nils-herrmann/clean_scirepeval huggingface dataset:
Resource-Aware Joint Source-Channel Multiple Description Estimation and Compression in Networks

We present a joint source-channel multiple description (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into K descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. To suit heterogeneous decoders the proposed JSC-MD approach is made resource- scalable. Powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum a posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC- MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions, while the JSC-MD MAP decoder is an algorithm of the longest path in a weighted directed acyclic graph. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8 dB). The new JSC-MD framework also enjoys an operational advantage over the existing MDQ decoders. It eliminates the need for as many as 2K-1 side decoders to handle different combinations of the received descriptions by unifying the treatments of all these possible cases.
Adaptive binary vector quantization using Hamming codes

Hamming codes are studied as a means of adaptive vector quantization of binary images. The idea is to minimize, within the equivalence class of a Hamming code, the expected quantization distortion, while bounding the maximum distortion per vector to prevent burst quantization errors in a binary image. Some interesting and useful relationships between distinct Hamming codes are presented. These findings can lead to efficient algorithms for designing adaptive binary vector quantizers whose codebooks can adapt to sources of smoothly changing statistics.
High-performance 3-D embedded wavelet video (EWV) coding

This paper presents a rate-distortion (R-D) optimized 3-D embedded wavelet video (EWV) coder by extending the concept of EBCOT from 2-D to 3-D. After a lifting based 3-D wavelet transform, different subbands are coded independently using bit plane coding with different context models to provide flexible scalability in both spatial and temporal domain. A global R-D optimization procedure is used to generate an embedded bitstream for a target bit rate. Experiments show that, even without motion estimation, the EWV coder outperforms both MPEG-4 and 3-D ESCOT for most low motion video sequences.
Illumination invariant feature based on neighboring radiance ratio

In many object recognition applications, especially in face recognition, varying illuminations can adversely affect the robustness of the object recognition system. In this paper, we propose a novel illumination invariant feature called Neighboring Radiance Ratio (NRR) which is insensitive to both intensity and direction of light. NRR is derived and analyzed based on a physical image formation model. The computation of NRR does not need any prior information or any training data and NRR is far less sensitive to the border of shadows than most existing methods. The analysis of the illumination invariance of NRR is also presented. The proposed NRR feature is tested on Extended Yale B and CMU-PIE databases and compared with several previous methods. The experimental results corroborate our analysis and demonstrate that NRR is highly robust image feature against illumination changes.
Multichannel guided image filter

Guided image filter (GIF) is an edge-preserving filtering technique that smooths the fine texture of an input image with the guide of a second image. One shortcoming of GIF and all its existing variants, such as, weighed guided image filter and gradient domain guided image filter, is that they only use one grayscale image as the guide and are consequently unable to fully utilize the rich information offered by multichannel images. In this paper, we extend GIF for multichannel guidance image and propose a novel correlation detection technique for retaining sharp edges with opposite gradient directions in the different channels of the same guidance image. Experimental results show that the proposed method preserves the details from both the input and guidance images better than existing GIF techniques.
Soft binary segmentation-based backlit image enhancement

This paper is concerned with the enhancement of backlit images by compensating for abnormal illumination conditions. The underexposed (backlit) or/and overexposed regions in a backlit image are identified by a soft binary segmentation process that is driven by a Gaussian mixture model. Optimal tone-mapping is performed on the under- and over-exposed regions separately to improve the visual quality. Experimental results demonstrate the efficacy of the proposed restoration method and its advantages over existing image enhancement algorithms in perceptual quality.
Sparsity-based soft decoding of compressed images in transform domain

We propose a sparsity-based soft decoding approach to restore compressed images directly in the transform domain of compression (DCT domain specifically examined in this paper). Restoring transform coefficients rather than pixel values prevents the propagation of quantization errors in the image domain. As natural images are statistically non-stationary with spatially varying sparse representations, we develop an adaptive block-wise sparsity-based restoration method that learns and exploits local statistics. Specially, for each DCT block, we collect sample blocks via non-local patch grouping to learn a compact dictionary based on principal component analysis. The resulting block-specific dictionary is used to estimate the corresponding DCT coefficients by a technique of collaborative sparse coding, in which the similarity between sample DCT patches used in dictionary construction is further considered. Experimental results are encouraging and demonstrate that the proposed soft decoding approach performs competitively on restoring compressed images against existing methods.
Context quantization by kernel Fisher discriminant

Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD). In particular, the KFD can describe linearly nonseparable quantizer cells by projecting input context vectors onto a high-dimensional curve, in which these cells become better separable. The new algorithms outperform the previous linear Fisher discriminant method for context quantization. They approach the minimum empirical conditional entropy context quantizer designed in the probability simplex space, but with a practical implementation that employs a simple scalar quantizer mapping function rather than a large lookup table.
Compressive-uniform hybrid sensing for image acquisition and communication

We propose a new image acquisition and recovery strategy of hybrid sensing (HS) that combines random sampling of compressive sensing (CS) and uniform down sampling. HS lets the two sampling schemes complement each other so that one can have the best of both worlds: signal-independent sparse sampling that is the hallmark of CS, and locally adaptive signal reconstruction that is afforded by uniform sampling. We suggest a few important applications of HS in image acquisition and communication, such as multispectral imaging, multiple description image coding, multiview video, and ultra-high throughput imaging (e.g., functional medical imaging).
Adaptive structured recovery of compressive sensing via piecewise autoregressive modeling

In compressive sensing (CS) a challenge is to find a space in which the signal is sparse and hence recoverable faithfully and efficiently. Given the nonstationarity of many natural signals such as images, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive, signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary most CS methods seek for a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem we propose a new framework for model-guided adaptive recovery of compressive sensing (MARX), and show how a piecewise autoregressive model can be integrated into the MARX framework to adapt to changing second order statistics of a signal in CS recovery. In addition, MARX offers a powerful mechanism of characterizing and exploiting structured sparsities of a signal, greatly restricting the CS solution space. A case study on CS-acquired images shows that the proposed MARX technique can increase the reconstruction quality by up to 8 dB over existing methods.
Alphabet Partitioning Techniques for Semiadaptive Huffman Coding of Large Alphabets

Practical applications that employ entropy coding for large alphabets often partition the alphabet set into two or more layers, and encode each symbol by using some suitable prefix coding for each layer. In this paper, we formulate the problem of finding an alphabet partitioning for the design of a two-layer semiadaptive code as an optimization problem, and give a solution based on dynamic programming. However, the complexity of the dynamic programming approach can be quite prohibitive for a long sequence and a very large alphabet size. Hence, we also give a simple greedy heuristic algorithm whose running time is linear in the length of the input sequence, irrespective of the underlying alphabet size. Although our dynamic programming and greedy algorithms do not provide a globally optimal solution for the alphabet partitioning problem, experimental results demonstrate that superior prefix coding schemes for large alphabets can be designed using our new approach
Conditional entropy coding of VQ indexes for image compression

Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.
Analysis on spectral effects of dark-channel prior for haze removal

In solving the inverse problem of haze removal, the most commonly used prior in the literature is perhaps that of dark channel, which assumes that at least one pixel in a small patch has a zero or near zero intensity level in one of the RGB color channels. However, this assumption is not physically based; it can be significantly off from the reality because most colors in outdoor natural scenes are unsaturated (e.g., the sky). Chances are that none of the R, G, B values in a patch of the latent image is close to zero. This paper offers detailed analysis on the effects of invalid dark channel assumption on dehazed images; in particular, it reveals the causes and behavior of spectral distortions that are inherent to the dark channel type of dehazing methods.
Context Modeling and Correction of Quantization Errors in Prediction Loop

In lossy predictive coding of Differential Pulse Code Modulation (DPCM) type, quantization performed in the prediction loop induces propagation of quantization errors, resulting in biased predictions of the subsequent samples. In this work, we aim to alleviate the negative effect of quantization errors on the robustness of prediction. We propose some practical techniques for context modeling of quantization errors and cancellation of estimation biases in the DPCM reconstruction. The resulting refined estimates are fed into the prediction to improve coding efficiency. When applied to 1D audio and 2D image signals, the proposed techniques can reduce the bit rate and at the same time improve the PSNR performance significantly.
On Design of Linear Minimum-Entropy Predictor

Linear predictors for lossless data compression should ideally minimize the entropy of prediction errors. But in current practice predictors of least-square type are used instead. In this paper, we formulate and solve the linear minimum-entropy predictor design problem as one of convex or quasiconvex programming. The proposed minimum-entropy design algorithms are derived from the well-known fact that prediction errors of most signals obey generalized Gaussian distribution. Empirical results and analysis are presented to demonstrate the superior performance of the linear minimum-entropy predictor over the traditional least-square counterpart for lossless coding.
Text detection and segmentation in complex color images

Text is a very powerful index in content-based image and video indexing. We propose a new text detection and segmentation algorithm that is especially designed for being applied to color images with complicated background. Our goal is to minimize the number of false alarms and to binarize efficiently the detected text areas so that they can be processed by standard OCR software. First, potential areas of text are detected by enhancement and clustering processes, considering most of constraints related to the texture of words. Then, classification and binarization of potential text areas are achieved in a single scheme performing color quantization and characters periodicity analysis. We report a high rate of good detection results with very few false alarms and reliable text binarization.
A Dictionary-Learning Sparse Representation framework for pose classification

This paper proposes a Dictionary-Learning Sparse Representation framework (DLSR) to deal with face pose estimation in noise, bad illumination and low-resolution cases. Sparse and redundant modelling of data assumes an ability to describe signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the choice of the dictionary that sparsifies the signals is crucial for the success of this pose estimation problem. The proposed approach models the appearance of face images from the same pose via a sparse model which learns the dictionary D from a set of image patches with the objective to minimize the reconstruction error of the target image, in order to coincide with the pose classification criterion. Then, the combination of the trained dictionaries of all pose classes are used as an over-complete dictionary for sparse representation and classification. Experimental results demonstrate the effectiveness of the proposed Dictionary-Learning Sparse Representation framework for treating the pose classification in dynamic illumination condition and low-resolution images.
ALIF: A dataset for Arabic embedded text recognition in TV broadcast

This paper proposes a dataset, called ALIF, for Arabic embedded text recognition in TV broadcast. The dataset is publicly available for a non-commercial use. It is composed of a large number of manually annotated text images that were extracted from Arabic TV broadcast. It is the first public dataset dedicated to the development and the evaluation of video Arabic OCR techniques. Text images in the dataset are highly variable in terms of text characteristics (fonts, sizes, colors...) and acquisition conditions (background complexity, low resolution, non-uniform luminosity and contrast...). Moreover, an important part of the dataset is finely annotated, i.e. the text in an image is segmented into characters, paws and words, and each segment is labeled. The dataset can hence be used for both segmentation-based and segmentation-free text recognition techniques. In order to illustrate how the ALIF dataset can be used, the results of an evaluation study that we have conducted on different techniques for Arabic text recognition are also presented.
Siamese neural network based similarity metric for inertial gesture classification and rejection

In this paper, we tackle the task of symbolic gesture recognition using inertial MicroElectroMechanicals Systems (MEMS) present in Smartphones. We propose to build a non-linear similarity metric based on a Siamese Neural Network (SNN), trained using a new error function that models the relations between pairs of similar and dissimilar samples in order to structure the network output space. Experiments performed on different datasets regrouping up to 22 individuals and 18 gesture classes, targeting the most likely real case applications, show that this structure allows for an improved classification and a higher rejection quality over the conventional MultiLayer Perceptron (MLP) and Dynamic Time Warping (DTW) similarity metric.
A probabilistic Self-Organizing Map for facial recognition

This article presents a method aiming at quantifying the visual similarity between an image and a class model. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to label a self-organizing map (SOM) to measure image similarity. To manage this goal, we feed local signatures associated to the regions of interest into the neural network. At the end of the learning step, each neural unit is tuned to a particular local signature prototype. During the labeling process, each image signature presented to the network generates an activity vote for its referent neuron. Facial recognition is then performed by a probabilistic decision rule. This scheme offers very promising results for face identification dealing with illumination variation and facial poses and expressions.
PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects

In this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-the-art tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient implementation, and thus tracking is very fast.
Handwritten word spotting based on a hybrid optimal distance

In this paper, we develop a comprehensive representation model for handwriting, which contains both morphological and topological information. An adapted Shape Context descriptor built on structural points is employed to describe the contour of the text. Graphs are first constructed by using the structural points as nodes and the skeleton of the strokes as edges. Based on graphs, Topological Node Features (TNFs) of n-neighbourhood are extracted. Bag-of-Words representation model based on the TNFs is employed to depict the topological characteristics of word images. Moreover, a novel approach for word spotting application by using the proposed model is presented. The final distance is a weighted mixture of the SC cost, and the TNF distribution comparison. Linear Discriminant Analysis (LDA) is used to learn the optimal weight for each part of the distance with the consideration of writing styles. The evaluation of the proposed approach shows the significance of combining the properties of the handwriting from different aspects.
Arabic text detection in videos using neural and boosting-based approaches: Application to video indexing

Text detection in videos is a primary step in any semantic-based video analysis systems. In this work, we propose and compare three machine learning-based methods for embedded Arabic text detection. These methods are able to detect Arabic text regions without any prior knowledge and without any pre-processing. The first method relies on a convolution neural network. The two other methods are based on a multi-exit asymmetric boosting cascade. The proposed methods have been extensively evaluated on a large database of Arabic TV channel videos. Experiments highlight a good detection rate of all methods even though neural network-based method outperforms the other ones in terms of recall/precision and computation time.
Joint denoising and magnification of noisy Low-Resolution textual images

Current issues on textual image magnification have been focused on noise-free low-resolution images. Nevertheless, real circumstances are far from these assumptions and existing systems are generally confronted with noisy images; limiting thus the efficiency of the magnification process. The scope of this study is to propose a joint denoising and magnification system based on sparse coding to tackle such a problem. The underlying idea suggests the representation of an image patch by a linear combination of few elements from a suitable dictionary. The proposed system uses both online and offline learned dictionaries that are selected adaptively for each image patch of the input Low-Resolution (LR) noisy image to generate its corresponding noise-free High-Resolution (HR) version. In fact, the online learned dictionaries are trained on a clustered dataset of the image patches selected from the input image and used for the denoising purpose in order to take benefit of the non-local self-similarity assumption in textual images. For the offline learned dictionaries, they are trained on an external LR/HR image patch pair dataset and employed for the magnification purpose. The performance of the proposed system is evaluated visually and quantitatively on different LR noisy textual images and promising results are achieved when compared with other existing systems and conventional approaches dealing with such kind of images.