from acceleration. OpenCL is a software platform specification for parallel programming that supports heterogeneous computing on a wide range of devices including GPUs, FPGAs, and multicore CPUs. In this paper, we present an accelerated implementation of SVM using a heterogeneous computing system programmed using OpenCL. The popular LIBSVM, an open source implementation of SVM, is used as the basis for our system, which allows the presented work to be integrated seamlessly into existing environments. The proposed framework is evaluated in terms of speed and accuracy for training and classification. Testing was based on two GPUs, the NVIDIA GTX 480 and Tesla K20, and compared to the serial implementation based on the Intel i5 Quad Core, 3 GHz, CPU. We find that SVM training is accelerated by a factor ranging from 9 to 22, and SVM classification is accelerated by a factor of up to 12.
Continuous recognition with incremental learning on Grassmann manifolds

Incremental learning allows incorporating new data in a classifier model without full retraining for computational efficiency. In this paper, we present two ways of performing incremental learning on Grassmann manifolds. In a Grassmann kernel learning framework, data are embedded on subspaces and kernels are constructed to map data subspaces to a projection space for classification. As new data samples become available, retraining degrades computational performance since Grassmann kernels need to be recomputed on larger matrices. We propose two computationally efficient techniques for incremental Grassmann kernel learning that achieve linear time complexity. We utilize the GROUSE framework to embed new data onto a pre-existing Grassmann manifold using Incremental Singular Value Decomposition (iSVD). Then we map the embeddings from a Grassmann space onto a projection space by exploiting the positive definite structure of Grassmann kernels and solving for principal angles of modified subspace pairs (iKernel). We show that our incremental learning approach is very effective in large systems and show examples for face recognition on standard datasets.
Blur identification by statistical analysis

A new blur identification method is proposed which estimates the point spread function (PSF) parameters by comparing the power spectrum of the Wiener restoration residual with its expected value, derived under the assumption that the parameter values tested are correct. The parameter values for which there is the closest match between the residual statistics are chosen as the estimates of the true parameters. The method has successfully identified blur parameters in both synthetically and in-camera blurred images.<<ETX>>
Keypoint matching and image registration using sparse representations

The field of sparse representations has found applications in a variety of computer vision and scientific fields. Although sparse representations were initially considered for reconstruction, they have been successfully adapted for classification. In this paper, we demonstrate the application of sparse representations in matching salient keypoint descriptors for image alignment, registration, or stitching. Our method initially builds a dictionary from keypoints in a reference image. Then keypoints associated with one or more secondary images are sparsely represented using the reference dictionary. The sparse coefficient signatures are analyzed to determine if there is a matched pair and identify the reference keypoint of the match. Top keypoint matches are used to construct the homography transformation for image registration. The usefulness of our methodology is demonstrated across several types of imagery showing robust performance while delivering state-of-the-art image alignment and registration.
Color characteristics for the evaluation of suspended sediments

This study focuses on a significant issue of the environmental monitoring application area, which is the suspended sediment concentration estimation. More specifically, the purpose of the current work is to provide a new non-intrusive way to estimate the suspended sediment (SS) distribution. The proposed methodology uses the color characteristics of river flow images and provides a high correlation factor with the suspended sediment measurements. In our opinion, the importance of the current work derives from the fact that it provides an alternative and effective way of estimating SS distribution rather as opposed to the conventional method that requires human presence, especially if we consider the difficulty of taking measurements of the river pollution during flush flood events when the sediment distribution is increased and is directly related to water quality.
Multiple description based image transmission with Low Density Parity Check codes

The multiple description coding framework is well-suited for the transmission of images in the presence of transmission errors. However, it has been mainly explored for idealized channels, where packets are either received correctly or not at all. In more realistic scenarios, bitstreams are affected by bit-level noise and error correcting codes must be employed. In the proposed scheme, the input image is decomposed into wavelet coefficients that are encoded using lattice vector quantization for the generation of multiple descriptions. Low-density parity-check (LDPC) codes are used for the protection of each stream, while joint decoding exploits the correlation between the multiple descriptions. The proposed joint source-channel decoding scheme can provide robust transmission of images by introducing a controlled amount of redundancy that can be used for error recovery. Results are presented for image transmission over binary symmetric and Gaussian channels.
Spline function approximation for velocimeter Doppler frequency measurement

A spline function approximation approach for measuring the Doppler spectral peak frequency in a laser Doppler velocimeter system is presented. The processor is designed for signal bursts with mean Doppler shift frequencies up to 100 MHz, input turbulence up to 20%, and photon counts as low as 300. The frequency-domain processor uses a bank of digital bandpass filters for the capture of the energy spectrum of each signal burst. The average values of the filter output energies, as a function of normalized frequency, are modeled as deterministic spline functions which are linearly weighted to evaluate the spectral peak location associated with the Doppler shift. The weighting coefficients are chosen to minimize the mean square error. Performance evaluation by simulation yields average errors in estimating mean Doppler frequencies within 0.5% for poor signal-to-noise conditions associated with a low photon count of 300 photons/burst.
Crowdpainting with Light: Participatory Imaging at the Big Shot

Painting with light is a night photography technique where the photographer illuminates the subject with moving lights over a long exposure time. The Big Shot project has taken this technique to the extreme over the past 30 years by engaging large crowds for the lighting of landmarks resulting in unique night photographs. In this paper, we overview the fundamental techniques of crowdpainting with light and show representative examples of images taken through the years. We also discuss some of the social aspects of this participatory experience that make it memorable and engaging. Finally, we propose computational techniques for painting with light and demonstrate, through a small experiment, how crowdpainting with light can be accessible to broad audiences for experimentation, social interaction and fun.
Facial pose estimation using a symmetrical feature model

This paper presents a robust approach to performing facial pose estimation by examining the behavior of key facial features over a wide range of poses. Such methods are useful in intelligent vision systems for entertainment, human computer interaction, and security. In our approach, faces of varying pose are automatically detected, eyes and mouth are located and an active shape model is superimposed. A facial pose estimator is developed using predictor models based on the position, size, and symmetry of facial features. By modeling these predictors over pose positions with varying yaw and pitch, excellent results are obtained without the need for complex computationally intensive methods.
A two phase approach to Bayesian network model selection and comparison between the MDL and DGM scoring heuristics

This paper presents an efficient algorithm for learning a Bayesian belief network (BBN) structure from a database, as well as providing a comparison between two BBN structure fitness functions. A Bayesian belief network is a directed acyclic graph representing conditional expectations. In this paper, we propose a two-phase algorithm. The first phase uses asymptotically correct structure learning for efficient search space exploration, while the second phase uses greedy model selection for accurate search space exploration. The minimum description length (MDL) structure fitness function is also compared with the database given model probability (DGM) fitness function in the second phase. The model selection algorithms are applied to the ALARM network to provide a comparison for the accuracy of the techniques.
Improved techniques for watermarking halftone images

Watermarking schemes that have been developed for continuous tone images cannot be directly applied to halftone images. Many of these watermarking methods require characteristics implicit in continuous tone images but absent from halftones. With this in mind, it seems reasonable to have a robust watermarking technique specific to halftones, equipped to work in the binary image domain. The paper reviews existing techniques and suggests improvements to increase their performance and overcome their limitations. The first set of post-halftone watermarking methods work on existing halftones. Data hiding cell parity (DHCP) works in the parity domain instead of on individual pixels. Data hiding mask toggling (DHMT) works by encoding two bits in the 2/spl times/2 neighborhood of a pseudorandom location. Dispersed pseudorandom generator (DPRG), on the other hand, is a preprocessing method that takes place before halftoning. DPRG achieves better visual results. Using the modified peak signal-to-noise ratio (MPSNR) metric, the proposed techniques outperform existing methods by up to 5-15%, depending on the image type and method considered.
LGE-KSVD: Robust Sparse Representation Classification

The parsimonious nature of sparse representations has been successfully exploited for the development of highly accurate classifiers for various scientific applications. Despite the successes of Sparse Representation techniques, a large number of dictionary atoms as well as the high dimensionality of the data can make these classifiers computationally demanding. Furthermore, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where, for example, variations in pose may affect identity and expression recognition. We analyze the interaction between dimensionality reduction and sparse representations, and propose a technique, called Linear extension of Graph Embedding K-means-based Singular Value Decomposition (LGE-KSVD) to address both issues of computational intensity and coefficient contamination. In particular, the LGE-KSVD utilizes variants of the LGE to optimize the K-SVD, an iterative technique for small yet over complete dictionary learning. The dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier are jointly learned through the LGE-KSVD. The atom optimization process is redefined to allow variable support using graph embedding techniques and produce a more flexible and elegant dictionary learning algorithm. Results are presented on a wide variety of facial and activity recognition problems that demonstrate the robustness of the proposed method.
Indirect camera calibration for surgery tracking

Embedding cameras in surgical lights allows full view of surgery for tracking and storage purposes. However, it presents the challenge of camera calibration, as camera lights frequently change position. In this paper, we propose a novel indirect calibration method that utilizes static ceiling cameras that detect the position of light cameras and calibrate their extrinsic parameters every time there is significant movement. In our experimental setup, indirect calibration was able to locate light cameras with an average error of 4.81 mm of the position found by standard camera calibration methods and offers the added advantage of avoiding interference with the surgeons.
Evaluation of lossless compression methods for gray scale document images

A comparative study of lossless compression algorithms is presented. The following algorithms are considered: UNIX compress, gzip, LZW, CCITT Group 3 and Group 4, JBIG, old lossless JPEG, JPEG-LS based on LOCO, CALIC, FELICS, S+P Transform, and PNG. In cases where the algorithm under consideration may only be applied to binary data, the bit planes of the gray scale image are separated, with and without Gray encoding, and the compression is applied to individual bit planes. Testing is done using a set of document images obtained by gray scale scanning of prints of the eight standard CCITT images and a set of nine gray scale pictorial images. The results show that the highest compression is obtained using the CALIC and JPEG-LS algorithms.
Fusion of depth and color for an improved active shape model

Active Shape Models (ASMs) have been widely used in facial feature representation and related applications. In this paper, we exploit the availability of registered depth and color information from a low resolution camera sensor to improve Active Shape Model fitting accuracy and efficiency. Two independent Active Shape Model profile models are constructed based on the gradient profiles of depth and color intensity information. These two channels are fused using full or partial depth information. The algorithm determines the optimal point localization using the color/depth profiles during the update step of the ASM fitting process. Results show improvements in accuracy over standard methods using color intensity or depth information alone.
Joint optimization of manifold learning and sparse representations

Dimensionality reduction via manifold learning offers an elegant representation of data whereby the high dimensional feature space is parameterized by a lower dimensional space where the data resides. Sparse representations efficiently represent test patterns by sparse linear coefficients from a dictionary of training exemplars. Sparse representations have been adopted for classification purposes, but the resulting classifiers may have to deal with data in high dimensions and large dictionaries. This paper analyzes the interaction between dimensionality reduction and sparse representations. The proposed technique, called K-LGE, presents a unified framework which utilizes a semi-supervised variant of Linear extension of Graph Embedding with K-SVD dictionary learning. An iterative procedure optimizes the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and linear classifier. Results are demonstrated in a wide variety of facial and activity recognition problems to demonstrate the robustness of our proposed method.
How connections matter: factors affecting student performance in STEM disciplines

The weakening academic performance of U.S. high school students and the declining influence of the U.S. economy have been studied extensively. Several studies have pointed to the declining interest of U.S. high school students in Science, Technology, Engineering, or Math (STEM) disciplines. This paper investigates the connectivity aspects that affect a high school student??s ability to excel academically. Using data from the National Center for Education Statistics (NCES), this paper analyzes the connections students have with their families, schools, friends, and community. Using these connections, this paper introduces an optimal prediction model named the Kalman-based Student Connectivity Model. By asking a student specific questions, this model can predict his/her cognitive reading and math performance on standardized exams. Further, this model can be used as a predictor on how modifying lifestyle choices or environmental factors may affect a student's performance in the STEM disciplines.
Benchmarking and hardware implementation of JPEG-LS

The JPEG-LS algorithm is one of the recently designated standards for lossless compression of grayscale and color images. Simulation results for lossless and near lossless compression of various image types are presented in order to explore the algorithm's effectiveness for a number of applications. A hardware implementation using VHDL is proposed and the schematic of a JPEG-LS codec, that is capable of standard-compliant lossless and near lossless encoding and decoding, has been generated using the Synopsys synthesis tool. Hardware implementation of the proposed solution on an FPGA allows for real-time processing of large image volumes.
Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition

In this paper, we propose the combination of manifold learning and distance metric learning for the generation of a representation that is both discriminative and informative, and we demonstrate that this approach is effective for face recognition. Initial dimensionality reduction is achieved using random projections, a computationally efficient and data independent linear transformation. Distance metric learning is then applied to increase the separation between classes and improve the accuracy of nearest neighbor classification. Finally, a manifold learning method is used to generate a mapping between the randomly projected data and a low dimensional manifold. Face recognition results suggest that the combination of distance metric learning and manifold learning can increase performance. Furthermore, random projections can be applied as an initial step without significantly affecting the classification accuracy.
A system for the identification of multi-page Web documents

When a World Wide Web (WWW) document spans multiple Web pages, it is often inconvenient to print or download the entire document using available tools. A two-phase iterative approach has been developed for the automated identification of pages residing within the same document boundary, given a starting uniform resource locator (URL) as input. This system was applied to a test suite of 98 Web documents, and the results were compared to a ground truth document boundary in each case. Using a set intersection metric, an overall success rate of 73 % was achieved. This is a significant improvement over existing tools, which are not fully automated, and can achieve a success rate of only 61 % with user assistance.