nils-herrmann/clean_scirepeval huggingface dataset:
Multibiometric secure system based on deep learning

In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable template is a binary vector and is passed through an appropriate error-correcting decoder to find a closest codeword and this codeword is hashed to generate the final secure template. The efficacy of the proposed approach is shown using a multimodal database where we achieve state-of-the-art matching performance, along with cancelability and security.
Multi-stage target recognition using modular vector quantizers and multilayer perceptrons

An automatic target recognition (ATR) classifier is proposed that uses modularly cascaded vector quantizers (VQs) and multilayer perceptrons (MLPs). A dedicated VQ codebook is constructed for each target class at a specific range of aspects, which is trained with the K-means algorithm and a modified learning vector quantization (LVQ) algorithm. Each final codebook is expected to give the lowest mean squared error (MSE) for its correct target class at a given range of aspects. These MSEs are then processed by an array of window MLPs and a target MLP consecutively. In the spatial domain, target recognition rates of 90.3 and 65.3 percent are achieved for moderately and highly cluttered test sets, respectively. Using the wavelet decomposition with an adaptive and independent codebook per sub-band, the VQs alone have produced recognition rates of 98.7 and 69.0 percent on more challenging training and test sets, respectively.
Interframe Hierarchical Address-Vector Quantization

A new interframe coding technique called interframe hierarchical address-vector quantization (IHA-VQ) is presented. It exploits the local characteristics of a moving image's motion-compensated (via block matching) difference signals by using quadtree segmentation to divide each image into large, uniform regions and smaller, highly detailed regions. The detailed regions are encoded by vector quantization (VQ), and the larger, low-detail regions are replenished from the previous frame, IHA-VQ also exploits the correlation between the small blocks by encoding the addresses of neighboring vectors by using several codebooks of address code-vectors.
Multisensor joint fusion and detection of mines using SAR and Hyperspectral

In this paper a new nonlinear joint fusion and detection algorithm is proposed for locating anomalies from two different types of sensor data (synthetic aperture radar (SAR) and hyperspectral sensor (HS) data). The proposed approach jointly exploits the nonlinear correlation or dependencies between the two sensors in order to simultaneously fuse and detect the objects of interest (mines). A well-known anomaly detector, so called RX algorithm is extended to perform fusion and detection simultaneously at the pixel level by appropriately concatenating the information from the two sensors. This approach is then extended to its nonlinear version using the idea of kernel learning theory which implicitly exploits the higher order dependencies (nonlinear correlations) between the two sensor data through an appropriate kernel.
An efficient terrain acquisition algorithm for a mobile robot

A terrain acquisition algorithm for an autonomous mobile robot to learn and model its surrounding terrain efficiently with respect to travel distance, and sensing operations is presented. It is shown that this algorithm enables a mobile robot to visit relatively few obstacle vertices to construct a map of a planar terrain occupied by polygonal obstacles. According to this algorithm, a mobile robot travels to a vertex of the obstacles only when additional terrain information can be obtained from there. The decision to move to a vertex is thus based on whether any incident edge of a vertex is missing or occluded.<<ETX>>
Kernel dictionary learning

In this paper, we present dictionary learning methods for sparse and redundant signal representations in high dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and K-SVD can be made nonlinear. We analyze these constructions and demonstrate their improved performance through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide better discrimination compared to their linear counterparts and kernel PCA, especially when the data is corrupted by noise.
Non-linear prediction using a three-layer neural network

A nonlinear predictor is investigated for a differential pulse code modulation (DPCM) encoder using artificial neural networks (ANNs). The predictor is based on a three-layer perceptron with three input nodes, 30 hidden nodes, and one output node. The backpropagation learning algorithm was used for the training of the network. Simulation results are presented to evaluate and compare the performance of the proposed neural-net-based nonlinear predictor with that of an optimized linear predictor. Success in the use of the nonlinear predictor is demonstrated through the reduction in the entropy of the differential error signal as compared to that of a linear predictor. It is shown that the ANN predictor is more robust for encoding noisy images compared to the linear predictor.<<ETX>>
Low rank representation for bilinear abundance estimation problem

In the hyperspectral abundance estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
Facial Attributes Guided Deep Sketch-to-Photo Synthesis

Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry. Despite the significant improvements in sketch-to-photo synthesis techniques, existing methods have still serious limitations in practice, such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image. In this work, we present a new framework, which is a conditional version of Cycle-GAN, conditioned on facial attributes. The proposed network forces facial attributes, such as skin and hair color, on the synthesized photo and does not need a set of aligned face-sketch pairs during its training. We evaluate the proposed network by training on two real and synthetic sketch datasets. The hand-sketch images of the FERET dataset and the color face images from the WVU Multi-modal dataset are used as an unpaired input to the proposed conditional CycleGAN with the skin color as the controlled face attribute. For more attribute guided evaluation, a synthetic sketch dataset is created from the CelebA dataset and used to evaluate the performance of the network by forcing several desired facial attributes on the synthesized faces.
Design and analysis of two-channel high definition television systems

A technique for the analysis of two-channel high-definition television (HDTV) transmission systems is presented. Design methods based on limitations due to the combined spatial and temporal characteristics of the human visual system (HVS) are developed. The concepts of resolution imbalance, recognizability, and spatial-temporal multiplexing are explained. The design and analysis of two-channel NTSC-compatible HDTV systems having an aspect ratio of 16:9 with double the vertical and horizontal resolution of the present NTSC system is described. It is shown that such a system requires more than two channels for transmission. Signal processing techniques are suggested to improve the quality of the images without using any extra channels. Time multiplexing of different frequency bands are used to exploit the psychovisual redundancies of the HVS to reduce the number of pels to be transmitted. Finally, comparing the spatial resolutions of the reconstructed images by a two-channel HDTV system with that of the HVS, it is found that the spatial resolution are not yet as good as that of the HVS.
Dual-band passive infrared imagery for automatic clutter rejection

In a typical automatic target recognition (ATR) system, the target detection module often produces too many false alarms, which may severely inhibit the effectiveness of the subsequent target classifier module. An effective clutter rejector is therefore needed between these two modules to reduce these false alarms. We explore the potential benefits of using dual-band infrared imagery to improve the performance of an eigenneural-based clutter rejector. Individual or combined bands of images are first compressed through eigenspace transformations, such as principal component analysis. The transformed data are then fed to a neural network that decides whether the input is a target or clutter. A huge and realistic set of dual-band passive infrared images was used in a series of experiments.
Bipolar eigenspace separation transformation for automatic clutter rejection

A major problem for a detection algorithm is the vast amount of false alarms normally generated. This amount of false alarms has to be substantially reduced so that a typical target classifier in the subsequent stage may work reasonably. We use the bipolar eigenspace separation transformation (BEST) and neural network techniques to improve the clutter rejection performance of an automatic target detector. Experiments have been conducted on huge and realistic datasets of forward looking infrared (FLIR) imagery. Compared to the performance of the unipolar EST and principal component analysis (PCA) with the same datasets, significant improvement in clutter rejection rates has been achieved with BEST.
Hyperspectral target detection using kernel orthogonal subspace projection

In this paper, a kernel-based nonlinear version of the orthogonal subspace projection (OSP) classifier is defined in terms of kernel functions. Input data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The OSP expression is then derived in the feature space which is kernelized in terms of kernel functions in order to avoid explicit computation in the high dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for target detection in hyperspectral imagery and it is shown that the kernel OSP outperforms the conventional OSP classifier.
Predictive vector quantization using a neural network

Predictive vector quantization (PVQ) of images using two novel coding approaches is considered. The first scheme, namely, address-PVQ, exploits the inter-vector (block) dependencies by predicting the VQ address of the current block from the addresses of the previously encoded blocks. A three-layer perceptron was used as an address-predictor with the position of the residual address being encoded. The second scheme is a vector extension of a differential pulse code modulation (DPCM) system. It exploits the inter-vector dependencies by predicting the current block of pixels. The predictive phase utilizes a three-layer perceptron while the residual blocks are vector quantized using the Kohonen self-organizing feature maps (KSOFM) clustering algorithm. The joint-optimization problem for design of the two components of PVQ was also considered. Coding results are presented for monochrome images. The joint optimization procedure improved the peak signal-to-noise ratio result by more than 1 dB.<<ETX>>
Kernel orthogonal subspace projection for hyperspectral signal classification

In this paper, a kernel-based nonlinear version of the orthogonal subspace projection (OSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OSP expression is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for detection of roads, roof tops, mines, and targets in hyperspectral imagery, and it is shown that the kernelized OSP method outperforms the conventional OSP approach.
Selectively optimized networks for automatic clutter rejection

An effective clutter rejection scheme is needed to distinguish between clutter and targets in a high-performance automatic target recognition (ATR) system. We present a clutter rejection scheme that consists of an eigenspace transformation and a multilayer perceptron (MLP). We use either principal component analysis (PCA) or the eigenspace separation transform (EST) to perform feature extraction and dimensionality reduction. The transformed data is then fed to an MLP that predicts the identity of the input, which is either a target or clutter. We devise an MLP training algorithm that seeks to maximize the class separation at a given false-alarm rate, which does not necessarily minimize the average deviation of the MLP outputs from their target valves. Experimental results are presented on a huge and realistic dataset of forward-looking infrared imagery.
Regularized Spectral Matched Filter for Target Detection in Hyperspectral Imagery

This paper describes a new adaptive spectral matched filter that incorporates the idea of regularization (shrinkage) to penalize and shrink the filter coefficients to a range of values. The regularization has the effect of restricting the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization is controlled by so called the regularization coefficient. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.
A Stereo Vision Technique Using Curve-Segments and Relaxation Matching

A multichannel feature-based stereo vision technique where curve segments are used as feature primitives in the matching process is described. The left image and the right image are filtered by using several Laplacian-of-Gaussian operators of different widths (channels). Curve segments are extracted by a tracking algorithm, and their centroids are obtained. At each channel, the generalized Hough transform of each curve segment in the left and the right image is evaluated. The epipolar constraint on the centroids of the curve segment and the channel size is used to limit the searching space in the right image. To resolve the ambiguity of the false targets (multiple matches), a relaxation technique is used where the initial scores of the node assignments are updated by the compatibility measures between the centroids of the curve segments. The node assignments with the highest score are chosen as the matching curve segments.
Discriminative graphical models for sparsity-based hyperspectral target detection

The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures class-specific discriminative information crucial for detection tasks. Spatio-spectral information has also been introduced into this framework via a joint sparsity model that simultaneously solves for the sparse features for a group of spatially local pixels, since such pixels are highly likely to have similar spectral characteristics. In this paper, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between these distinct sparse representations corresponding to different pixels in a spatial neighborhood. Simulation results show that the proposed algorithm outperforms classical hyperspectral target detection algorithms as well as support vector machines.
Kernel-based subpixel target detection in hyperspectral images

In This work we present a nonlinear realization of a signal detection approach that uses the generalized likelihood ratio tests (GLRTs). It is based on converting the linear mixture subspace model, so called matched subspace detector (MSD) into its corresponding nonlinear subspace model. The linear model for the GLRT of MSD is first extended to a high dimensional feature space (equivalent to a non-linear space in the input domain) and then the corresponding nonlinear GLRT expression is obtained. In order to address the intractability of the GLRT in the feature space we kernelize the nonlinear GLRT using kernel eigenvector representations as well as the kernel trick where dot products in the feature space are implicitly computed by kernels. The proposed kernel-based nonlinear detector, so called kernel matched subspace detector (KMSD), is applied to a given hyperspectral imagery - HYDICE (hyperspectral digital imagery collection experiment) images - to detect targets of interest. KMSD showed superior detection performance over MSD for the HYDICE images tested in this paper.
Image coding using a dynamic multi-layer address-vector quantization technique

A new vector quantization scheme for encoding monochrome and RGB color images is presented. The proposed technique is called dynamic multilayer address vector quantization (DMLAVQ) and is based on using vector quantization in a multilayer fashion. A three-layer coding system, where the codebook in the first layer is a standard vector quantization (VQ) codebook, and the codebooks in the other two layers are address codebooks with code vectors consisting of the addresses of the previous layer's codebook, is simulated. In the first layer, a standard VQ is performed on each block of the image. In the second layer, the resulting VQ addresses of the neighboring blocks are merged together to form a new vector which is said to be address vector quantized if this address combination vector can be found in an address codebook. This process of merging four nodes (neighboring addresses) is repeated recursively in a bottom-up hierarchical manner in the subsequent higher layers using the corresponding address codebook in each layer. The proposed technique is based on a tree-structured merging of the nodes of a quadtree in a bottom-up manner to form a larger block, provided a merging criterion is satisfied.<<ETX>>
Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

We present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.
Hierarchical Block Truncation Of Digital Hdtv Images

' 1 ABSTRACT L/ In this pap6 source coding technique is presented for compressing HDdgital images for B-ISDN visual services. The proposed source coding technique is called Hierarchicat JIock*Truncation Coding (HBTC). It is a combination of the Block Truncation Coding technique and a Quadtree segmenta%on method. The quadtree segmentation is used to decompose an image into homogeneous regions so that the BTC method can exploit the non-stationarity of the image dsta. Bit rates around /pixel achieved with this method.
An analysis of the robustness of deep face recognition networks to noisy training labels

In recent years, state-of-the-art face recognition performance has improved by using deep convolutional neural networks. One disadvantage of these methods is their need for very large, labeled training datasets as collecting and labeling them can be time consuming and prone to error. In this work we examine the robustness of a convolutional neural network to limited training data and training data with noisy labels. We train face recognition networks with varying training set sizes and varying amounts of mislabeled samples. Our experiments show data fidelity is significantly more important than training set size; decreasing the percentage of correctly labeled samples by ten is approximately equivalent to halving the number of training samples.
Comparison of two optimization techniques for channel assignment in cellular radio network

The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Two optimization methods, the neural networks and the genetic algorithms are applied to the channel assignment problem. To avoid falling into the local minima in the neural networks, certain techniques such as forced assignment and changing the cell list order, are used. In the genetic algorithms approach, the proper genetic operators are developed. All three constraints are also considered for the channel assignments: the co-channel constraint, the adjacent channel constraint and the co-site channel constraint. As simulation results, the average iteration (or generation) numbers, the convergence rates and the CPU times according to the various techniques for the two approaches are presented and compared.
Object recognition using multilayer Hopfield neural network

An object recognition approach based on concurrent coarse-and-fine matching using a multilayer Hopfield neural network is presented. The proposed network consists of several cascaded single-layer Hopfield networks, each encoding object features at a distinct resolution, with bidirectional interconnections linking adjacent layers. The interconnection weights between nodes associating adjacent layers are structured to favor node pairs for which model translation and rotation, when viewed at the two corresponding resolutions, are consistent. This interlayer feedback feature of the algorithm reinforces the usual intralayer matching process in the conventional single-layer Hopfield network in order to compute the most consistent model-object match across several resolution levels. The performance of the algorithm is demonstrated for test images containing single objects, and multiple occluded objects. These results are compared with recognition results obtained using a single-layer Hopfield network.