nils-herrmann/clean_scirepeval huggingface dataset:
Finding distinctive facial areas for face recognition

One of the key issues for local appearance based face recognition methods is that how to find the most discriminative facial areas. Most of the existing methods take the assumption that anatomical facial components, such as the eyes, nose, and mouth, are the most useful areas for recognition. Other more elaborate methods locate the most salient parts within the face according to a pre-specified criterion. In this paper, a novel method is proposed to identify the discriminative facial areas for face recognition. Unlike the existing methods that only analyze the given face, the proposed method identifies the distinctive areas of each individual's face by its comparison to the general population. In particular, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation of the facial patterns from a generic face image database. In the learned subspace, the degree of distinctiveness for any facial area is measured depends on the probability of this area is belong to a general face. For evaluation, the proposed method is tested on exaggerated face images and applied in exiting face recognition systems. Experimental results demonstrate the efficiency of the proposed method.
Stereoscopic panoramic video generation using centro-circular projection technique

The paper presents a method of stereoscopic panoramic video generation including techniques for panorama projection, stitching and calibration for various depth planes. The methods described can be used on video sequences captured by an arrangement of multiple pairs of cameras or multiple stereoscopic cameras mounted on a regular polygonal shaped camera rig. Algorithms can also be used in combination or separately, for generating both stereoscopic and monoscopic video and still panoramas.
Investigation of different skeleton features for CNN-based 3D action recognition

Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.
Industrial computer vision using undefined feature extraction

This paper presents an application of computer vision in a real-world uncontrolled environment found at BHP Steel Port Kembla. The task is visual identification of torpedo ladles at a blast furnace which is achieved by reading numbers attached to each ladle. Number recognition is achieved through use of feature extraction using a multi-layer perceptron (MLP) artificial neural network (ANN). The novelty in the method used in this application is that the features the MLP is being trained to extract are undefined before the MLP is initialised. The results of the MLP processing are passed to a decision tree for analysis and final classification of each object within the image. This technique is achieving a recognition rate on previously unseen images of greater than 80%.
Compression tolerant DCT based image hash

With the advent of Internet image authentication has become a central part of research in security. Since JPEG has recommended discrete cosine transform as one of the steps in image compression systems, a hash function which utilizes discrete cosine decomposition is desirable. In this paper, we propose a discrete cosine based hash function which distinguishes acceptable level of compression from image processing modifications such as Median filtering, Gaussian noise addition, and FMLR attack. To increase manipulation detection, we optimize the number of AC coefficients needed in smoothing.
Smoke detection in videos using Non-Redundant Local Binary Pattern-based features

This paper presents a novel and low complexity method for real-time video-based smoke detection. As a local texture operator, Non-Redundant Local Binary Pattern (NRLBP) is more discriminative and robust to illumination changes in comparison with original Local Binary Pattern (LBP), thus is employed to encode the appearance information of smoke. Non-Redundant Local Motion Binary Pattern (NRLMBP), which is computed on the difference image of consecutive frames, is introduced to capture the motion information of smoke. Experimental results show that NRLBP outperforms the original LBP in the smoke detection task. Furthermore, the combination of NRLBP and NRLMBP, which can be considered as a spatial-temporal descriptor of smoke, can lead to remarkable improvement on detection performance.
Discriminative Key Pose Extraction Using Extended LC-KSVD for Action Recognition

This paper presents a method for extracting discriminative key poses for skeleton-based action recognition. Poses are represented by normalized joint locations, velocities and accelerations of skeleton joints. An extended label consistent K-SVD (ELC-KSVD) algorithm is proposed for learning the common and action-specific dictionaries. Discriminative key poses are represented by the atoms of the action-specific dictionaries. With the specific dictionaries, sparse codes are obtained for representing action instances through max pooling and temporal pyramid. A SVM classifier is trained for action recognition. The proposed method was evaluated on the MSRC-12 gesture and MSR-Action 3D datasets. Experimental results have shown that the proposed method is effective in extracting discriminative key poses.
Edge image description using fractal interpolation

Edge images derived from compressed image databases are described using fractal techniques. The proposed method is able to give an affine transformation-invariant description suitable for use in a query-by-example database application. Comparison among the proposed method, polynomial interpolation and spline interpolation is given. It is concluded that fractal interpolation can give a compact description of image contours and is able to cope with random perturbation of the coordinates of the contour points by as much as 25 percent.
Human detection with contour-based local motion binary patterns

This paper presents a human detection method using contour-based local motion features. The local motion is encoded using a variant of the popular Local Binary Pattern (LBP) called Non-Redundant Local Binary Pattern (NRLBP) descriptor computed on the difference image of two consecutive frames. In addition, the local motion features are extracted along the human's boundary contour. Localising features on the contours has the advantage of utilizing a precise human shape description. A motivation of the proposed method is that most of informative movements are performed on boundary contours of the body parts, e.g. legs of pedestrians. Evaluation of the proposed method was conducted on the INRIA and ETH datasets. Apart from showing the importance of motion information, experimental results also showed that localising features along the object boundary contours improves the detection performance.
Visual perceptual process model and object segmentation

Modeling human visual process is crucial for automatic object segmentation that is able to produce consistent results to human perception. Based on the latest understanding of how human performs the task of extracting objects from images, we proposed a graph-based computational framework to model the visual process. The model supports the hierarchical nature of human visual perception and consists of the key steps of human visual perception including pre-attentive (pre-constancy) grouping, figure-and-ground organization, and attentive (post-constancy) grouping. A divide-and-conquer implementation of the model based on the concept of shortest spanning tree (SST) has demonstrated the potential of the model for object segmentation.
Regularized MRE method for blind multichannel image deconvolution

In this paper we deal with blind image restoration in a multi-channel framework. We use the mutually referenced equalizers (MRE) in order to perform exact image restoration in the noiseless case and under some identifiability conditions. In presence of noise, this method fails to restore correctly the original image. So, we introduce a regularization term to improve the restored image quality. We introduce two regularization techniques: a norm minimization scheme and an eigen value truncation scheme in order to perform the regularization and reduce the noise amplification assigned to image restoration. We compare the performance gain of our method for different number of observed images with the mono-channel case. We evaluate the performance of our method using an image similarity index.
A new automatic framework for document image enhancement process based on anisotropic diffusion

In the last two decades many nonlinear anisotropic diffusion-based approaches have been proposed to deal with document image enhancement. However, all these methods are based on an iterative process that highly depends on two crucial parameters K±, used to separate coefficients representing foreground edges from those representing artifacts into eigenvalues matrices λ±. These parameters are tuned manually. In this paper, a new approach which blindly and automatically highlights eigenvalues coefficients representing foreground strokes is proposed. This solution is then integrated into a full anisotropic diffusion-based filter proposed earlier. The performance of the proposed method is evaluated and compared with non-automatic methods of the state-of-the-art by means of objective measures and perceptual judgment on DIBCO databases and some document images collected from the web.
A distortion-based ranking of image quality indices using mutual information

Measurement of visual signal quality is of fundamental importance in a broad range of applications. The ultimate goal of quality assessment algorithms is to assess automatically the quality of images or videos in agreement with subjective human quality judgments. We discuss in this paper a new approach for ranking quality measures across different types of degradation that affect a given image. To rank the different image quality indices, we propose to use the concept of mutual information or information content. The experimental results show that the proposed ranking of quality indices is superior to the ranking based on second order correlation coefficients.
Natural Rendering of Color Image based on Retinex

A new method for Natural Rendering of Color Image based on Retinex (NRCIR) is proposed. Here, the word “natural” means that the ambience of image (warm or cold color impression) should not be changed after enhancement. Furthermore, the treatment should not introduce any additional light sources and should not produce halo effect or amplify blocking effect. Inspired by Retinex theory and histogram rescaling techniques, the proposed method tries to realize natural rendering of image with respect to the constraints listed above. Extensive tests with different types of natural images have been performed. The obtained results clearly demonstrate the efficiency of the proposed method.
A new adaptive image post-treatment for deblocking and deringing based on Total Variation method

A new HVS-inspired Total Variation (TV) method is proposed for jointly reducing blocking and ringing effects. The key idea is to adapt the TV problem to each relevant content of the image expressed through various maps. A perceptual segmentation method based on some characteristics of the Human Visual System (HVS) is used in order to extract theses maps. Then a perceptual adaptive TV-based method using the projected subgradient technique is used for deringing and deblocking JPEG-compressed image. The performance of the proposed method is evaluated objectively and subjectively in terms of blocking, ringing and blurring effects reduction, using the Kodak base images. The proposed method is also compared to the state of the art.
Local estimation of displacement density for abnormal behavior detection

Detecting abnormal behavior in video sequences has become a crucial task with the development of automatic video-surveillance systems. Here, we propose an algorithm which locally models the probability distribution of objects behavioral features. A temporal Gaussian mixture with local update is introduced to estimate the local probability distribution. The update of the feature probability distribution is thus temporal and local, allowing a smooth transition for neighboring locations. The integration of local information in the estimation provides a fast adaptation along with an efficient discrimination between normal and abnormal behavior. The proposed technique is evaluated on both synthetic and real data. Synthetic data simulates different scenarios occurring in road traffic, and illustrates how the model adapts to local conditions. Real data demonstrates the ability of the system to detect abnormal behavior due to the presence of pedestrians and animals on highways. In all tested scenarios the system identifies abnormal and normal behavior correctly.
A local directional bandlimited contrast

This article proposed a new definition for the local contrast inspired by some findings on the human visual system mechanisms. This measure exploits the multichannel nature of the human visual system. This new definition takes into account explicitly the frequency and directional selectivity of the cortex as demonstrated through many neurophysiological and psychovisual experiments. The visual information conveyed by this new contrast, called local directional bandlimited contrast, are analysed on real and synthetic images. The consistency of this local contrast with the subjective perception and its computational complexity are compared to other similar contrasts.
Combination of closest space and closest structure to ameliorate non-local means method

Recently non-local means (NLM) has been known to be one of the most attractive denoising algorithms. It alters each pixel by a weighted average of pixels in the image. The weights express the level of similarity between two small patches defined for two involved pixels. There are many propositions to ameliorate the performance of this method. One of branches is to seek the whole image the most similar patches for a given one. In this paper, we investigate this approach and show that it is suitable for only highly textured images. Moreover, we show that combination of this approach and the original NLM yields better result for all image types.
Blind Image Separation using Sparse Representation

This paper focuses on the blind image separation using their sparse representation in an appropriate transform domain. A new separation method is proposed that proceeds in two steps: (i) an image pre-treatment step to transform the original sources into sparse images and to reduce the mixture matrix to an orthogonal transform (ii) and a separation step that exploits the transformed image sparsity via an lscrp-norm based contrast function. A simple and efficient natural gradient technique is used for the optimization of the contrast function. The resulting algorithm is shown to outperform existing techniques in terms of separation quality and computational cost.
Block truncation coding using edginess information

An improvement of the well-known block truncation coding (BTC) method, introduced by Delp and Mitchell (1979), is proposed. The basic idea, derived from previous work of Beghdadi and Le Negrate (1989, 1991) on contrast enhancement and image filtering using a local contrast measure is to exploit edginess information in estimating both the quantization threshold and the reconstruction levels. The results obtained on actual images clearly show the efficiency of the method in preserving edge information compared to similar approaches.
Mammography analysis using a soft perceptual segmentation approach

A new mammography image segmentation method based on a perceptual and fuzzy approach is proposed. The main idea is to exploit some properties of the Human Visual System namely the directional and frequency selectivity and the fuzzy sets theory in order to segment the mammography images into meaningful components. A contrast enhancement is used in the case of suspicious cases identified during the segmentation process. The performance of the proposed method is objectively evaluated using the just-noticeable difference measure expressed through a saliency map of the mammography image before and after processing. The obtained results confirm the efficiency of the method in segmenting the images into meaningful regions.
Coding artifacts reduction method based on a perceptually-weighted variational approach

A new adaptive post-processing method for reducing blocking and ringing effects is proposed. This approach exploits some characteristics of the Human Visual System (HVS), namely the Just Noticeable Contrast (JNC) and the luminance adaptation. The method is based on a perceptually-weighted Total Variation regularization approach constrained by the knowledge of a prior information derived from some structural features maps extracted from the image. The performance of the proposed method is evaluated objectively and subjectively in terms of blocking and ringing visibility. The proposed method is also compared to the state of arts.
A new perceptually adaptive method for deblocking and deringing

In this paper, a new perceptually adaptive method for reducing the blocking and ringing artifacts encountered in image compression is proposed. The method consists of three steps: (i) blocking-ringing artifacts detection, (ii) perceptual distortion measure and (iii) blocking-ringing artifacts reduction. The performance of the proposed method is evaluated objectively and subjectively in terms of image fidelity and blocking, ringing and blur effects reduction. The obtained results are very promising and confirm once more the efficiency of perceptual approaches in image processing.
Improving performance of deblocking techniques using image fusion

Block based transform coding is the most popular technique for image and video compression. However, images compressed using block based algorithms exhibit blocking which is one of the most annoying artifacts in compressed images and video. Many deblocking algorithms have been proposed to improve the quality of the reconstructed image. Although the deblocking operation improves the image appearance by smoothing the block discontinuities, a loss of information also occurs during this process. As a consequence, there is a need to develop methods to reduce the blocking artifacts while minimizing the information loss. We propose a novel fusion based technique to minimize the loss of useful information which may result from the deblocking operation. The loss of key image information can be reduced by fusing a deblocking result and the compressed image.
Optimized residual image for stereo image coding

Many research works have been developed for stereo image compression purpose by focusing on the disparity compensation technique. For this reason, a great attention should be paid to the generation of the disparity-compensated residual image. Generally, the residual image is computed through a simple substruction of the disparity-compensated reference image from the target one. In this paper, we investigate two techniques for optimizing the computation of the residual image. The obtained results confirm the benefits of these optimization approaches in the context of stereo image coding.
Improve gamut mapping of Color Management System by perceptual quality-oriented analysis

The state-of-the-art solution for Color Management System (CMS) proposed by ICC V4 is mainly based on color distribution. No sophisticated image content analysis is included. However, not all color points are viewed equal in perception, and they should be clustered into different groups according to their visual importance so that different mapping strategies can be applied respectively to achieve a better color image reproduction. The proposed work introduces the perceptual image quality concept into gamut mapping of Color Management System by a multi-domain image gamut analysis. The ambiguity of rendering intent selection can be alleviated by content-aware image gamut analysis whose intention is to realize an Image Gamut Boundary Reduction (IGBR). It enables CMS a more reasonable selection of rendering intents and more coherent reproduction.
Investigating “the experience effect” in audio quality assessment

The study reported in this paper aims to investigate the integration of the experience effect in audio quality assessment methods. During our subjective tests, we have noticed that a low quality audio signal affect strongly and negatively the listeners' opinion on the next-coming test sequences, even if they are of higher quality. This leads eventually to erroneous data and consequently to a misevaluation of audio codecs and systems. This phenomenon called here, the “experience effect”, is presented and analyzed through an enriched experimental framework, with suggestions for statistical approaches to smooth out raw data and get more reliable results. This paper is a snapshot of our ongoing work on the development of new quality assessment methodologies, combining both the perceptual and behavioral aspects of human response.
Multi-resolution Mean-Shift Algorithm for Vector Quantization

This paper presents a new multi-resolution mean-shift algorithm for vector quantization of high-resolution images and generation of a stratified codebook. The algorithm employs the discrete wavelet transform (DWT) to circumvent the problem of bandwidth selection with the mean-shift algorithm. Here, the mean-shift algorithm is applied to a reduced set of samples in the color space. The detection of salient edges is performed on the DWT subbands for each level of decomposition to identify the pixels escaping the basin of attraction. The quantized image is reconstructed by upward interpolation of the salient pixels in the feature space. We also propose a Modified-Weighted mean-shift algorithm to speed up the image reconstruction stage. Experiments show that the proposed multi-resolution mean-shift provides significant speed-up compared to the Linde-Buzo-Gray (LBG) algorithm.
Noise filtering using Empirical Mode Decomposition

In this paper a noise filtering method using the empirical mode decomposition (EMD) is proposed. The noisy signal is decomposed into oscillatory components called intrinsic mode functions (IMFs) using a process referred to as sifting. The basic idea of the proposed scheme is the partial re-construction of the signal using the IMFs corresponding to the most important structures of the signal (low frequency modes). A new criterion is proposed to determine the IMF after which the energy distribution of the important structures of the signal overcomes that of the noise and that of the high frequency components of the signal. The method is tested on simulated and real signals.