nils-herrmann/clean_scirepeval huggingface dataset:
Face features extraction based on multi-scale LBP

How to extract the strong features of face image is vital important in the face recognition technology. The extracted features should be robust for variation of illumination and expression. a novel feature extraction algorithm based on wavelet decomposition and LBP is proposed, which makes use of the idea of wavelet multiresolution and local characteristic of LBP. And the features extracted by this way contain holistic and local information that can be robust to identify faces. Experiment results show that the proposed method can effectively be used in face recognition with single training sample per person. The performance is better than PCA and original LBP. And the importance of different level's lower coefficients is also analyzed.
A Bayesian-based probabilistic model for unconstrained handwritten offline Chinese text line recognition

A Bayesian-based probabilistic model is presented for unconstrained handwritten offline Chinese text line recognition. After pre-segmentation of a text line, plenty of invalid characters are produced which heavily interfere in the process of text line recognition. The proposed probabilistic model can incorporate isolated character recognition, character sample verification, and n-gram language model in a simple way, leading to more reliable recognition of a text line. When testing on HIT-MW database, experiments show that the proposed method can achieve character-level recognition accuracies of 63.19% without language model and 73.97% with bi-gram language model, respectively, outperforming the most recent results testing on the same dataset.
A virtual character recognition system based on optical detection of red light and its embedded implementation

This paper proposes a novel ldquoair-writingrdquo character recognition system (ACRS) based on optical detection of red light, with which a user can write a character in the air with a red light-emitting device. The trajectories of the light can be captured and detected by a camera during the writing process and then a character reconstruction algorithm is employed to convert them to a 2-D plan (as a virtual character trajectory) after a serials of preprocessing steps, including background analyzing and noise erasing. Finally, the virtual character trajectory is considered as a kind of inkless character pattern and recognized by a minimum classifier. To expand the application of our system, we implement an ARM7-based embedded ldquoair-writingrdquo character recognition system (E-ACRS). Experiments show that both ACRS and E-ACRS are able to recognize cursive digits and English alphabetical characters with encouraging accuracy.
Facial attractiveness prediction using psychologically inspired convolutional neural network (PI-CNN)

This paper proposes a psychologically inspired convolutional neural network (PI-CNN) to achieve automatic facial beauty prediction. Different from the previous methods, the PI-CNN is a hierarchical model that facilitates both the facial beauty representation learning and predictor training. Inspired by the recent psychological studies, significant appearance features of facial detail, lighting and color were used to optimize the PI-CNN facial beauty predictor using a new cascaded fine-tuning method. Experiments indicate that the cascaded fine-tuned PI-CNN predictor is robust to facial appearance variances, and obtains the highest correlation of 0.87 in the SCUT-FBP benchmark database, which is superior to the related hand-designed feature and related deep learning methods.
Multi-font printed Chinese character recognition using multi-pooling convolutional neural network

Although previous studies have achieved effective printed Chinese character recognition (PCCR) in the case a single font or a few different fonts, large scale multi-font PCCR remains a major challenge owing to the wide variety in the shape, layout, and grey-level distribution of single Chinese characters across different font styles. This paper applies multi-pooling and data augmentation with non-linear transformation to a convolutional neural network (CNN) for multi-font PCCR. We propose a multi-pooling layer on top of the final convolutional layer; this approach is found to be robust to spatial layout variations and deformations in multi-font printed Chinese characters. Experimental results show that multi-pooling significantly improves CNN performance. In addition, we adopt a distorted sample generation technique by applying non-linear warping functions along an original font image, which distorts the local density of image-based Chinese character strokes. We find that CNN performance is further boosted by the distorted samples technique. An input character image is transformed into four distorted images and the CNN learns the original image as well as the distorted samples to classify 3755 classes (level-1 set of GB2312-80) of printed Chinese characters in 280 widely varying fonts and 120 manually selected fonts. Outstanding recognition rates of 94.38% and 99.74% are achieved in the former and latter cases, respectively, which indicates the effectiveness of the proposed methods.
The Generic Object Classification Based on MIML Machine Learning

Multi-instance and Multi-Label (MIML) machine learning has been employed in the generic object classification for it's gracefully performance in solving the ambiguity of image. The whole image is regarded as a multi-instance bag. The image is separated into four parts, whose edge's histograms are calculated. These input vectors can be combined a multi-instance ones for adapting the MIML learning. The experimental results show that the average precise ratio of our method is higher 3% than one of the traditional Support Vector Machine method.
Time-memory tradeoffs in vector quantizer codebook searching based on decision trees

The paper presents several algorithms for designing fixed- and variable-depth decision trees for searching vector quantizer (VQ) codebooks. Two applications of such are explored. First, given a source vector, a tree can be used to find the closest codevector in the VQ codebook with many fewer arithmetic operations than the usual "full search." This decrease in complexity comes at the expense of an increase in auxiliary table storage. Second, the tree can be used as the first stage of fine-coarse vector quantization, which yields further savings in complexity at the cost of somewhat more storage and a small increase in distortion. The design methods involve incrementally growing trees with a variety of node splitting criteria and, subsequently, optimally pruning trees on the basis of performance functionals such as distortion, storage, and computational complexity. The pruning is done with the BFOS algorithm, which optimally trades one performance functional with another, and with an extension of the BFOS algorithm wherein one performance measure is traded with a combination of two others. The results of applying these methods to i.i.d. Gaussian, Gauss-Markov, and sampled speech sources at encoding rates of one and two bits per source sample demonstrate the tradeoffs achievable amongst time (complexity), memory (storage), and distortion.
Mismatched DPCM encoding of autoregressive processes

A method for computing the mean squared error distortion of differential pulse code modulation (DPCM) applied to Gaussian autoregressive sources is developed. This extends previous work wherein the code predictor was matched to the source. A two-dimensional version of the projection method for the computation of the stationary distribution of the joint source-state process is developed from which distortion can be readily evaluated. An iterative algorithm is used to optimize the quantizer for a given source and predictor. The results show that the matched predictor is very nearly optimal but not exactly so, and that DPCM is fairly robust to mismatch of the prediction coefficient to the correlation coefficient of a first-order autoregressive source.
Perceptual coding of images for halftone display

A new technique is presented for coding gray-scale images for facsimile transmission and printing on a laser printer. The authors use a gray-scale image encoder so that it is only at the receiver that the image is converted to a binary pattern and printed. The conventional approach is to transmit the image in halftoned form, using entropy coding (e.g. CCITT Group 3, or JBIG). The main advantages of the new approach are that it is possible to get higher compression rates and that the receiver can tune the halftoning process to the particular printer. A perceptually based subband coding approach is used.<<ETX>>
Transform Coding of Densely Sampled Gaussian Data

With mean-squared error D as a goal, it is well known that one may approach the rate-distortion function R(D) of a nonbandlimited, continuous-time Gaussian source by sampling at a sufficiently high rate, applying the Karhunen-Loeve transform to sufficiently long blocks, and then independently coding the transform coefficients of each type. In particular, the coefficients of a given type are ideally encoded with performance attaining a suitably chosen point on the first-order rate-distortion function of that type of coefficient. This paper considers a similar sample-and-transform coding scheme in which ideal coding of coefficients is replaced by coding with some specified family of quantizers, whose operational rate-distortion function is convex. A prime example is scalar quantization with entropy-coding and, if needed for convexity, time sharing. It is shown that when the sampling rate is large, the operational rate-distortion function of such a scheme comes within a finite constant of R(D). Applied to the scalar quantization family, the finiteness of this bound contrasts with a recent result showing that direct scalar quantization of samples (without a transform) has unbounded rate when distortion is held constant and sampling rate becomes large, even when the quantized samples are compressed to their entropy-rate. Thus, at high sampling rates, the transform reduces the loss due to scalar quantization from something infinite to something finite.
An Upper Bound to the Rate of Ideal Distributed Lossy Source Coding of Densely Sampled Data

Motivated by the question of the efficiency of dense sensor networks for sampling, encoding and reconstructing spatial random fields, this paper uses the Berger-Tung upper bound to the discrete-time distributed rate-distortion function and Grenander-Szego asymptotic eigenvalue theory to obtain an upper bound to the smallest possible rate when using distributed lossy encoding of densely spaced samples that is tighter than the bound recently obtained by Kashyap et al. Both bounds indicate that with ideal distributed lossy coding, dense sensor networks can efficiently sense and convey a field, in contrast to the negative result obtained by Marco et al. for encoders based on time- and space-invariant scalar quantization and ideal Slepian-Wolf distributed lossless coding
Wavelet transform synthetic texture VQ coding of SAR imagery

We present a novel vector quantizer (VQ) image coding algorithm, called synthetic texture VQ (STVQ), that employs shape-gain techniques to adaptively modify a synthetically-generated codebook to changing image statistics. We recursively apply STVQ to encode high-pass bands of a wavelet transform using the low-pass band as side information. An advantage of our approach is that no training is required to construct codebooks. We demonstrate STVQ on synthetic aperture radar (SAR) imagery and show that it provides efficient coding of the characteristic SAR image texture while preserving perceptual quality. Preliminary comparisons indicate that STVQ's perceptual performance exceeds JPEG at comparable rates, even though the mean-squared error performance of JPEG is better than STVQ.
Minimum conditional description length estimation for Markov random fields

In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph G = (V, E). Then, for a subset U ⊂ V, we estimate the parameters for nodes in U as well as for edges incident to a node in U, by finding the exponential parameter for that subset that yields the best compression conditioned on the values on the boundary ∂U. Our estimate is derived from a temporally stationary sequence of observations on the set U. We discuss how this method can also be applied to estimate a spatially invariant parameter from a single configuration, and in so doing, derive the Maximum Pseudo-Likelihood (MPL) estimate.
The Effect of Node Density and Propagation Model on Throughput Scaling of Wireless Networks

This paper derives a lower bound of the form ngamma-1 to the per-node throughput achievable by a wireless network when n source-destination pairs are randomly distributed throughout a disk of radius ngamma, 0 < gamma < 1/2 and propagation is modeled by an attenuation of the form 1/(1 + d)alpha, alpha > 2
Source-channel coding of analog data for digital magnetic recording

It is hypothesized that more data can be stored by increasing the density with which bits are placed on the media and using source-channel coding to both compress the data and protect it from the resulting increased bit error rate. This paper estimates the gains achievable with this approach. Central to this work is a model for the digital magnetic recording channel that contains a linear filter representing the Lorentzian step response and additive Gaussian processes representing electronic and media noises. The model differs from previous ones in that lineal bit density, as well as track density, are parameters that may be varied. Parameters for this model that typify disk storage systems are identified and the gains in data density for the proposed approach are estimated in two ways: by estimating the capacity of the channel model and by simulating a family of source-channel codes. From capacity bounds developed in a companion paper, it is estimated that data density may ultimately be increased by a factor of 20 or more. To assess the gains achievable with a practical system, a tree-structured family of source-channel codes is proposed and analyzed. It is found that these are much simpler than optimal unstructured source-channel codes, yet have comparable performance. By simulating them on a Gaussian source and the given channel model, it is estimated that gains on the order of 3 to 4 in data density are achievable with peak detection demodulation and gains of 2 to 3 are achievable with 1-D/sup 2/ partial response maximum likelihood demodulation, each with moderate increases in bit density. Comparable gains are anticipated for speech and images.
Strategies for quadtree predictive image coding

A new image coder is presented in which an image is divided into blocks, each block is quadtree segmented, and each segment is coded using a form of predictive coding. We provide nearly-optimal segmentation and quantization rules for this framework, as well as an iterative codebook design algorithm. In simulations, our new system comes within 1 dB of JPEG 2000 and outperforms JPEG by about 1.5 dB at moderate rates and up to 4.5 dB at low rates. Additionally, our method captures edges better and has potentially lower complexity than transform-based methods.
Coding for channels with cost constraints

We address the problem of finite-state code construction for the costly channel. This channel model is a generalization of the hard-constrained channel, also known as a subshift. Adler et al. (1986) developed the powerful state-splitting algorithm for use in the construction of finite-state codes for hard-constrained channels. We extend the state-splitting algorithm to the costly channel. We construct synchronous (fixed-length to fixed-length) and asynchronous (variable-length to fixed-length) codes. We present several examples of costly channels related to magnetic recording, the telegraph channel, and shaping gain in modulation. We design a number of codes, some of which come very close to achieving capacity.
Row-centric lossless compression of Markov images

Motivated by the question of whether the recently introduced Reduced Cutset Coding (RCC) ,  offers rate-complexity performance benefits over conventional context-based conditional lossless coding for sources with two-dimensional Markov structure, this paper compares several row-centric coding strategies that vary in the amount of conditioning as well as whether a model or an empirical table is used in the encoding of blocks of rows. The conclusion is that, at least for sources exhibiting low-order correlations, 1-sided model-based conditional coding is superior to the method of RCC for a given constraint on complexity, and conventional context-based conditional coding is nearly as good as the 1-sided model-based coding.
Distributions of local radius indices on periodic tessellations

This paper presents a theoretical analysis of a newly proposed texture similarity feature, Local Radius Index (LRI). LRI is designed to enable texture similarity metrics consistent with human perception to be based on it, and has been successfully applied in several applications, including perceptual image compression, identical texture retrieval and texture classification. In this paper, a theoretical analysis of its different versions, LRI-A and LRI-D, is presented for textures modeled as periodic tessellations. General formulas are derived to predict LRI-A and LRI-D feature vectors, which are histograms. The analysis reveals the fundamental relationship between LRI-A and LRI-D. Numerical results demonstrate the accuracy of the analysis.
On the performance of tree-structured vector quantization

Tree-structured vector quantization (TSVQ) is a commonly used structured vector quantization (VQ) technique that permits much faster quantization and incurs a little more distortion than full search of an optimal unstructured VQ. By exploiting two recently developed asymptotic quantization formulas it is shown that the mean-squared error of TSVQ and, moreover, the probability density of the magnitude of its errors support the hypothesis that TSVQ has an optimal point density and that its cells consist of various rectangular-like polyhedra.<<ETX>>
Perceptual coding of images for halftone display.

The authors present a new technique for coding gray-scale images for facsimile transmission and printing on a laser printer. They use a gray-scale image encoder so that it is only at the receiver that the image is converted to a binary pattern and printed. The conventional approach is to transmit the image in halftoned form, using entropy coding (e.g., CCITT Group 3 or JBIG). The main advantages of the new approach are that one can get higher compression rates and that the receiver can tune the halftoning process to the particular printer. They use a perceptually based subband coding approach. It uses a perceptual masking model that was empirically derived for printed images using a specific printer and halftoning technique. In particular, they used a 300 dots/inch write-black laser printer and a standard halftoning scheme ("classical") for that resolution. For nearly transparent coding of gray-scale images, the proposed technique requires lower rates than the standard facsimile techniques.
Image Analysis: Focus on Texture Similarity

Texture is an important visual attribute both for human perception and image analysis systems. We review recently proposed texture similarity metrics and applications that critically depend on such metrics, with emphasis on image and video compression and content-based retrieval. Our focus is on natural textures and structural texture similarity metrics (STSIMs). We examine the relation of STSIMs to existing models of texture perception, texture analysis/synthesis, and texture segmentation. We emphasize the importance of signal characteristics and models of human perception, both for algorithm development and testing/validation.
Block and sliding-block source coding

A new form of source coding subject to a fidelity criterion, called sliding-block coding, has recently been introduced by Gray, Neuhoff, and Ornstein. A method of conversion from block coding to sliding-block coding, or vice versa, with an arbitrarily small increase in rate is shown for ergodic sources. The ergodic theorem is used to convert from sliding-block codes to block codes. A theorem similar to one of Rohlin is used to convert from block codes to sliding-block codes. A proof of this theorem and some discussion of the nonergodic case appear in the appendices.
Model-based Halftoning

A model-based printing method and system for generating halftone output images corresponding to gray-scale-coded input signals. Models for individual two-level (e.g., black on white) printer types allow predicted printer error signals to be generated which can be used to modify the gray-scale coded signals in such manner as to produce binary signals which, when applied to the printer, create halftone images of enhanced quality. In an alternative embodiment binary signals are selected which minimize an error function based on the difference between (i) a predicted perceived image corresponding to the gray scale inputs as filtered by an eye-model filter and (ii) the halftone image resulting from filtering of the binary sequence by a filter modeling the printer followed by the eye-model filter.
Printer models and error diffusion

A new model-based approach to digital halftoning is proposed. It is intended primarily for laser printers, which generate "distortions" such as "dot overlap". Conventional methods, such as clustered-dot ordered dither, resist distortions at the expense of spatial and gray-scale resolution. The proposed approach relies on printer models that predict distortions, and rather than merely resisting them, it exploits them to increase, rather than decrease, both spatial and gray-scale resolution. We propose a general framework for printer models and find a specific model for laser printers. As an example of model-based halftoning, we propose a modification of error diffusion, which is often considered the best halftoning method for CRT displays with no significant distortions. The new version exploits the printer model to extend the benefits of error diffusion to printers. Experiments show that it provides high-quality reproductions with reasonable complexity. The proposed modified error diffusion technique is compared with Stucki's (1981) MECCA, which is a similar but not widely known technique that accounts for dot overlap. Model-based halftoning can be especially useful in transmission of high-quality documents using high-fidelity gray-scale image encoders.
Near-lossless compression of medical images

This paper studies the characteristic properties of a specific class of near-lossless image compression schemes which consists of a lossless coder followed by a uniform scalar quantizer. Three specific instances of such schemes are investigated; namely, differential pulse code modulation, hierarchical interpolation, and two-dimensional space-varying multiplicative autoregressive coders. The compression gains attainable with such schemes are studied and results of experiments conducted on several medical images are presented.