nils-herrmann/clean_scirepeval huggingface dataset:
Multi-instance local exemplar comparisons for pedestrian detection

We propose to use the partial similarity between a sample and a number of exemplars as the image features for visual object detection. Define a part of the object as a sub-window inside the object bounding box, for each part of the object, a codebook of local appearance templates is learned. By using multiple templates for each part, and allowing the template to be compared with a bag of part instances in the neighborhood of the canonical location, the deformable and multi-aspect properties can be captured. A linear classifier is learned with feature selection, selecting a subset of the templates. To improve the efficiency of the detector, a rejection cascade is built by calibrating the linear classifier; the rejection cascade makes decisions using partial scores. Experimental results show that our method substantially improves the performance for human detection.
Simplified gabor wavelets for efficient feature extraction

Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).
Saliency detection using quatemionic distance based weber descriptor and object cues

In this paper, a simple and efficient method, based on Quaternionic Distance Based Weber Descriptor (QDWD) and object cues, is proposed for saliency detection. Firstly, QDWD, which was initially designed for detecting outliers in color images, is used to represent the directional cues in an image. Meanwhile, two low-level priors, namely the color contrast and center cue of the image, are utilized and fused as an object-level cue. Finally, by combining QDWD with object cues, a reliable saliency map of the image can be computed. Experimental results, based on a widely used and openly available database, show that the proposed method is able to produce promising results, compared to other state-of-the-art saliency-detection models.
Depth Estimation of Face Images Based on the Constrained ICA Model

In this paper, we propose a novel and efficient algorithm to reconstruct the 3-D structure of a human face from one or a number of its 2-D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a nonfrontal-view face image is at first formulated as a constrained independent component analysis (cICA) model. Then, the overcomplete ICA problem is converted into a normal ICA problem by incorporating a prior from the CANDIDE 3-D face model. Furthermore, the CANDIDE model is employed to construct a reference signal that is used in both the initialization and the objective function of the cICA model. Moreover, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. An important advantage of the proposed algorithm is that no frontal-view face image is required for the estimation of the corresponding 3-D face structure. Experimental results on a real 3-D face image database demonstrate the feasibility and efficiency of the proposed method.
Facial shape and albedo reconstruction based on a trained prototype

Facial shape and albedo can be estimated under unknown lighting conditions according to a facial prototype, which is generated by using Photometric Stereo. However, the prototype is seriously affected by shadows, ambient light, and noises. A solution to this problem is to train the prototype based on a facial database. In our algorithm, the facial shape and the albedo of a novel human face under unknown lighting conditions are reconstructed based on the trained prototype and singular value decomposition. Extensive experiments have proven that using our proposed trained prototype is more robust than other methods when a face is under shadow.
Guided iterative back-projection scheme for single-image super-resolution

The iterative back-projection (IBP) technique is an effective approach to single-image super-resolution (SR) reconstruction. However, the conventional IBP-based SR method suffers from a jaggy and ringing effect around the edges, as the up-sampling process and the back-projection of the reconstructed image are isotropic. In this paper, we present an improved IBP approach, namely guided iterative back-projection (GIBP), which employs an effective edge-guided interpolation method for up-sampling, and either a joint bilateral filter or a guided filter is used to constrain the projection of the error coefficients so as to suppress the noise and the ringing artifacts (i.e. the halo effect). To up-scale an image with an arbitrary ratio, we also propose an edge-persevering bilinear up-scaling interpolation method in cooperation with GIBP. Experimental results validate that the proposed algorithm can significantly improve SR reconstruction accuracy, while suppressing the main artifacts, such as the ringing and jaggy effects, and the noise created in the traditional IBP methods.
Rate control for MPEG-4 FGS coded video using piecewise rate distortion model

Three efficient rate allocation schemes for the MPEG-4 fine granular scalability (FGS) enhancement layer with constant quality are proposed. With the distortion measured using the mean squared error (MSE) or peak signal-to-noise ratio (PSNR) metric, the rate distortion (R-D) curve of the MPEG-4 FGS enhancement layer exhibits piecewise characteristics. For each piecewise region, a first-order or a second-order model is adopted to estimate the actual R-D curve. Based on these piecewise models, three optimal rate allocation schemes are proposed to allocate an appropriate number of bits to the enhancement layer of each frame. Experimental results show that, compared to the average rate allocation method and the exponential model method, our proposed algorithms can achieve a more constant quality in the reconstructed video.
Multi-view face hallucination based on sparse representation

In this paper, we propose a novel method to generate the hallucinated multi-views of faces using the sparse-representation model. In order to render a faithful virtual view, we introduce centralized constraints into a variation framework for optimization. The constraints are formulated based on an attempt to minimize the difference between the sparse-coding coefficients derived for two distinct views. In our algorithm, sift optical-flow method is employed to formulate the constraints. An input face is firstly sparsely coded over a given dictionary, and then the sparse-coding coefficients for the input face are refined through an optimization framework with the centralized constraints. Intensive experimental results demonstrate that our proposed method can perform well in terms of both reconstruction accuracy and visual quality.
Dynamic textures indexing and retrieval based on intrinsic properties

The indexing and retrieval of dynamic textures remains one of the most challenging tasks in both computer vision and computer graphics. This paper first introduces the concept of intrinsic properties of dynamic textures, including Static Texture Features (STF) and Dynamic Texture Features (DTF), and then proposes an efficient method that utilizes these intrinsic properties for the indexing and retrieval of dynamic textures. Our proposed method is evaluated and compared to existing methods based on a variety of video texture samples. Experimental results show that our scheme can produce promising results.
Totally-corrective boosting using continuous-valued weak learners

The Boosting algorithm has two main variants: the gradient Boosting and the totally-corrective column-generation Boosting. Recently, the latter has received increasing attention since it exhibits a better convergence property, thus resulting in more efficient strong learners. In this work, we point out that the totally-corrective column-generation Boosting is equivalent to the gradient-descent method for the gradient Boosting in the weak-learner selection criterion, but uses additional totally-corrective updates for the weak-learner weights. Therefore, other techniques for the gradient Boosting that produce continuous-valued weak learners, e.g. step-wise direct minimization and Newtons method, may also be used in combination with the totally-corrective procedure. In this work we take the well known AdaBoost algorithm as an example, and show that employing the continuous-valued weak learners improves the performance when used with the totally-corrective weak-learner weight update.
Region-based eigentransformation for face image hallucination

In this paper, we propose a region-based eigentransformation method for human face hallucination. The algorithm first uses the snake method to locate the targeted regions with high-frequency features in an edge-smoothed face mask, and then employs the eigentransformation to reconstruct the corresponding high-resolution counterpart. The resulting high-resolution face can be obtained by combining all of the reconstructed regions. As the boundaries of the respective high-frequency regions are also smooth, so the merging process at the boundaries will not produce visual artifacts. In addition, by segmenting the facial features into different regions, the eigentransformation can achieve a better performance level in reconstructing the high-frequency information. Experiments show that our proposed algorithm outperforms the original eigentransformation for face hallucination.
Face segmentation and facial feature tracking for videophone applications

In this paper, an efficient foreground/background video coding algorithm is proposed. The algorithm consists of three major parts: an automatic human face detection and facial feature extraction, facial feature tracking, and video coding method based on the properties of the facial feature. In the first part, the location of the human face and its respective facial features are extracted by means of a genetic algorithm and eigenface techniques. After the face region and the facial features have been detected in the first frame, a fast and robust method is proposed for tracking the detected facial features on the subsequent frames. Finally, the location of the facial features are passed to the encoding system for compression. As a result, both the subjective and the objective image quality of the face region are improved.
An output-based knowledge transfer approach and its application in bladder cancer prediction

Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively yet. In order to overcome these problems and construct an efficient classifier, for these real situations in medical fields, in this work we present an approach based on the least squares support vector machine (LS-SVM), which utilizes a transfer learning framework to make maximum use of the data and guarantee its enhanced generalization capability. The proposed approach is capable of effectively learning a target domain with limited samples by relying on the probabilistic outputs from the other previously learned model using a heterogeneous method in the source domain. Moreover, it autonomously and quickly decides how much output knowledge to transfer from source domain to the target one using a fast leave-one-out cross validation strategy. This approach is applied on a real-world clinical dataset to predict 5-year mortality of bladder cancer patients after radical cystectomy, and the experimental results indicate that the proposed method can achieve better performances compared to traditional machine learning methods, consistently showing the potential of the proposed method under the circumstances with insufficient data.
Salient object detection using octonion with Bayesian inference

A novel computational model for detecting salient regions in color images is proposed, based on a two-stage coarse-to-fine framework. Firstly, different early visual feature maps - including the edge intensity; the black-white, red-green, and blue-yellow color opponents; and the Gabor features with four directions - are incorporated into the eight channels of an octonion image. Spectral normalization is achieved with the octonion Fourier transform by preserving the phase information of the octonion image. Then, with mean-shift segmentation, the saliency values in each segment are averaged to form a coarse saliency map. Finally, the coarse saliency map is subject to Bayesian inference to further refine the salient regions. The integration of frequency normalization, spatial segmentation and Bayesian inference exploits the benefits from both the spectral domain and the spatial domain. Experimental results show the superiority of the proposed method compared to several existing methods.
An efficient method for face recognition under varying illumination

Principal component analysis (PCA) is a classical method which is often used for human face representation or recognition. However, for those images under uneven lighting conditions, the performance of PCA degrades greatly. In this paper, an efficient method for human face recognition under varying illumination is proposed. In our method, a local normalization technique is applied on the image point by point, which can efficiently and effectively eliminate the effect of uneven illuminations, white keeping the local statistical properties of the processed image the same as the corresponding image under normal lighting condition. Then, the processed images are used for face recognition. Experimental results show that, with the use of PCA for face recognition, the recognition rates can be improved by 46.4%, 40.0%, 8.3% and 37.9% based on the YaleB database, Yale database, AR database and the combined database, respectively, when our proposed algorithm is used.
Depth Estimation of Face Images Using the Nonlinear Least-Squares Model

In this paper, we propose an efficient algorithm to reconstruct the 3D structure of a human face from one or more of its 2D images with different poses. In our algorithm, the nonlinear least-squares model is first employed to estimate the depth values of facial feature points and the pose of the 2D face image concerned by means of the similarity transform. Furthermore, different optimization schemes are presented with regard to the accuracy levels and the training time required. Our algorithm also embeds the symmetrical property of the human face into the optimization procedure, in order to alleviate the sensitivities arising from changes in pose. In addition, the regularization term, based on linear correlation, is added in the objective function to improve the estimation accuracy of the 3D structure. Further, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. Experimental results on the 2D and 3D databases demonstrate the feasibility and efficiency of the proposed methods.
A fast approach for detecting human faces in a complex background

Human face detection is an important capability with a wide range of applications, such as human face recognition, surveillance systems, human-computer interfacing, video-conferencing, etc. In most such applications, the existence of human faces and their corresponding locations must be found. In other words, a reliable and fast method for detecting and locating face regions is of practical importance. In this paper, possible face region candidates in an image with a complex background are identified by means of the valley features on the eyes. These valley features are considered to belong to the eyes of a human face if they satisfy the local properties of the eyes. A pair of these features is matched if their Gabor features are similar; a face region is then formed. Each of the face region candidates is then further verified by matching it to a human face template, and by measuring its symmetry. Experiments show that this approach is fast and reliable.
An image restoration method based on PDEs and a new gradient model

The goal of image restoration is to eliminate noise and insignificant details from a blurred or noise-affected image, without blurring important semantic structures such as edges. In this paper, we propose a new approach based on partial differential equations (PDEs) by adjusting the threshold when iteration proceeds, and a new gradient model based on the idea of bilateral filtering, which can retain more visual details than other methods. With this new approach, the noise can be removed efficiently while preserving important semantic structures such as edges.
Edge detection using simplified Gabor wavelets

Gabor wavelets (GWs) have been commonly used for extracting local features for various applications, such as recognition, tracking, and edge detection. However, extracting the Gabor features is computationally intensive, so the features may be impractical for real-time applications. In this paper, we propose a set of simplified version of Gabor wavelets (SGWs) for edge detection. Experimental results show that our SGW-based edge detection algorithm can achieve a similar performance level to that using GWs, while the runtime required for feature extraction using SGWs is faster than that with GWs with the use of the fast Fourier transform (FFT). When compared to the Canny and other conventional edge detection methods, our proposed method can achieve a better performance in the terms of detection accuracy and computational complexity.
Image magnification based on adaptive MRF model parameter estimation

The Markov random field (MRF) model, whose model parameters specify the amount of smoothness in an image, is a popular approach to image magnification. The model parameters must be estimated accurately in order to obtain an elegant solution. The conventional parameter estimation methods consider an image to be homogeneous and have a high computational complexity. However, images are usually not homogenous; using only one set of parameters cannot describe a whole image effectively. We therefore devise an adaptive parameter estimation method for the MRF model to reduce the blocky artifact while preserving the edges in the (high-resolution) HR image. In our method, an initial estimated HR image is divided into small blocks, and the respective parameters are then estimated. Their values are defined as inversely proportional to their energy in the corresponding direction. Then, the gradient descent algorithm is employed iteratively to obtain an improved HR image in a Bayesian MAP framework. Experimental results show that, when compared to the MRF model with a fixed set of parameters, using the MRF model with our adaptive parameter estimation method can produce a magnified image with the edges and texture well preserved. Both the PSNR and visual quality of our proposed method are much better than the fixed-parameter method.
Microarray Data Classification Using the Spectral-Feature-Based TLS Ensemble Algorithm

The reliable and accurate identification of cancer categories is crucial to a successful diagnosis and a proper treatment of the disease. In most existing work, samples of gene expression data are treated as one-dimensional signals, and are analyzed by means of some statistical signal processing techniques or intelligent computation algorithms. In this paper, from an image-processing viewpoint, a spectral-feature-based Tikhonov-regularized least-squares (TLS) ensemble algorithm is proposed for cancer classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of the atoms of a dictionary. Two types of dictionaries, namely singular value decomposition (SVD)-based eigenassays and independent component analysis (ICA)-based eigenassays, are proposed for the TLS model, and both are extracted via a two-stage approach. The proposed algorithm is inspired by our finding that, among these eigenassays, the categories of some of the testing samples can be assigned correctly by using the TLS models formed from some of the spectral features, but not for those formed from the original samples only. In order to retain the positive characteristics of these spectral features in making correct category assignments, a strategy of classifier committee learning (CCL) is designed to combine the results obtained from the different spectral features. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.
Saliency detection based on adaptive DoG and distance transform

A novel computational model for detecting salient regions in color images is proposed, based on adaptive difference of Gaussian (DoG) filtering and distance transform. In our method, we first transform an image into the frequency domain, and perform adaptive DoG filtering, whose parameters are determined by the energy spectrum of the image. Then, the edge information is extracted from the DoG filtering output, and the distance transform is applied to the edge map. Finally, the Gaussian pyramids are used to enhance the distance transform performance. Our proposed method achieves spectral domain filtering as well as spatial domain edge extraction, thus exploiting the benefits from both the spatial domain and the spectral domain for saliency detection. We compare our proposed method with five existing saliency detection methods in terms of precision, recall, and F-measure. Experiments on the MSRA dataset show the outperformance of the proposed method over those saliency algorithms.
A new cubic rate distortion model for low-delay video communication

A new rate model based on a cubic function, and its related control schemes, is proposed to estimate the quantization scales of frames for a specified target bit rate. Our scheme is based on a distortion-rate model, which can easily be extended to a higher order for different coding algorithms without requiring much effort to determine the quantization scales. The formulation of the cubic model is derived from the quadratic rate-distortion model and from simple curve fitting. Our model is implemented in H.264 JM8.1a, and the performance is compared to JM8.1a with different test sequences and bit rates. Experimental results show that our proposed model outperforms the quadratic model in terms of both PSNR and bit rate.
Sampling Gabor features for face recognition

The Gabor feature is effective for facial image representation. However, the dimension of a Gabor feature vector is very high so that the computation and memory requirements are prohibitively large. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of principal component analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.