nils-herrmann/clean_scirepeval huggingface dataset:
A robust algorithm for detection of human faces in color images

An efficient algorithm for detecting human faces in color images is proposed. The first step of our algorithm is to segment the possible skin-like regions in an image by using color information. One of the major problems of using skin color is that a face region may not be detected under poor or intense lighting conditions, or if the lighting conditions vary over the face region. Our approach considers the distribution of the color components of skin pixels under different illumination. This information can be used to identify skin color pixels reliably under different lighting conditions. The skin color regions are then clustered and verified as human face regions or not. In order to improve the reliability of detection, an eigenmask that has a large magnitude at the important facial features of a human face is devised. Experimental results show that this algorithm can detect human faces under different lighting conditions reliably.
A joint deep-network-based image restoration algorithm for multi-degradations

In the procedures of image acquisition, compression, and transmission, captured images usually suffer from various degradations, such as low-resolution and compression distortion. Although there have been a lot of research done on image restoration, they usually aim to deal with a single degraded factor, ignoring the correlation of different degradations. To establish a restoration framework for multiple degradations, a joint deep-network-based image restoration algorithm is proposed in this paper. The proposed convolutional neural network is composed of two stages. Firstly, a de-blocking subnet is constructed, using two cascaded neural network. Then, super-resolution is carried out by a 20-layer very deep network with skipping links. Cascading these two stages forms a novel deep network. Experimental results on the Set5, Setl4 and BSD100 benchmarks demonstrate that the proposed method can achieve better results, in terms of both the subjective and objective performances.
An Adaptive-Profile Active Shape Model for Facial-Feature Detection

In this paper, a novel algorithm based on the Active Shape Model (ASM) for locating landmarks on human faces is proposed. A challenge for detecting facial features is that faces may be under different poses, this makes the local appearance of each facial landmark vary greatly. To account for these variations, we propose an adaptive-profile scheme for ASM so that facial landmarks can be detected reliably and accurately under different poses. In our algorithm, a 2D profile is used for each landmark, and the 2D profiles of each landmark of the training face images are grouped to form a number of clusters. The corresponding shape vector for each of the clusters is then learned. For a query face image, the profiles to be used to locate the respective facial landmarks will be selected according to the face-shape vector in the current iteration. In other words, adaptive profiles are used in the search for landmarks. Face images from two subsets of the IMM Face Database are used for training, and the other two subsets are used for testing. The performance of our proposed algorithm is also evaluated using another dataset, namely the Bosphorus Dataset. Experiment results show that our proposed Adaptive-Profile Active Shape Model (APASM) can locate facial landmarks accurately under different face shapes, expressions, and poses.
An efficient human face indexing scheme using eigenfaces

We propose an efficient indexing structure for searching a human face in a large database. In our method, a set of eigenfaces is computed based on the faces in the database. Each face in the database is then ranked according to its projection onto each of the eigenfaces. A query input will be ranked similarly, and the corresponding nearest faces in the ranked position with respect to each of the eigenfaces are selected from the database. These selected faces will then form a small database, namely a condensed database, for face recognition, instead of considering the original large database. In the experiments, the effect of the number of eigenfaces used on the size of the condensed database is investigated. Experimental results show that the size of the condensed database is 35% of the original large database when 25% of the eigenfaces with the largest eigenvalues are selected. The processing time required to generate the condensed database is less than one second.
An Efficient Scene-Break Detection Method Based on Linear Prediction With Bayesian Cost Functions

This paper describes an efficient approach to scene-break detection, which can detect cuts, dissolves, and wipes reliably and effectively by means of temporally linear prediction models. In our algorithm, two linear prediction models are adopted to predict a current frame: one for dissolves, and the other for stationary scenes. The predicted frames, derived based on the two models, are compared with the original frames, and cuts and dissolves are then determined based on Bayesian cost functions. For the detection, our algorithm requires the setting of a single threshold only. In wipe detection, our linear prediction models are employed to detect areas of change between two successive frames. By accumulating the changed areas and the overlap of the changed areas over the successive frames, wipes of an arbitrary shape and direction are detected. Experimental results show that our algorithm can achieve a high level of precision even if a video contains object motion and camera motion. The detection time required to analyze a 38-min video is no more than several seconds.
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View

We propose an analytic-to-holistic approach which can identify faces at different perspective variations. The database for the test consists of 40 frontal-view faces. The first step is to locate 15 feature points on a face. A head model is proposed, and the rotation of the face can be estimated using geometrical measurements. The positions of the feature points are adjusted so that their corresponding positions for the frontal view are approximated. These feature points are then compared with the feature points of the faces in a database using a similarity transform. In the second step, we set up windows for the eyes, nose, and mouth. These feature windows are compared with those in the database by correlation. Results show that this approach can achieve a similar level of performance from different viewing directions of a face. Under different perspective variations, the overall recognition rates are over 84 percent and 96 percent for the first and the first three likely matched faces, respectively.
Salient-region detection in a multi-level framework of image smoothing with over-segmentation

Saliency detection is one of the extraordinary abilities of the human visual system; it also provides a powerful tool for predicting where people tend to focus in the free-viewing process. In this paper, we propose a novel salient-object detection method which applies an over-segmentation-based saliency detection algorithm to multi-level smoothed images. The original image is initially subjected to smoothing based on multi-level L0 gradient minimization; this can characterize its fundamental constituents while diminishing the insignificant details. Then, segment-based saliency computation is applied to the multi-level smoothed images to produce a series of intermediate saliency maps. The final saliency map is generated by combining the intermediate saliency maps. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of Precision, Recall and F-measure, as well as in terms of the area under the ROC curve (AUC).
Recognition of low-resolution face images using sparse coding of local features

In this paper, we propose a new approach for recognition of low-resolution face images by using sparse coding of local features. The proposed algorithm extracts Gabor features from a low-resolution gallery image and a query image at different scales and orientations, then projects the features separately into a new low-dimensional feature space using sparse coding that preserves the sparse structure of the local features. To determine the similarity between the projected features, a coefficient vector is estimated by using linear regression that determines the relationship between the projected gallery and query features. On the basis of this coefficient vector, residual values will be computed to classify the images. To validate our proposed method, experiments were performed using three databases (ORL, Extended-Yale B, and CAS-PEAL-R1), which contain images with different facial expressions and lighting conditions. Experimental results show that our method outperforms various classical and state-of-the-art face recognition methods.
Phase-based salient object detection

In this paper, we propose an efficient approach to computing saliency maps and determining salient areas in images. Biologically inspired feature maps are first constructed for a given image, and the phase information is then extracted from each feature map in the frequency domain. Finally, a saliency map is formed by using the phase information, followed by a smoothing operation. Experiment results show that our proposed algorithm is computationally efficient and can detect salient objects accurately.
A new storage scheme for conflict-free vector access

Investigations into data storage schemes for parallel memory system of vector processing have been mainly focused on low-order interleaved schemes, skewed schemes and XOR schemes. In this paper, a new interleaved storage scheme, namely k-row interleaved scheme, is suggested and investigated. This scheme allocates k consecutive data of a vector onto one memory module sequentially and then the next k consecutive data onto the next memory module. The address mapping functions are devised and the performance of this scheme is evaluated. It is found that this scheme improves the average performance for vector access over the previous schemes. The address generation hardware is also shown to be simple.<<ETX>>
From quaternion to octonion: Feature-based image saliency detection

A novel computational model for detecting salient regions in color images is proposed by utilizing early visual features and performing spectral normalization in the octonion algebra framework, which can accommodate more feature channels than quaternions can. Firstly, feature maps based on edge intensity, the black-white, red-green, and blue-yellow color opponents, as well as the Gabor features with four directions, are incorporated into the eight channels of the octonion image. Then, spectral normalization is achieved by preserving the phase information of the octonion image. Finally, saliency maps are generated at different scales using Gaussian pyramids, and are combined to form the final saliency map. The integration of frequency normalization into the octonion image and saliency-map pyramids exploits the benefits from both the spectral domain and the spatial domain. Experimental results on the MSRA dataset demonstrate that our proposed method outperforms five existing saliency detection models.
A face hallucination algorithm via KPLS-eigentransformation model

In this paper, we present a novel eigentransformation based algorithm for face hallucination. The traditional eigentransformation method is a linear subspace approach, which represents an image as a linear combination of training samples. Consequently, it cannot effectively represent the relationship between the low resolution facial images and the corresponding high-resolution version. In our algorithm, a Kernel Partial Least Squares (KPLS) predictor is introduced into the eigentransformation model for solving the High Resolution (HR) image form a Low Resolution (LR) facial image. We have compared our proposed method with some current Super Resolution (SR) algorithms using different zooming factors. Experimental results show that our algorithm provides improved performances over the compared methods in terms of both visual quality and numerical errors.
Human face recognition using a spatially weighted modified Hausdorff distance

Hausdorff distance is an efficient measure of the similarity of two point sets. We propose a modified Hausdorff distance measure for human face recognition. This modified Hausdorff distance measure incorporates information about the location of important facial features when comparing the edge maps of two facial images. The distance measure is weighted according to a weighted function derived from the spatial information of the human face. This distance measure, namely spatially weighted Hausdorff distance (SWHD), is further improved by combining it with the 'doubly' modified Hausdorff distance (M2HD). This new Hausdorff distance measure is called spatially weighted 'doubly' Hausdorff distance (SW2HD), which can alleviate the effect of facial expressions in human face recognition. Experiment results show that both the SWHD and SW2HD outperform the M2HD in recognition rate. The SW2HD can achieve the best recognition rate among the different Hausdorff distance measures. In our experiment, its recognition rates are 89%, 94%, and 98% for the first one, the first five, and the first ten likely matched faces, respectively. The average processing time for recognition of a human face is less than one second.
An Effective Missing-Data Estimation Approach for Small-Size Image Sequences

Missing data is a frequently encountered problem for structure-from-motion (SFM) where the 3D structure of an object is estimated based on 2D images. In this paper, an effective approach is proposed to deal with the missing-data estimation problem for small-size image sequences. In the proposed method, a set of sub-sequences is first extracted. Each sub-sequence is composed of the frame to be estimated and a part of the original sequence. In order to obtain diversified estimations, multiple weaker estimators are constructed by means of the column-space-fitting (CSF) algorithm. The various sub-sequences are in turn used as the inputs to the algorithm. As the non-missing entries are known, the estimation errors of these entries are computed so as to select weaker estimators with better estimation performances. Furthermore, a linear programming based weighting model is established to compute the weights for the selected weaker estimators. After the weighting coefficients are obtained, a linear weighting estimation which is used as the final estimation of the missing entries is computed based on the outputs of the weaker estimators. By applying the strategies of weaker-estimator selection and the linear programming weighting model, the proposed missing entry estimation method is more accurate and robust than the existing algorithms. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
An efficient approach for facial feature detection

Facial feature extraction is an important step in automated visual interpretation and human face recognition. Among the facial features, the eyes play the most important part in the recognition process. An efficient approach for detecting and locating the eyes in a head-and-shoulders image is devised. Possible eye candidates in an image are identified by means of the valley features and corners of the eyes. Two possible eye candidates are considered to belong to the eyes of a human face if their respective local properties are similar; an eye window is then formed. Each of the eye region candidates is then further verified by comparing them with a standard eye template, and by measuring its symmetry. Experiments show that this approach is fast and reliable.
Mean-shift based mixture model for face detection in color image

Human face detection is a challenging task under different lighting conditions. We propose an efficient and reliable algorithm to detect human faces in an image. Our algorithm uses a region-based approach to identify skin-colored pixels under various lighting conditions. Within the detected skin-color regions, a ratio method is proposed to determine possible eye candidates. Two eye candidates form a possible face region, which is then verified by means of a two-stage procedure with an eigenmask. Experimental results based on the HHI MPEG-7 face database show that this face detection algorithm is efficient and reliable under different lighting conditions.
An efficient scene break detection based on linear prediction

Detection of edits in a video sequence is the first step for video analysis, which segments a video into its basic components. In this paper, we propose a novel and efficient approach to scene break detection, which can detect cuts, dissolves, and fades reliably. Our approach is based on the temporal linear prediction of the frames. For the frames in a video shot, a frame can be predicted from its previous frames. If the prediction error is high, a cut should happen. For dissolves and fades, the prediction coefficients are constants, and these kinds of gradual transitions can be detected by comparing the prediction errors of two different linear predictions. Experimental results show that our algorithm can achieve high precision even if a video contains object motion and camera motion, and is able to detect and classify cuts, dissolves and fades in real time.
A faster converging snake algorithm to locate object boundaries

A different contour search algorithm is presented in this paper that provides a faster convergence to the object contours than both the greedy snake algorithm (GSA) and the fast greedy snake (FGSA) algorithm. This new algorithm performs the search in an alternate skipping way between the even and odd nodes (snaxels) of a snake with different step sizes such that the snake moves to a likely local minimum in a twisting way. The alternative step sizes are adjusted so that the snake is less likely to be trapped at a pseudo-local minimum. The iteration process is based on a coarse-to-fine approach to improve the convergence. The proposed algorithm is compared with the FGSA algorithm that employs two alternating search patterns without altering the search step size. The algorithm is also applied in conjunction with the subband decomposition to extract face profiles in a hierarchical way.
An efficient low bit-rate video-coding algorithm focusing on moving regions

Block-based motion estimation and compensation are the most popular techniques for video coding. However, as the shape and the structure of an object in a picture are arbitrary, the performance of such conventional block-based methods may not be satisfactory. In this paper, a very low bit-rate video coding algorithm that focuses on moving regions is proposed. The aim is to improve the coding performance, which gives better subjective and objective quality than that of the conventional coding methods at the same bit rate. Eight patterns are pre-defined to approximate the moving regions in a macroblock. The patterns are then used for motion estimation and compensation to reduce the prediction errors. Furthermore, in order to increase the compression performance, the residual errors of a macroblock are rearranged into a block with no significant increase of high-order DCT coefficients. As a result, both the prediction efficiency and the compression efficiency are improved.
Undergraduate Students Compete in the IEEE Signal Processing Cup: Part 1 [sp Education]

Signal processing is the key to success for the efficient storage, transmission, and manipulation of information. The liveliness of signal processing relies on having a large number of students who undertake this research and embark on this career path. Signal processing is part of the curriculum in many undergraduate engineering programs. Some of the students also do their capstone or final-year projects on signal processing. To increase students? interest in signal processing and to get them to better appreciate its applications in real life, the IEEE Signal Processing Society (SPS) has created an undergraduate competition, referred to as the Signal Processing Cup (SP Cup) , which provides undergraduate students with an opportunity to form teams and work together to solve a challenging and interesting real-world problem using signal processing techniques and methods. The first competition was held at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014. Based on its success, it has now been instated as an annual event at ICASSP conferences.
Differentiating pancreatic mucinous cystic neoplasms form serous oligocystic adenomas in spectral CT images using machine learning algorithms: A preliminary study

Pancreatic cancer is one of the most fatal cancers. Distinguishing mucinous cystic neoplasm from serous oligocystic adenoma by using cross-sectional imaging system is very important for patients' prognosis. Gemstone spectral computed tomography (CT) can provide more information as compared with the conventional CT. Machine-learning algorithms have been employed in a great variety of applications. This preliminary study aims to verify the effectiveness of the additional information provided by spectral CT with the use of the state-of-the-art classification algorithm combined with feature-selection methods. Results show that SVM+MI achieves the highest classification accuracy (71.43%). The second highest classification accuracy is obtained by using SVM+LO (63.83%). Features selected by these algorithms are consistent with clinical observations. Top-ranking features include lower viewing energy (around 50 keV) CT values, Iodine-Water concentrations, and Effective-Z.
Gabor-Feature Hallucination based on Generalized Canonical Correlation Analysis for face recognition

In face recognition, image resolution is an important factor which has a great influence on the recognition rate. In traditional face recognition for low-resolution images, face interpolation/super-resolution is usually performed first, and the constructed high-resolution face image will then pass through a face recognition system, which includes feature extraction and classification. To achieve a more efficient and accurate approach, we propose a new method of “Gabor-Feature Hallucination”, which predicts the high-resolution Gabor features from the low-resolution Gabor features directly by using linear regression and Generalized Canonical Correlation Analysis. Then, the low-resolution features in the projected Generalized Canonical Correlation space and the predicted high-resolution Gabor features are adopted for face classification. Our algorithm can therefore avoid performing interpolation/super-resolution and high-resolution Gabor feature extraction. Experimental results show that the proposed method has a superior recognition rate and efficiency to the traditional methods.
An efficient method for blocky and scratch noise removal

In this paper, we propose a new method which can remove blocky noises and scratch noises efficiently. Our method employs the idea of robust principal component analysis (RPCA) and uses similar patches from natural images as much as possible. In our method, a matrix is formed whose columns are the patches, which are then decomposed into a noise matrix and a noiseless matrix. Experimental results show that our method can achieve excellent results when compared to state-of-the-art methods.