nils-herrmann/clean_scirepeval huggingface dataset:
A blind code timing estimator and its implementation for DS-CDMA signals in unknown colored noise

Commonly, the channel noise is assumed as temporally white when we estimate the PN code timing for direct-sequence code-division multiple-access (DS-CDMA) systems. However, it may be invalid in practice due to, for instance, the presence of some narrow-band interference or lumping the secondary users into the noise. In this paper, we introduce the matrix decomposition technique in a subspace-based code timing estimator. The new code timing estimator can robustly work in unknown colored noise. The Cramer-Rao bound for the code timing estimator is outlined. Furthermore, we propose a practical implementation method, which includes reducing the number of eigendecompositions and an adaptive eigendecomposition algorithm based on subspace tracking using the unconstrained gradient-descent technique. The performance of the new method is evaluated by computer simulation and compared with the MUSIC method. It is proved that the proposed code timing estimation algorithm outperforms the MUSIC method in colored noise or when the number of users is large.
On Branded Handbag Recognition

Manufacturing branded handbags is a big business in the fashion world. Shoppers' feedback showing photos of their purchased handbags in social networks or blogs is important for branding purposes. In this paper, we deal with handbag recognition. It is a challenging problem due to the inter-class style similarity and the intra-class color variation. We focus on developing discriminative representations of handbag style and color. For handbag style representation, two supervised mid-level patch selection procedures are proposed to select discriminative patches, regarding individual classes and pairwise classes. We also propose a low-level complementary feature, extracted from texture-enhanced mid-level patches, to capture the fine details of the mid-level patches. For handbag color representation, we propose to extract dominant color features to handle the illumination changes. The performance of our proposed method is evaluated on a newly built branded handbag dataset. The results show that our method performs favorably in recognizing handbags, with around 10% improvement in accuracy when compared with the existing fine-grained or generic object recognition methods.
Category-Separating Strategy for branded handbag recognition

In recent years, computer vision community has devoted efforts on the recognition of basic-level categories. On the other hand, finegrained object recognition which targets at recognizing objects belonging to the same basic-level class, is a more challenging problem and receives an increasing attention during recent years. In this paper, we propose a hierarchical structure Category-Separating Strategy for branded handbag recognition which is the first attempt to address the fine-grained object recognition on branded handbags. Experimental results on a newly constructed dataset are provided to show the effectiveness of the proposed methodology.
A new channel estimation method for RF beamforming implementable in mobile terminals

This paper investigates RF beamforming techniques for improving downlink performance by applying a two-element array at the mobile terminal. A new channel estimation method suitable for RF beamforming is proposed, and further applied to the systems with combined transmit diversity and RF beamforming receive diversity. Computer simulations have shown that with the new channel estimation method, RF beamforming receive diversity techniques can improve the BER performance considerably, especially for fast fading environments.
Digital image-in-image watermarking for copyright protection of satellite images using the fast Hadamard transform

In this paper, a robust and efficient digital image watermarking algorithm using the fast Hadamard transform (FFIT) is proposed for the copyright protection of satellite images. This algorithm can embed or hide an entire image or pattern as a watermark such as a company's logo or trademark directly into the original satellite image. The performance of the proposed algorithm is evaluated using a benchmarking tool called Stirmark. Results show that this algorithm is very robust and can survive up to 60% of all Stirmark attacks. These attacks were tested on a number of satellite test images of size 512/spl times/512/spl times/8 bit, embedded with a watermark image of size 64/spl times/64/spl times/8 bits. The simplicity of the fast Hadamard transform also offers a significant advantage in shorter processing time and ease of hardware implementation.
Secure data hiding in binary document images for authentication

In this paper, we present a data hiding algorithm for binary document images. This algorithm is based on the Distance-Reciprocal Distortion Measure that is used to evaluate the amount of distortion caused by flipping a particular pixel in binary document images. The pixels that will cause less distortion after flipping are preferred candidates for flipping. We do the embedding by enforcing the odd-even features of non-uniform blocks and employ a 2-D shifting to provide security for tamper proofing and authentication. Experiments show that the watermark-embedded document image has good quality and tampering of content can be detected successfully.
Text document filters using morphological and geometrical features of characters

This paper aims at irregular noise removal in binary document images. A new filtering algorithm called the modified directional morphological filter (MDMF) is introduced with dual properties for eliminating document salt-and-pepper noises, and for remedying eroded character stroke distortion. For eliminating larger noise, an enhanced algorithm called the image geometric structure filter (IGSF) is proposed based on the geometric stroke information of the characters. The IGSF can especially be used for noise removal in alphanumeric documents. Experiments demonstrate that the performance of combining two filters is robust, which can feasibly filter out various isolated noises in the binary images.
Fast computation of R-dimensional DHT with size Q(L/sub 1/)/spl times/Q(L/sub 2/)/spl times/.../spl times/Q(L/sub R/)

Fast algorithms are presented for multi-dimensional discrete Hartley transform (MD-DHT) with size q(l/sub 1/)/spl times/q(l/sub 2/)/spl times/.../spl times/q(l/sub r/), where q is an odd prime number, and r>1 is the number of dimensions. By using the multi-dimensional polynomial transform, the MD-DHT can be converted into a series of reduced one-dimensional DHTs. Compared to other fast algorithms, the proposed one substantially reduces the overall computational complexity and has a simple computational structure.
Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.
Benchmarking Single-Image Reflection Removal Algorithms

Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches using the same dataset has never been conducted due to the lack of suitable benchmark data with ground truth. This paper presents the first captured Single-image Reflection Removal dataset ‘SIR2’ with 40 controlled and 100 wild scenes, ground truth of background and reflection. For each controlled scene, we further provide ten sets of images under varying aperture settings and glass thicknesses. We perform quantitative and visual quality comparisons for four state-of-the-art single-image reflection removal algorithms using four error metrics. Open problems for improving reflection removal algorithms are discussed at the end.
A novel approach for partial blur detection and segmentation

This paper proposes a novel approach for partial blur detection and segmentation. The local blur kernels of image blocks are firstly estimated and then a reblurring technique is used to measure relative blur degrees of the local blur kernels. The output of reblurring is a metric to classify blurred and non-blurred image blocks. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of blurred and non-blurred regions. Our approach is evaluated for out-of-focus and motion blurred images. The experimental results show that the proposed approach detects and segments the blurred and non-blurred regions in partial blurred images with 88% accuracy for natural out-of-focus blur, 86% accuracy for artificial out-of-focus blur and 83% accuracy for artificial motion blur, which outperforms the state-of-the-art approaches of partial blur detection and segmentation.
An Improved Scheme for Full Fingerprint Reconstruction

Different fingerprint recognition systems store minutiae-based fingerprint templates differently. Some store them inside a small token; some can be found in a server database. As the minutiae template is very compact, many take it for granted that the template does not contain sufficient information for reconstructing the original fingerprint. This paper proposes a scheme to reconstruct a full fingerprint image from the minutiae points based on the amplitude and frequency modulated (AM-FM) fingerprint model. The scheme starts with generating a binary ridge pattern which has a similar ridge flow to that of the original fingerprint. The continuous phase is intuitively reconstructed by removing the spirals in the phase image estimated from the ridge pattern. To reduce the artifacts due to the discontinuity in the continuous phase, a refinement process is introduced for the reconstructed phase image, which is the combination of the continuous phase and the spiral phase (corresponding to the minutiae). Finally, the refined phase image is used to produce a thinned version of the fingerprint, from which a real-look alike gray-scale fingerprint image is reconstructed. The experimental results show that our proposed scheme performs better than the-state-of-the-art technique.
Cross-domain shoe retrieval using a three-level deep feature representation

In this paper, we address the problem of matching the shoes from the daily life photos to exactly the same shoes from online shops. The problem is extremely challenging because of the significant visual differences between street domain images (shoe images captured in the daily life scenario) and online domain photos (images from online shops taken in the controlled environment). This paper presents a semantic Shoe Attribute-Guided Convolutional Neural Network (SAG-CNN) to extract the deep features. Moreover, we develop a three-level feature representation based on SAG-CNN. The deep features extracted from the image, region and part levels effectively match the images across different domains. We collect a novel shoe dataset, which consists of 8021 street domain and 5821 online domain images. The experimental results on our dataset show that the top-20 retrieval accuracy of our approach improves over that using the pre-trained CNN features by about 40%.
Joint learning for image-based handbag recommendation

Fashion recommendation helps shoppers to find desirable fashion items, which facilitates online interaction and product promotion. In this paper, we propose a method to recommend handbags to each shopper, based on the handbag images the shopper has clicked. This is performed by Joint learning of attribute Projection and One-class SVM classification (JPO) based on the images of the shopper's preferred handbags. More specifically, for the handbag images clicked by each shopper, we project the original image feature space into an attribute space which is more compact. The projection matrix is learned jointly with a one-class SVM to yield a shopper-specific one-class classifier. The results show that the proposed JPO handbag recommendation performs favorably based on initial subject testing.
Identification of recaptured photographs on LCD screens

With advances in image display technology, recapturing good-quality images from the high-fidelity artificial scenery on a LCD screen becomes possible. Such image recapturing posts a security threat, which allows the forgery images to bypass the current forensic systems. In this paper, we first recapture some good-quality photos on different LCD screens by properly setting up the recapturing environment and tuning the controllable settings. In a perceptional study, we find that such finely recaptured images can hardly be identified by human eyes. To prevent the image recapturing attack, we propose a set of statistical features, which capture the common anomalies introduced in the camera recapturing process on LCD screens. With a probabilistic support vector machine classifier, comparison results show that our proposed features work very well, which outperform the conventional image forensic features in identification of the finely recaptured images.
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
Histogram-Based Fast Text Paragraph Image Detection

Rumormongers always use long paragraphs to spread slanderous stories so that they can convince readers. Those illegal or sensitive rumors uploaded into the internet can be written on images to by-pass text filters. These images can be detected by existing filters such as OCR, but the detection is very time consuming. To prohibit the dissemination of those commentaries, detecting whether an image contains a sufficient amount of words provides convenience to the government or internet service providers. Because of this, we focus on developing a fast pre-processor algorithm for detecting images embedded with sufficient text, such that the text filters (e.g. OCR) only need to focus on those suspected images. In this paper, we propose a histogram-based fast detection method to determine whether an image contains paragraphs of text or not. Binary histograms are extracted from the converted binary images. Then, due to the periodic pattern of the histograms, a step curve is designed to apply on the autocorrelation of those histograms. The area under the curve is further utilized to differentiate images with paragraphs and those without. To imitate the scenario, we construct a new dataset covering more than 2000 images of with and without paragraphs. The results show the effectiveness of the proposed detection system, which achieves 99.5% in accuracy and 15 millisecond per image in speed implemented in C++.
Information Retrieval Model for Online Handwritten Script Identification

Script identification has always been a topic of much research interest in the field of document analysis. The accurate determination of the identity of the script is paramount to many post-processing steps such as document sorting, translation and in determining the choice of linguistic resources to use for OCR or handwriting recognition. However, few works exist with regards to the identification of online handwritten scripts, partly due to the large variations and challenges innate in handwritten scripts. This paper proposes a novel approach for online handwritten script identification based on the Information Retrieval model. We attempt to identify among three script families; Arabic, Roman and Tamil scripts, which attained an average accuracy of 93.3% from our results. This signifies promising potential in utilizing Information Retrieval models for script identification.
Objective Distortion Measure for Binary Text Image Based on Edge Line Segment Similarity

This paper proposes a new approach to measure the distortion introduced by changing individual edge pixels in binary text images. The approach considers not only how many pixels are changed but also where the pixels are changed and how the flipping affects the overall shape formed by the edge line. Similarities between the edge line segments in the original and distorted image are compared to measure the distortion. Subjective testing shows that the new distortion measure correlates well with human visual perception
Noncoherent sequence detection of differential space-time modulatio

Approximate maximum-likelihood noncoherent sequence detection (NSD) for differential space-time modulation (DSTM) in time-selective fading channels is proposed. The starting point is the optimum multiple-symbol differential detection for DSTM that is characterized by exponential complexity. By truncating the memory of the incremental metric, a finite-state trellis is obtained so that a Viterbi algorithm can be implemented to perform sequence detection. Compared to existing linear predictive receivers, a distinguished feature of NSD is that it can accommodate nondiagonal constellations in continuous fading. Error analysis demonstrates that significant improvement in performance is achievable over linear prediction receivers. By incorporating the reduced-state sequence detection techniques, performance and complexity tradeoffs can be controlled by the branch memory and trellis size. Numerical results show that most of the performance gain can be achieved by using an L-state trellis, where L is the size of the DSTM constellation.
Step-edge reconstruction using 2D finite rate of innovation principle

Parametric signals that have a finite number of degrees of freedom per unit of time are defined as signals with Finite Rate of Innovation (FRI). Sampling and reconstruction schemes have been developed based on the 1D FRI principle and applied to reconstructing step edge images on a row by row basis. In this paper, we derive the 2D FRI principle by exploiting the separability of the B-spline sampling kernel. The proposed 2D FRI principle regards the sampling and reconstruction as block by block operations. The step-edge parameters can be retrieved in high accuracy with no post-processing. The performance on synthetic images shows that our proposed technique is more precise than the row by row approaches on Signal-to-Noise Ratio (SNR) levels larger than 4 dB. Experimental results on real images demonstrate that the proposed method can reconstruct the step-edge precisely under noisy and practical sampling conditions.
Tagging the shoe images by semantic attributes

With the rapid proliferation of Internet, it becomes a great challenge to annotate explosive number of objects manually. Especially for the fashion domain where a massive collection of new products come up everyday. Therefore, to save human labor, it is essential to develop an automatic tagging system for those fashion products in a variety of appearances. In this paper, we focus on addressing the issue of automatic shoe tagging where a novel system is proposed to predict the semantic attributes of the shoe images. Given a shoe image in an unknown viewpoint, our proposed system first classify it into one of the 6 pre-defined representative viewpoints, which are commonly displayed in online merchants. To localize the shoe parts on the identified viewpoint, a view-specific part localization model is proposed based on the prior knowledge of the shoe structures under different viewpoints. Finally, we extract several complementary low-level features from the localized shoe parts, which is fed into a SVM classifier for attribute prediction. The effectiveness of the proposed system is demonstrated on a newly-built pump shoe image dataset.
Image splicing localization based on blur type inconsistency

In a spliced blurred image, the spliced region and the original image may have different blur types. Splicing localization in this image is challenging when a forger uses image resizing as anti-forensics to remove the splicing traces anomalies. In this paper, we overcome this problem by proposing a method for splicing localization based on partial blur type inconsistency. In this method, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types.
Performance of Space–Time Codes: Gallager Bounds and Weight Enumeration

Since the standard union bound for space-time codes may diverge in quasi-static fading channels, the limit-before-average (LBA) technique has been exploited to derive tight performance bounds. However, it suffers from the computational burden arising from a multidimensional integral. In this paper, efficient bounding techniques for space-time codes are developed in the framework of Gallager bounds. Two closed-form upper bounds, the ellipsoidal bound and the spherical bound, are proposed that come close to simulation results within a few tenths of a decibel. In addition, two novel methods of weight enumeration operating on a further reduced state diagram are presented, which, in conjunction with the bounding techniques, give a thorough treatment of performance bounds for space-time codes.