nils-herrmann/clean_scirepeval huggingface dataset:
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors

In this paper, we present Linear Discriminant Projections (LDP) for reducing dimensionality and improving discriminability of local image descriptors. We place LDP into the context of state-of-the-art discriminant projections and analyze its properties. LDP requires a large set of training data with point-to-point correspondence ground truth. We demonstrate that training data produced by a simulation of image transformations leads to nearly the same results as the real data with correspondence ground truth. This makes it possible to apply LDP as well as other discriminant projection approaches to the problems where the correspondence ground truth is not available, such as image categorization. We perform an extensive experimental evaluation on standard data sets in the context of image matching and categorization. We demonstrate that LDP enables significant dimensionality reduction of local descriptors and performance increases in different applications. The results improve upon the state-of-the-art recognition performance with simultaneous dimensionality reduction from 128 to 30.
Object-detection with a varying number of eigenspace projections

We present a method allowing a significant speed-up of the eigen-detection method (detection based on principle component analysis). We derive a formula for an upper bound on the class-conditional probability (or equivalently a lower bound on the Mahalanobis distance) on which detection is based. Often, the lower bound of Mahalanobis distance (MD) reaches a preset threshold after computation of only a few eigen-projections. In this case the computation of MD can be immediately terminated. Regardless of the precise value of MD, the detection hypothesis (object from class /spl Omega/ is detected) can be rejected. While provably obtaining results identical to the standard technique, we achieved a two- to three-fold speed-up in face detection experiments on images from the CMU database.
Object recognition using a tag

We propose a method for object recognition in an unconstrained condition. This includes wide range of illumination, unknown view points and complicated background. We simplified the general problem by placing special design patterns (Tsai's (1987) camera calibration chart) on the object that allows us to solve the pose determination problem easily. After establishing the methodological framework, we experiment with this technique on a wide range of real data to show the reliability of the approach. We argue that with this capability we should be able to (a) establish camera position with respect to a landmark, (b) recognise an object which is tagged with the calibration chart and (c) test any camera calibration and 3D pose estimation routines, thus facilitating future research and applications in mobile robots navigation, 3D reconstruction and stereo vision.
Optimal Randomized RANSAC

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is close to the shortest possible and superior to any deterministic verification strategy. A provably fastest model verification strategy is designed for the (theoretical) situation when the contamination of data by outliers is known. In this case, the algorithm is the fastest possible (on the average) of all randomized RANSAC algorithms guaranteeing a confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making, in particular, a modified sequential probability ratio test (SPRT). Next, the R-RANSAC with SPRT algorithm is introduced. The algorithm removes the requirement for a priori knowledge of the fraction of outliers and estimates the quantity online. We show experimentally that on standard test data, the method has performance close to the theoretically optimal and is 2 to 10 times faster than standard RANSAC and is up to four times faster than previously published methods.
Two-view geometry estimation unaffected by a dominant plane

A RANSAC-based algorithm for robust estimation of epipolar geometry from point correspondences in the possible presence of a dominant scene plane is presented. The algorithm handles scenes with (i) all points in a single plane, (ii) majority of points in a single plane and the rest off the plane, (iii) no dominant plane. It is not required to know a priori which of the cases (i)-(iii) occurs. The algorithm exploits a theorem we proved, that if five or more of seven correspondences are related by a homography then there is an epipolar geometry consistent with the seven-tuple as well as with all correspondences related by the homography. This means that a seven point sample consisting of two outliers and five inliers lying in a dominant plane produces an epipolar geometry which is wrong and yet consistent with a high number of correspondences. The theorem explains why RANSAC often fails to estimate epipolar geometry in the presence of a dominant plane. Rather surprisingly, the theorem also implies that RANSAC-based homography estimation is faster when drawing nonminimal samples of seven correspondences than minimal samples of four correspondences.
On Combining Multiple Segmentations in Scene Text Recognition

An end-to-end real-time scene text localization and recognition method is presented. The three main novel features are: (i) keeping multiple segmentations of each character until the very last stage of the processing when the context of each character in a text line is known, (ii) an efficient algorithm for selection of character segmentations minimizing a global criterion, and (iii) showing that, despite using theoretically scale-invariant methods, operating on a coarse Gaussian scale space pyramid yields improved results as many typographical artifacts are eliminated. The method runs in real time and achieves state-of-the-art text localization results on the ICDAR 2011 Robust Reading dataset. Results are also reported for end-to-end text recognition on the ICDAR 2011 dataset.
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp:felk:cvut:cz/t-less.
ICDAR 2015 competition on Robust Reading

A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.
MMSE nonlocal means denoising algorithm for Poisson noise removal

A nonlocal minimum mean square error (MMSE) image denoising algorithm to remove Poisson noise is proposed in this work. Based on the Bayesian estimation theory, we first derive the nonlocal MMSE denoising filter, which can minimize the mean square error (MSE) of a denoised block. Then, we develop an approximation of the filter for practical implementation. Simulation results show that the proposed algorithm provides significantly better denoising performance than the conventional nonlocal means filter and its recent extension for Poisson noise.
Single image dehazing based on contrast enhancement

A simple and adaptive single image dehazing algorithm is proposed in this work. Based on the observation that a hazy image has low contrast in general, we attempt to restore the original image by enhancing the contrast. First, the proposed algorithm estimates the airlight in a given hazy image based on the quad-tree subdivision. Then, the proposed algorithm estimates the transmission map to maximize the contrast of the output image. To measure the contrast, we develop a cost function, which consists of a standard deviation term and a histogram uniformness term. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct fine details in original scenes clearly.
Two-step RDF query processing for Linked Data

Since RDF triples are modeled as graphs, we cannot directly adopt existing solutions from relational databases and XML technologies. Thus, there are still a number of open problems in the area of Linked Data. We present a hybrid method between centralized and distributed approaches. By using auxiliary indexes based on the MBB approximation, our approach can retrieve distributed Linked Data efficiently. The goal of our approach is to support efficient join query processing by quickly pruning unnecessary scanning data.
Reliable Depth-of-Field Rendering Using Estimated Depth Maps

A reliable algorithm for rendering depth of field (DoF) effects using estimated depth maps, obtained through stereo matching, is proposed in this paper. The proposed algorithm generates blurring to simulate images spontaneously seen by human vision systems. We develop two types of windows : circle of confusion (CoC) blurring window and peripheral blurring window. First, the CoC blurring window is determined by comparing the depth values of a gazing point and each sample point. Second, the peripheral blurring window is obtained by calculating the distance between the gazing and sample points. Then, we combine the two windows to make the total blurring window. Finally, through a masking process, we modulate the total blurring window to provide a more natural DoF. Experimental results demonstrate that the proposed algorithm provides realistic blurring, by preserving edges clearly as well as blurring far points from the gazing point effectively.
Motion-Compensated Frame Interpolation Using Bilateral Motion Estimation and Adaptive Overlapped Block Motion Compensation

In this work, we develop a new motion-compe (MC) interpolation algorithm to enhance the temporal resolution of video sequences. First, we propose the bilateral motion estimation scheme to obtain the motion field of an interpolated frame without yielding the hole and overlapping problems. Then, we partition a frame into several object regions by clustering motion vectors. We apply the variable-size block MC (VS-BMC) algorithm to object boundaries in order to reconstruct edge information with a higher quality. Finally, we use the adaptive overlapped block MC (OBMC), which adjusts the coefficients of overlapped windows based on the reliabilities of neighboring motion vectors. The adaptive OBMC (AOBMC) can overcome the limitations of the conventional OBMC, such as over-smoothing and poor de-blocking. Experimental results show that the proposed algorithm provides a better image quality than conventional methods both objectively and subjectively
Robust stereo matching under radiometric variations based on cumulative distributions of gradients

We propose a robust stereo matching algorithm for images captured under varying radiometric conditions, such as exposure and lighting variations, based on the cumulative distributions of gradients. The gradient operator extracts local changes in pixel values, which are less sensitive to radiometric variations than the original pixel values. Moreover, the cumulative distribution function (CDF) of gradient vectors reflects the ranks of edge strength levels, and corresponding pixels in stereo images tend to have similar ranks regardless of radio-metric conditions. Therefore, we design the matching cost function based on the dissimilarity of gradient CDF values. However, since multiple pixels in an image may have the same gradient CDF value, we further constrain the correspondence matching by checking the dissimilarity of gradient orientations. Finally, to estimate an accurate disparity at each pixel, we adaptively aggregate matching costs using the color similarity and the geometric proximity of neighboring pixels. Experimental results demonstrate that the proposed algorithm provides more accurate disparities than conventional algorithms, especially under varying lighting conditions.
Image and video colorization based on prioritized source propagation

An efficient colorization scheme for images and videos based on prioritized source propagation is proposed in this work. A user first scribbles colors on a set of source pixels in an image or the first frame of a movie. The proposed algorithm then propagates those colors to the other non-source pixels and the subsequent frames. Specifically, the proposed algorithm identifies the non-source pixel with the highest priority, which can be most reliably colorized. Then, its color is interpolated from the neighboring pixels. This is repeated until the whole image or movie is colorized. Simulation results demonstrate that the proposed algorithm yields more reliable colorization performance than the conventional algorithms.
R-intersections and R-unions of neutrosophic cubic sets

R-unions and R-intersections of T-external (Iexternal, F-external) neutrosophic cubic sets are considered. Examples to show that the R-intersection and R-union of Texternal (I-external, F-external) neutrosophic cubic sets may not be a T-external (I-external, F-external) neutrosophic cubic set are provided. Conditions for the R-union and R-intersection of T-external (I-external, F-external) neutrosophic cubic sets to be a T-external (I-external, F-external) neutrosophic cubic set are discussed.
Power-constrained backlight scaling and contrast enhancement for TFT-LCD displays

We propose a novel backlight-scaled contrast enhancement algorithm to maintain the image quality when the backlight of a TFT-LCD display is dimmed for power reduction. First, we derive the transformation function to maintain the perceived luminance and enhance the contrast. Second, we model the perceptual distortion, which describes the information loss due to the backlight scaling. Then, we propose a Lagrangian method to maximize the backlight-scaled image contrast subject to the constraint on the perceptual distortion. Simulation results show that the proposed algorithm provides better image qualities than the conventional algorithms.
PGT: Proposal-guided object tracking

We propose a robust visual tracking system, which refines initial estimates of a base tracker by employing object proposal techniques. First, we decompose the base tracker into three building blocks: representation method, appearance model, and model update strategy. We then design each building block by adopting and improving ideas from recent successful trackers. Second, we propose the proposal-guided tracking (PGT) algorithm. Given proposals generated by an edge-based object proposal technique, we select only the proposals that can improve the result of the base tracker using several cues. Then, we discriminate target proposals from non-target ones, based on the nearest neighbor classification using the target and background models. Finally, we replace the result of the base tracker with the best target proposal. Experimental results demonstrate that proposed PGT algorithm provides excellent results on a visual tracking benchmark.
Background subtraction using encoder-decoder structured convolutional neural network

A background subtraction algorithm using an encoderdecoder structured convolutional neural network is proposed in this work, in order to segment out moving objects from the background. A target frame, its previous frame, and a background model are concatenated and fed into the network as the input. Then, the encoder generates a highlevel feature vector, and the decoder converts the feature vector into a segmentation map, which roughly identifies moving object regions. Moreover, we develop background modeling and foreground extraction techniques, which exploit contour information. Experimental results on the CD-net2014 dataset demonstrate that the proposed algorithm outperforms state-of-the-art techniques significantly.
Frame loss concealment for stereoscopic video based on inter-view similarity of motion and intensity difference

An efficient frame loss concealment algorithm for stereoscopic video based on the inter-view similarity of motion vectors and intensity differences is proposed in this work. Suppose that a frame at time t in the right view is lost during the transmission. To conceal its loss, we use the information in the previous frame at time t − 1 in the right view and the frames at t − 1 and t in the left view. More specifically, we first estimate the disparity vector field of the previous frame to find the matching pixels in the left view, and determine the motion vectors and the intensity differences of those matching pixels. By projecting those motion vectors and intensity differences onto the right view, we recover the lost frame. Simulation results demonstrate that the proposed algorithm provides significantly better performance than conventional algorithms.
Power control of a doubly fed induction machine without rotational transducers

This paper describes a variable speed drive and power control of a doubly fed induction machine (DFIM) without rotational transducers. A stator flux orientation scheme and rotor speed estimator are employed to achieve the power generating control. To show the validity of theoretical analysis, a 5 HP DFIM prototype system and PWM power converter are built. Computer simulations and experimental results are presented to support the discussion.
Efficient Prediction Structure for 2-D Wide Multi-view Video Sequence

In this paper, two-dimensional (2D) spatial hierarchical B view prediction structure is proposed to exploit redundancies between neighboring frames in 2D multi-view video sequences. We also develop a virtual view generation algorithm based on the bilateral disparity estimation and a novel skip mode. We implement the proposed structure by modifying the JMVM 5.1 software, and compare its performance with the conventional prediction structure. Test sequences are obtained from a 2D (5x10) camera array. Simulation results show that proposed structure and the skip mode achieve about 1.2 d B coding gain.
Multiple description motion coding algorithm for robust video transmission

This paper proposes a novel algorithm for robust transmission of video sequences, based on the multiple description motion coding (MDMC). In the MDMC, the motion vector field is encoded into two descriptions, which are transmitted over distinct channels to the decoder. The decoder is designed to provide an acceptable quality prediction image, even if one of the descriptions is lost during the transmission. Moreover, the decoder reconstructs a higher quality prediction image, if both the descriptions are received without error. A complete multiple description video coder is implemented by modifying the syntax of the H.263 standard. Intensive simulation results show that the proposed algorithm provides much better performance than the H.263 coder in realistic error prone environment.
Robust Image Watermarking Based on Local Zernike Moments

Invariant image features can be used to carry watermarks so as to improve the robustness of the watermarks against geometric transformations. However, most previous watermarking algorithms using invariant features are still sensitive to cropping attacks and combinations of rotation, scaling, and translation (RST) attacks. To improve the resilience against these attacks, we propose a multi-bit image watermarking algorithm using local Zernike moments (LZMs). The magnitude of LZMs are dither-modulated to embed watermark bits. To achieve scale invariance, we restore the original sampling rate using invariant centroid and geometric moments. Simulation results demonstrate that the proposed watermarking algorithm is robust against various geometric attacks as well as signal processing attacks.
Compression of 3D triangle mesh sequences

In this paper, we propose a new geometry compression algorithm for 3D mesh sequences, based on vertex-wise motion vector (MV) prediction. In general, the MV of a vertex is highly correlated to those of adjacent vertices. To exploit the high correlation, we define a neighborhood of a vertex, and predict the MV of the vertex from those of the neighborhood. It is shown that the proposed algorithm has simpler structure than the existing segment-based algorithm. Furthermore, simulation results demonstrate that the proposed algorithm yields better compression performance than the existing algorithm.
Multiple channel division for efficient distributed video coding

A novel concept of channel division to improve the performance of distributed video coding is proposed in this work. At the decoder, the proposed algorithm partitions each side information frame into multiple regions with different expected distortions. It is shown that the partitioning is equivalent to the division of the virtual noisy channel from the encoder to the decoder into multiple channels. Then, the proposed algorithm analyzes the noise characteristics of those multiple channels, and allocates a limited bit budget to those channels adaptively to improve the rate-distortion performance. Simulation results demonstrate that the proposed algorithm provides up to 5 dB better performance than the conventional DVC algorithms, as well as reduces the decoding complexity significantly.
Large-area oranic solar cells with metal electrode

This study examined the effects of the cell area in spray-coated organic solar cells (OSCs) by introducing a metal sub-electrode and grid-electrode to produce large-area cells up to 12.25 cm2. The effects of the cell area were investigated systematically to realize large-area cells. The series resistance could be reduced significantly by inserting a metal grid-electrode onto the indium tin oxide (ITO) anode, yielding a power conversion efficiency of 2.11 % at a cell area of 12.25 cm2 and 2.49 % at an effective photocurrent generated area of 11.23 cm2 under AM.1.5 simulated illumination. This is comparable to the 3.13 % obtained in the cell produced by spray-coating at a cell area of 0.38 cm2.