nils-herrmann/clean_scirepeval huggingface dataset:
A dynamic finite-state vector quantization scheme

A vector quantization (VQ) scheme with memory called dynamic finite-state vector quantization (DFSVQ) is described. In the proposed DFSVQ system, a super-codebook is designed using the generalized Lloyd algorithm. During encoding, an input vector subcodebook is dynamically generated by reordering the positions of the codevectors in the super-codebook. The codevectors that are the most probable approximations of the input vector are therefore moved to the top of the super-codebook. The first N codevectors of the super-codebook form the subcodebook for that input vector. An adaptive DFSVQ scheme is proposed in which the subcodebook is searched for a matching codevector satisfying a prespecified waveform distortion when encoding an input vector. If such a codevector is not found in the current subcodebook, the whole super-codebook is checked for the best match and a signaling flag along with the corresponding address of the best-matching codevector is transmitted to the receiver.<<ETX>>
Accurate and Timely Situation Awareness Retrieval from a Bandwidth Constrained Camera Network

Wireless cameras can be used to gather situation awareness information (e.g., humans in distress) in disaster recovery scenarios. However, blindly sending raw video streams from such cameras, to an operations center or controller can be prohibitive in terms of bandwidth. Further, these raw streams could contain either redundant or irrelevant information. Thus, we ask "how do we extract accurate situation awareness information from such camera nodes and send it in a timely manner, back to the operations center?" Towards this, we design ACTION, a framework that (a) detects objects of interest (e.g., humans) from the video streams, (b) combines these streams intelligently to eliminate redundancies and (c) transmits only parts of the feeds that are sufficient in achieving a desired detection accuracy to the controller. ACTION uses small amounts of metadata to determine if the objects from different camera feeds are the same. A resource-aware greedy algorithm is used to select a subset of video feeds that are associated with the same object, so as to provide a desired accuracy, for being sent to the operations center. Our evaluations show that ACTION helps reduce the network usage up to threefold, and yet achieves a high detection accuracy of &#x2248; 90%.
Time series processing of FLIR imagery for MTI and change detection

The paper addresses the problem of calibrating FLIR images of a scene that are acquired at different time points to construct information for moving target indication (MTI) and change detection. A signal model is developed to identify variations and imperfections of an FLIR sensor in time. This model is utilized to compensate for relatively slow variations of a bias in the FLIR sensor via a Fourier-based processing. Furthermore, a two-dimensional adaptive filtering method is developed to compensate for variations of the image point response (IPR) of a FLIR sensor as well as sub-pixel changes in the relative coordinates of the sensor-target over time. Results with time series FLIR, data of a scene with an airborne helicopter and a ground target are provided.
A modular neural network vector predictor for predictive image coding

In this paper, we present a modular neural network vector predictor that improves the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes, based on its directional variances. One expert predictor is optimized for stationary blocks, and each of the other four expert predictors are optimized to predict horizontal, vertical, 45 degrees , and 135 degrees diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is transmitted to the receiver about the selected predictor or the integration of the predictors. Experimental results show that the proposed scheme gives an improvement of 1.7 dB over a single multilayer perceptron (MLP) predictor. Furthermore, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over a single MLP predictor. The perceptual quality of the predicted images is also significantly improved.
Subband coding of video using an edge-based vector quantization technique for compression of the upper bands

A new subband video coding technique is introduced which utilizes several memoryless vector quantizing (VQ) schemes for encoding the various layers. A set of quadrature mirror filter banks was applied to the motion compensated frame differences (MCFDs) of the moving sequences to produce seven nonuniform bands. The upper bands displayed a significant amount of information at edge locations, these being the positions where the baseband of each layer was observed to have a similar behavior. Therefore, an edge-detecting operator, e.g., Laplacian or a Gaussian, was incorporated into the video compression model to extract the perceptually important locations of the upper bands from their correspondingly encoded baseband, thus eliminating the need for transmission of their addresses. Promising results were obtained which are suitable for low-bit-rate video applications where videophone and videoconferencing systems may be realized.<<ETX>>
Performance analysis of a vector quantizer with cache memory

Applies the cache memory scheme for an efficient implementation of a vector quantization system. The authors structure their vector quantizer to have a large "main" codebook and a smaller "cache" codebook. The main codebook holds all of the possible code vectors for the quantizer, while the cache codebook holds only those code vectors that have been used recently and are expected to be used again soon. The authors ran simulations for a fully associative cache codebook with least-recently-used replacement algorithm. Cache codebook hit ratios ranging from 60% to 90% were achieved with PSNR of 33-36 dB for line sizes of 1 to 8 and code vector dimension of 4. This scheme improves the encoding performance without significantly affecting the compression ratio and image quality.<<ETX>>
A nonlinear kernel-based joint fusion/detection of anomalies using Hyperspectral and SAR imagery

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 which explicitly exploits the higher order dependencies (nonlinear correlations) between the two sensor data through an appropriate kernel.
Hyperspectral Image Classification via Kernel Sparse Representation

In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.
A modular neural network vector predictor for predictive VQ

In this paper, we present a modular neural network vector predictor in order to improve the predictive component of a predictive vector quantization (PVQ) scheme. The proposed vector prediction technique consists of five dedicated predictors (experts), where each expert predictor is optimized for a particular class of input vectors. An input vector is classified into one of five classes based on its directional variances. One expert is optimized for stationary blocks, and each of the other four experts are optimized to predict horizontal, vertical, 45/spl deg/, and 135/spl deg/ diagonally oriented edge-blocks, respectively. An integrating unit is then used to select or combine the outputs of the experts in order to form the final output of the modular network. Therefore, no side information is required to transmit to the receiver about the predictor selection. Experimental results show that the proposed scheme gives an improvement of 1 to 1.5 dB better than a single multilayer perceptron (MLP) predictor. However, if the information about the predictor selection is sent to the receiver, the improvement could be up to 3 dB over the single MLP predictor. The perceptual quality of the predicted images is also significantly improved.
Kernel Matched Signal Detectors for Hyperspectral Target Detection

In this paper, we compare several detection algorithms that are based on spectral matched (subspace) filters. Nonlinear (kernel) versions of these spectral matched (subspace) detectors are also discussed and their performance is compared with the linear versions. These kernel-based detectors exploit the nonlinear correlations between the spectral bands that are ignored by the conventional detectors. Several well-known matched detectors, such as matched subspace detector, orthogonal subspace detector, spectral matched filter and adaptive subspace detector (adaptive cosine estimator) are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The detection algorithm 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. Experimental results based on simulated toyexamples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.
A joint compression-discrimination neural transformation applied to target detection

Many image recognition algorithms based on data-learning perform dimensionality reduction before the actual learning and classification because the high dimensionality of raw imagery would require enormous training sets to achieve satisfactory performance. A potential problem with this approach is that most dimensionality reduction techniques, such as principal component analysis (PCA), seek to maximize the representation of data variation into a small number of PCA components, without considering interclass discriminability. This paper presents a neural-network-based transformation that simultaneously seeks to provide dimensionality reduction and a high degree of discriminability by combining together the learning mechanism of a neural-network-based PCA and a backpropagation learning algorithm. The joint discrimination-compression algorithm is applied to infrared imagery to detect military vehicles.
A stereo vision technique using curve-segments and relaxation matching

A multichannel feature-based stereo vision technique is described in which curve segments are used as the feature primitives in the matching process. The left and right images are first filtered by using several Laplacian of Gaussian operators of different widths (channel). 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 images is evaluated. The R-table is used as a local feature vector in representing the distinctive characteristics of a segment. The epipolar constraint on the centroids of the curve segment and the channel size is used to limit the search space in the right image. To resolve the ambiguity of the false targets (multiple matches), a relaxation technique is used in which 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 segments.<<ETX>>
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification

Pixelwise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine. Recently, by incorporating additional structured sparsity priors, the second-generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependences between the neighboring pixels, the inherent structure of the dictionary, or both. In this letter, we review and compare several structured priors for sparse-representation-based HSI classification. We also propose a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.
Use of Hopfield network for stereo vision correspondence

An optimization approach is used to solve the correspondence problem for a set of features extracted from a pair of stereo images. A cost function is defined to represent the constraints on the solution which is then mapped onto a 2-D neural network for minimization. Each neuron in the network represents a possible match between a feature in the left image and one in the right image. Correspondence is achieved by initializing all the neurons that represent the possible matches and allowing the network to use the compatibility measures between the matched points to settle down into a stable state.<<ETX>>
Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition

Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation for heterogenous face recognition. This coupled network is unique, because a cross-modal transformation is learned by forcing the hidden units (latent features) of two neural networks to be as similar as possible, while simultaneously preserving information from the input. The effectiveness of this model is demonstrated using multiple existing heterogeneous face recognition databases. Moreover, the empirical results show that the learned image representation-common latent features-by the coupled auto-associative produces competitive cross-modal face recognition results. These results are obtained by training a softmax classifier using only the latent features from the source domain and testing using only the latent features from the target domain.
Graph-Based Sensor Fusion for Classification of Transient Acoustic Signals

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches.
Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery

This letter extends the idea of regularization to spectral matched filters. It incorporates a quadratic penalization term in the design of spectral matched filters in order to restrict 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 which is controlled by a parameter so-called the regularization coefficient. In this letter, the sum-of-squares of the filter coefficients is used as the regularization term, and different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also described which provides a procedure for choosing the regularization coefficient. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.
Variable-rate predictive residual vector quantizer

A major problem with a VQ-based image compression scheme is its codebook search complexity. Recently, a predictive residual vector quantizer (PRVQ) was proposed by Rizvi and Nasrabadi  1994). This scheme has a very low search complexity, and its performance is very close to that of the predictive vector quantizer (PVQ). The article presents a new VQ scheme called variable-rate PRVQ (VR-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed VR-PRVQ is found to give an excellent rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by the Joint Photographic Experts Group (JPEG).<<ETX>>
Stereo vision correspondence using a multichannel graph matching technique

A multichannel feature-based stereo vision technique is described wherein curve segments are used as the feature primitives in the matching process. Curve segments are extracted by tracking the zero-crossings of the left and right images. The generalized Hough transform of each curve and the length of the segment in the left image are used as a local feature vector to represent the distinctive characteristics of the segment. The feature vector of each segment is used as a constraint to find an instance of the same segment in the right image. The epipolar constraint on the centroids of the curve segment is used to limit the searching space in the right image. A relational graph is formed from the left image by treating the centroids as the nodes of the graph. The local features of the segments are used to represent the local properties of the nodes, and the relationship between the nodes represents the structural properties of the object in the scene. A similar graph is formed from the right image curve segments. A graph isomorphism is then formed between the two graphs.<<ETX>>
Anomaly Detection for Longwave FLIR Imagery Using Kernel Wavelet-RX

This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) Forward Looking Infrared (FLIR) imagery. The proposed approach called kernel wavelet-RX algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high dimensional feature space (possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high dimensional feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature space (kernelizing the wavelet-RX algorithm). In our kernel wavelet-RX algorithm, a 2-D wavelet transform is first applied to decompose the input image into uniform subbands. A number of significant subbands (high energy subbands) are concatenated together to form a subband-image cube. The kernel RX algorithm is then applied to these subband-image cubes obtained from wavelet decomposition of the LW database images. Experimental results are presented for the proposed kernel wavelet-RX, wavelet-RX and the classical CFAR algorithm for detecting anomalies (targets) in a large database of LW imagery. The ROC plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well as the classical CFAR detector.
Coupled dictionaries for thermal to visible face recognition

Thermal to visible face recognition is the problem of identifying a thermal infrared (IR) face image given a gallery of visible light face images. We attempt to solve this problem by learning coupled dictionaries to represent the two domains. The dictionaries provide a sparse representation which transforms the data into a single, domain-independent, latent space. We formulate the dictionary learning problem as a bi-level optimization problem and perform a stochastic gradient descent on the dictionaries to solve it. We present experimental results demonstrating the effectiveness of our approach.
Rate-constrained modular predictive residual vector quantization

This paper investigates a novel modular image coding paradigm using residual vector quantization (RVQ) with memory that incorporates a modular neural network vector predictor in the feedback loop. A modular neural network predictor consists of several expert networks, where each expert network is optimized for predicting a particular class of data, and an integrating unit that mixes the outputs of the expert networks in order to form the final output of the prediction system. The vector quantizer also has a modular structure. The proposed modular predictive RVQ (MPRVQ) is designed by imposing a constraint on the output rate of the system. Experimental results show that the modular PRVQ outperforms simple PRVQ by as much as 1 dB at low bit rates. Furthermore, for the same PSNR, the modular PRVQ reduces the bit rate by more than a half when compared to the JPEG algorithm.