nils-herrmann/clean_scirepeval huggingface dataset:
Facial Attribute Recognition by Recurrent Learning With Visual Fixation

This paper presents a recurrent learning-based facial attribute recognition method that mimics human observers’ visual fixation. The concentrated views of a human observer while focusing and exploring parts of a facial image over time are generated and fed into a recurrent network. The network makes a decision concerning facial attributes based on the features gleaned from the observer’s visual fixations. Experiments on facial expression, gender, and age datasets show that applying visual fixation to recurrent networks improves recognition rates significantly. The proposed method not only outperforms state-of-the-art recognition methods based on static facial features, but also those based on dynamic facial features.
Edge-preserving spatial deinterlacing for still images using block-based region classification

A number of deinterlacing algorithms have been proposed, however, they have some drawbacks: blocking artifacts or blurring around edges. To overcome these drawbacks, we propose an edge-preserving spatial deinterlacing algorithm using block-based region classification. The proposed algorithm classifies blocks of a line into three types of regions: connected, shaded, and unshaded. It finds the best matched block and interpolates lines block by block. Experimental results give better performance than conventional spatial deinterlacing algorithms, especially near edges, in terms of the peak signal to noise ratio (PSNR) and subjective visual quality.
Crosstalk characterization method for stereoscopic three-dimensional television

A crosstalk characterization model is proposed that shows the relationship between colors with and without crosstalk by using CIE tristimulus values. The proposed model is based on the 3D color measurement results of two different stereoscopic 3D televisions with shutter glasses, namely, a PDP and an LCD. The PDP exhibits a significant dependence of crosstalk on channel and tristimulus values, meaning that different channels and wavelengths result in different degrees of crosstalk, while the LCD exhibits little channel or wavelength dependence. Moreover, for PDP, the proportion of leaked light increases as the luminance of the opposite view decreases. In spite of different crosstalk characteristics, both PDP and LCD show similar performance resulting in 1.5ΔE*ab and 1.9ΔE*ab average color difference between the measured and the predicted. The proposed model could be used to develop a better crosstalk metric or a crosstalk cancellation algorithm by more accurately predicting colors including crosstalk.
Color preserving contrast enhancement for low light level images based on Retinex

In this paper, we propose a novel contrast enhancement algorithm for low light level images, which preserves image details and color constancy based on Retinex. We decompose an input low contrast image into luminance and chrominance components in Lab color space, which reflects the perception characteristics of human visual system well, and enhance the luminance component only. We first estimate illumination using adaptive bilateral filtering, which guarantees the available range of reflectance by considering proper neighboring pixels according to their luminance and color values. Then we enhance the contrast of the estimated illumination image using parabola-based tone mapping function. Finally, the enhanced luminance and the original chrominance are combined together to yield an enhanced color image. Experiment results show that the proposed algorithm enhances image details and edge structures by alleviating halo artifacts, and also preserves naturalness faithfully by avoiding color shifting artifacts.
Jagging artifact free interpolation [video signal processing]

Digital display products are gradually becoming diversified and pursuing high-quality image display. Digital TV supports various video signal formats from conventional SD to digital HD, because format conversion of the video image is required. Traditional format conversion of the video image is achieved by a 1D linear interpolator applied in both horizontal and vertical directions. Jagging artifacts can be expressed as the linkage of line segments in several directions. We present a method that removes jagging artifacts effectively using PCA (principle component analysis) and preserves the detail in a given image.
Motion compensation assisted motion adaptive interlaced-to-progressive conversion

Block-wise processing of motion compensated interlaced-to-progressive conversion (MC-IPC) can cause spurious errors that degrade converted image quality significantly. This paper presents a novel IPC method based on motion adaptive IPC (MA-IPC) with its adaptation assisted by the motion compensation. The motion adaptation is performed with respect to a motion selected from a limited set based on the accuracy evaluated with a reduced and overlapped block. The use of the reduced and overlapped blocks makes evaluation of the accuracy more precise, and implementation less costly. The motion adaptation structure ensures that any possible error occurs pixel-wise so that the block-wise spurious errors can be eliminated. The estimation of the motion is performed efficiently with data already available for the motion adaptation. The proposed motion compensation assisted MA-IPC method provides image quality similar to the MC-IPC, for those images, the MC-IPC offers significant improvement over the MA-IPC, yet at considerably lower computational and implementation costs.
Parallel Block Sequential Closed-Form Matting With Fan-Shaped Partitions.

Applying alpha matting to large images is a challenging task because of its computational complexity. This paper provides a divide and conquer strategy for performing closed-form matting. The matting problem, defined for an entire image, is broken down into systems of linear equations defined for very small blocks of the image. The sizes of the small systems are small enough for us to find solutions efficiently using a direct sparse linear equation system solver. The small systems are solved following a sequential order such that the alpha matte grows from a user scribble. With the block sequential application, matting is performed on fan-shaped partitions in parallel on multiple processing cores. Experiments on large test images as well as on standard benchmark test images show that the proposed parallel block sequential matting provides high quality alpha mattes with good scalability.
Contrast enhancement using histogram equalization with bin underflow and bin overflow

The histogram equalization (HE) is a widely used contrast enhancement method. But what is missing from the HE is a mechanism to control the rate of enhancement. The enhanced image always follows the uniform distribution. This paper presents a simple enhancement rate control mechanism for the HE. The gradient of the mapping function is controlled by putting constraints on the probability density function with the bin underflow (BU) and bin overflow (BO). The BUBO operation can provide the rate of enhancement from non to the full HE with a single parameter. With the enhancement rate control mechanism available, the HE can be used to perform image processing tasks such as black/white level stretch or automatic brightness control as well as variable rate contrast enhancement.
Blocking artifact free inverse discrete cosine transform

This paper presents the generalized lapped biorthogonal transform embedded inverse discrete cosine transform (ge-IDCT) as an alternative to the IDCT. The ge-IDCT with nonlinear weighting in the embedded transform domain can reconstruct the signal with alleviated blockishness. Additional complexity, imposed by the replacement, is trivial thanks to an efficient lattice structure. The proposed ge-IDCT is applied in the JPEG still image compression standard to demonstrate its validity.
Motion compensation assisted motion adaptive interlaced-to-progressive conversion

This paper presents a novel interlaced-to-progressive conversion (IPC) method based on the motion adaptive IPC (MA-IPC) with its adaptation assisted by the motion compensation. The motion adaptation is performed with respect to a motion selected from a limited set based on the accuracy evaluated with a reduced and overlapped block. The use of the reduced and overlapped blocks makes the evaluation of the accuracy more precise, and the implementation less costly. The motion adaptation structure ensures that any possible error occurs pixel-wise, so that block-wise spurious error can be eliminated. The estimation of the motion is performed efficiently with data already available for the motion adaptation. The proposed motion compensation assisted motion adaptive IPC (MoMA-IPC) method provides image quality similar to the motion compensated IPC (MC-IPC) for those images the MC-IPC offers significant improvement over the MA-IPC, yet at the considerably lower computational and implementation cost.
Particle Filtering Based Estimation of Consistent Motion and Disparity With Reduced Search Points

A particle filtering based block-wise estimation method for estimation of motion in a video sequence and joint estimation of disparity and motion in a stereo video sequence is proposed. Parameters of motion and disparity of a block in a sequence are defined as a state, and evolution of the state with respect to the block index is tracked with particle filtering. The state is assumed to be dependent on the states of neighboring blocks. Estimated motion and disparity fields are consistent and suitable for intermediate frame or view generation. The particle filter provides a method to effectively sample the search space. The particles are concentrated in regions where the probability density function for the state has large values. Hence, the locations of the particles are good candidates for search points of a fast search method. The proposed method can estimate motion and disparity with a fraction of search points necessary for conventional estimation methods.
Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model.

Images acquired by a camera show lens blur due to imperfection in the optical system even when images are properly focused. Lens blur is non-stationary in a sense that the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense that the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with a different symmetry and skewness to model complicated lens blur due to optical aberration in a properly focused images accurately. Blur of single focal length lenses is estimated, and the accuracy of the models is compared with the existing parametric blur models. An advantage of the proposed models is demonstrated through deblurring experiments.
Vision-based cleaning area control for cleaning robots

This paper provides a vision based HCI method for a user to command a cleaning robot to move to a specific location in home environment. Six hand poses are detected from a video sequence taken from a camera on the cleaning robot. AdaBoost based hand-pose detectors are trained with a reduced Haar-like feature set to make the detectors robust to the influence of the complex background. The first three stages of the cascade in the six detectors are used as pose estimation to reduce the computational complexity. The cleaning area is determined from the detected pose. The performances of the proposed detectors are validated with a set of test images with cluttered background. The cleaning area control is simulated with real-world video sequences. The proposed method can effectively control a cleaning robot without the need for a user to wear or employ any input devices.
Converting the interlaced 3:2 pulldown film to the NTSC video without motion artifacts

In this paper, we present a new format conversion methodology in which the interlaced 3:2 pulldown film is converted to the NTSC video. For display purposes, the 24 frames/s film is converted to 60 fields/s video by alternatively repeating each frame for 3 fields and 2 fields. This conversion results in severe motion judder when the film is displayed on the progressive TV. Although motion estimation and compensation (ME and MC) are employed to eliminate the motion judder, other artifacts may be resulted depending on the accuracy of ME. Such motion artifacts are including halo effects, block artifacts, and the severe distortion in periodic patterns when the block-based ME is used to interpolate the intermediate fields. To resolve those problems, we propose a robust full-search block motion algorithm using pseudo M-estimator, which is performed on field images to reduce the physical memory size (reducing motion search range) when implemented with hardware.
Block-based motion estimation using the pixelwise classification of the motion compensation error

We propose block-based motion estimation algorithms based on the pixelwise classification of the motion compensation error: (1) displaced frame difference (DFD) and (2) brightness constraint constancy term (BCCT). Block-based motion estimation has disadvantages such as unreliable motion vectors and block artifacts, especially in object boundaries. The proposed algorithms attempt to reduce artifacts in boundary blocks caused by incorrect assumption of a single rigid (translational) motion. They yield more appropriate motion vectors in boundary blocks under the assumption that there exist up to three different regions with different motions. Experimental results with several test sequences show the effectiveness of the proposed algorithms.
An effective de-interlacing technique using two types of motion information

In this paper, we propose a new de-interlacing algorithm using two types of motion information, i.e., the block-based and the pixel-based motion information. In the proposed scheme, block-wise motion is first calculated using the frame differences. Then, it is refined by the pixel-based motion information. The results of hardware implementation show that the proposed scheme using block-wise motion is more robust to noise than the conventional schemes using pixel-wise motion. Also, the proposed spatial interpolation provides a good visual performance in the case of moving diagonal edges.
Blocking effect removal using regularization and dithering

Image compression codecs suffer from the various coding artifacts at low bit rate. The coding artifacts can be removed by way of post-processing. Most of the coding artifacts removal algorithms depend on a priori knowledge of the original image, which is about its smoothness. The resulting surface of the restored image is often too smooth to be the one in the natural scene. Dithering is based on the idea of adding controlled noise to the system to achieve the better results. An algorithm based on regularization and dithering is proposed to remove the coding artifacts. The results show that the algorithm removes the coding artifacts successfully and the surface of the restored image is more visually pleasing.
Learning an Intrinsic Image Decomposer Using Synthesized RGB-D Dataset

Intrinsic image decomposition refers to recover the albedo and shading from images, which is an ill-posed problem in signal processing. As realistic labeled data are severely lacking, it is difficult to apply learning methods in this issue. In this letter, we propose using a synthesized dataset to facilitate the solving of this problem. A physically based renderer is used to generate color images and their underlying ground-truth albedo and shading from three-dimensional models. Additionally, we render a Kinect-like noisy depth map for each instance. We utilize this synthetic dataset to train a deep neural network for intrinsic image decomposition and further fine-tune it for real-world images. Our model supports both RGB and RGB-D as input, and it employs both high-level and low-level features to avoid blurry outputs. Experimental results verify the effectiveness of our model on realistic images.
Top-Push Video-Based Person Re-identification

Most existing person re-identification (re-id) models focus on matching still person images across disjoint camera views. Since only limited information can be exploited from still images, it is hard (if not impossible) to overcome the occlusion, pose and camera-view change, and lighting variation problems. In comparison, video-based re-id methods can utilize extra space-time information, which contains much more rich cues for matching to overcome the mentioned problems. However, we find that when using video-based representation, some inter-class difference can be much more obscure than the one when using still-image-based representation, because different people could not only have similar appearance but also have similar motions and actions which are hard to align. To solve this problem, we propose a top-push distance learning model (TDL), in which we integrate a top-push constrain for matching video features of persons. The top-push constraint enforces the optimization on top-rank matching in re-id, so as to make the matching model more effective towards selecting more discriminative features to distinguish different persons. Our experiments show that the proposed video-based reid framework outperforms the state-of-the-art video-based re-id methods.
Weakly Supervised Learning on Pre-image Problem in Kernel Methods

This paper presents a novel alternative approach, namely weakly supervised learning (WSL), to learn the pre-image of a feature vector in the feature space induced by a kernel. It is known that the exact pre-image may typically seldom exist, since the input space and the feature space are not isomorphic in general, and an approximate solution is required in past. The proposed WSL, however, would find an appropriate rather than only a purely approximate solution. WSL is able to involve some weakly supervised prior knowledge into the study of pre-image. The prior knowledge is weak and no class label of the sample is required, providing only information of positive class and negative class which should properly depend on applications. The proposed algorithm is demonstrated on kernel principal component analysis (KPCA) with application to illumination normalization and image denoising on faces. Evaluations of the performance of the proposed algorithm show notable improvement as comparing with some well-known existing approaches
HEp-2 cells staining patterns classification via wavelet scattering network and random forest

Automatic classification of Human Epithelial Type-2 (HEp-2) cells staining patterns is a challenging and important problem in the field of medical image analysis. This paper proposed an efficient framework to address the HEp-2 cell image classification problem based on wavelet scattering network and Random Forest. The wavelet scattering network computes rotation-invariant wavelet coefficients as representations of cells images which are robust to different orientations and scale changes in HEp-2 cells images. A Random Forest classifier is trained and then subsequently used to predict the pattern label of a cell image from six classes. Our method is extensively evaluated on two benchmarking datasets based on different experimental protocols. Results show that the proposed system is highly effective on both ICPR 2012 dataset and ICIP 2013 dataset and outperforms several state-of-the-art methods including LBPs, RICLBP, PRICoLBP, and high-order statistic of microtexton.
Reidentification by Relative Distance Comparison

Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models.
RGB-Infrared Cross-Modality Person Re-identification

Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics. For person Re-ID, this is a very challenging cross-modality problem that has not been studied so far. In this work, we address the RGB-IR cross-modality Re-ID problem and contribute a new multiple modality Re-ID dataset named SYSU-MM01, including RGB and IR images of 491 identities from 6 cameras, giving in total 287,628 RGB images and 15,792 IR images. To explore the RGB-IR Re-ID problem, we evaluate existing popular cross-domain models, including three commonly used neural network structures (one-stream, two-stream and asymmetric FC layer) and analyse the relation between them. We further propose deep zero-padding for training one-stream network towards automatically evolving domain-specific nodes in the network for cross-modality matching. Our experiments show that RGB-IR cross-modality matching is very challenging but still feasible using the proposed model with deep zero-padding, giving the best performance. Our dataset is available at http:// isee.sysu.edu.cn/project/RGBIRReID.htm.
Online visual tracking via correlation filter with convolutional networks

Robust online visual tracking is a challenging task of the computer vision due to its violent variation within the video sequences. To approach these issues, deep networks have been applied in order to improve accuracy and correlation filter based trackers perform excellent efficiency and adaptation to scale. In this paper, we present a novel method with convolutional networks and correlation filter. A simple two-layer convolutional network is constructed to learn robust representations, which encode the inner geometric layout and local structural information, and the tracking framework resorts to learning discriminative correlation filters based on them. For our method satisfies both veracity and efficiency by finding a compromise between two theories, it performs favorably against several state-of-the-art methods with 50 public challenging videos.