nils-herrmann/clean_scirepeval huggingface dataset:
Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor

In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.
Correspondence Matching of Multi-View Video Sequences Using Mutual Information Based Similarity Measure

We propose a correspondence matching algorithm for multi-view video sequences, which provides reliable performance even when the multiple cameras have significantly different parameters, such as viewing angles and positions. We use an activity vector, which represents the temporal occurrence pattern of moving foreground objects at a pixel position, as an invariant feature for correspondence matching. We first devise a novel similarity measure between activity vectors by considering the joint and individual behavior of the activity vectors. Specifically, we define random variables associated with the activity vectors and measure their similarity using the mutual information between the random variables. Moreover, to find a reliable homography transform between views, we find consistent pixel positions by employing the iterative bidirectional matching. We also refine the matching results of multiple source pixel positions by minimizing a matching cost function based on the Markov random field. Experimental results show that the proposed algorithm provides more accurate and reliable matching performance than the conventional activity-based and feature-based matching algorithms, and therefore can facilitate various applications of visual sensor networks.
An error detection and recovery algorithm for compressed video signal using source level redundancy

The motion compensation-discrete cosine transform (MC-DCT) coding is an efficient compression technique for a digital video sequence. However, the compressed video signal is vulnerable to transmission errors over noisy channels. In this paper, we propose a robust video transmission algorithm, which protects the compressed video signal by inserting redundant information at the source level. The proposed algorithm encodes every lth frame in the semi-intra frame (S-frame) mode, in which the redundant parity-check DC coefficients (PDCs) are systematically inserted into the compressed bitstream. Then, the decoder is capable of recovering very severe transmission errors, such as loss of an entire frame, in addition to detecting the errors effectively without requesting any information from external devices. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.
Enhancement of noisy low-light images via structure-texture-noise decomposition

We propose a novel noisy low-light image enhancement algorithm via structure-texture-noise (STN) decomposition. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. Specifically, we first enhance the contrast of the structure image, by extending a 2D histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving texture components from the noise image, and enhance it by exploiting the perceptual response of the human visual system. Experimental results demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while removing noise without artifacts.
Novel fractal image compression method with non-iterative decoder

We propose a novel fractal image compression technique, which does not require iteration at the decoder. The main problem relating to the conventional noniterative algorithm is that the smooth region cannot be coded efficiently, since the size of the range block is limited to be less than 8/spl times/8. We alleviate this problem by generating two codebooks from a planarly approximated image. In other words, the first codebook is generated by the smoothing operator for large and smooth range blocks and the second codebook is generated by the spatial contraction operator for small and active range blocks, respectively. The computer simulation results on the real images demonstrate that the proposed algorithm provides much better performance than most other fractal-based coders, in terms of the subjective quality as well as the objective quality (PSNR). Moreover, the proposed algorithm is very fast in decoding, since it does not require iteration at the decoder.
Subpixel rendering without color distortions for diamond-shaped PenTile displays

A novel subpixel rendering algorithm for diamond-shaped PenTile displays, which improves the apparent display resolution and reduces color fringing artifacts, is proposed in this work. We develop two types of filters: main filters and nonnegative filters. First, the main filters are derived to minimize the error between an original input image and a perceived image. Second, the nonnegative filters are modified from the main filters. While the main filters are optimized for improving the resolution, the nonnegative filters suppress color distortions effectively. We analyze local characteristics of an image and estimate the amount of color distortions in each region. Based on the color distortion analysis, we render the image by applying adaptive combinations of the main filters and the nonnegative filters. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms, by not only improving the apparent resolution but also suppressing color distortions effectively.
CDTS: Collaborative Detection, Tracking, and Segmentation for Online Multiple Object Segmentation in Videos

A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment, by employing the alternate shrinking and expansion (ASE) segmentation. Third, we refine the segment tracks, by detecting object disappearance and reappearance cases and merging overlapping segment tracks. Experimental results show that the proposed algorithm significantly surpasses the state-of-the-art conventional algorithms on benchmark datasets.
Semantic Line Detection and Its Applications

Semantic lines characterize the layout of an image. Despite their importance in image analysis and scene understanding, there is no reliable research for semantic line detection. In this paper, we propose a semantic line detector using a convolutional neural network with multi-task learning, by regarding the line detection as a combination of classification and regression tasks. We use convolution and max-pooling layers to obtain multi-scale feature maps for an input image. Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps. Next, we feed the feature vector into the parallel classification and regression layers. The classification layer decides whether the line candidate is semant ic or not. In case of a semantic line, the regression layer determines the offset for refining the line location. Experimental results show that the proposed detector extracts semantic lines accurately and reliably. Moreover, we demonstrate that the proposed detector can be used successfully in three applications: horizon estimation, composition enhancement, and image simplification.
Robust transmission of video sequence using double-vector motion compensation

This paper proposes a motion compensation (MC) algorithm for robust transmission of video sequence, called the double-vector motion compensation (DMC). In the DMC, each block B in a frame is predicted from the weighted superposition of two blocks in the previous two frames, using two motion vectors. Therefore, when one of these two blocks is corrupted during the transmission, the decoder can efficiently suppress its error propagation to the subsequent frames, by predicting B only from the other block. It is shown by analysis that the DMC algorithm yields significantly lower error bounds for the subsequent frames than the conventional MC technique. Furthermore, the DMC algorithm can be combined with an effective concealment algorithm, which is capable of recovering very severe transmission errors, such as loss of an entire frame. A complete video coder, based on the DMC, is implemented by modifying the MC syntax of the H.263 standard and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the H.263 coder in the error-prone environment.
An Object Removal Using Multi-view Sequence Inpainting Technique

A framework for object removal in multi-view video sequences is proposed in this work. After a mask cloning procedure, we arrange the sites at which source patches are pasted over mask areas. To each site, candidate blocks are assigned by a decimation scheme, which helps to reduce the computational complexity in the following procedures. Finally, we choose the best source block from the candidates by a global optimization technique. Simulation results illustrate that the proposed algorithm removes the objects in the mask area effectively, while preserving the spatial-temporal consistency.
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions

An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.
A fractal vector quantizer for image coding

We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.
Efficient stereo video coding based on frame skipping for real-time mobile applications

An efficient stereo video coding algorithm based on frame skipping is proposed for real-time mobile applications in this work. Based on the binocular suppression theory, the proposed algorithm skips a selected set of frames to save the encoding complexity and the transmission bit rate. At the decoder side, we reconstruct those skipped frames faithfully using the advanced bilateral interpolation method. Extensive simulation results demonstrate that the proposed algorithm provides better PSNR performances than the conventional stereo coding algorithm, while achieving realtime encoding and decoding.
Consistent Stereo Matching Under Varying Radiometric Conditions

A consistent stereo matching (CSM) algorithm under varying radiometric conditions, such as lighting and exposure variations, for intermediate view synthesis is proposed in this work. First, we transform the colors of stereo images adaptively so that they are similar at corresponding pixels. Since the correspondences are generally unknown before stereo matching, we estimate pseudo-disparity vectors by sorting pixels based on the cumulative color histograms and use those pseudo vectors in the color transform. Then, to improve the accuracy of stereo matching, we jointly estimate the disparity maps for virtual intermediate views as well as those for real views, based on the consistency criterion that an object point should have the same disparity through all the views. Specifically, we compute matching costs using the reliability term and aggregate the costs to obtain initial disparity maps. We then refine the initial disparity maps by minimizing an energy function, which includes the consistency term. Experimental results show that the proposed CSM algorithm significantly reduces the error rate of disparity estimation under different radiometric conditions and synthesizes high quality intermediate views.
Dark image enhancement based onpairwise target contrast and multi-scale detail boosting

A dark image enhancement algorithm based on the pairwise target contrast and the multi-scale detail boosting is proposed in this work. We first compute the pairwise target contrast between a pair of pixels, which represents the desired gray level difference of the two pixels in the output image. By aggregating the pairwise target contrasts for all pairs of pixels in an image, we formulate a cost function for the enhancement. By minimizing the cost function, we obtain the optimal transformation function and enhance the image. In addition, we propose a multi-scale approach to boost details in the globally enhanced image. Experimental results show that the proposed algorithm enhances the contrast and visibility of dark images more effectively than conventional algorithms.
Robust transmission of video over noisy channel using parity motion vector

This paper introduces a novel concept of PMV (parity motion vector) into the MC-DCT (motion compensation-discrete cosine transform) coder, in order to improve its robustness to transmission errors over noisy channels. By inserting the redundant PMVs systematically into the compressed bitstream, the proposed algorithm is capable of recovering very severe transmission errors, such as loss of an entire frame. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.
Approximate solution to optimized contrast enhancement for hazy image

An approximate solution to single image dehazing algorithm is proposed in this work. We improve the performance of our previous dehazing algorithm, which optimally estimates transmission values, by approximating a cost function. The cost function consists of two terms: contrast cost and information loss cost. The information loss cost is nonlinear, which cannot be converted to a quadratic form. To address this issue, the proposed algorithm applies the logistic function and replaces the information loss term to the logistic form, thereby achieving complexity reduction. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct details in original scenes clearly.
Video Deraining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion

A novel algorithm to remove rain or snow streaks from a video sequence using temporal correlation and low-rank matrix completion is proposed in this paper. Based on the observation that rain streaks are too small and move too fast to affect the optical flow estimation between consecutive frames, we obtain an initial rain map by subtracting temporally warped frames from a current frame. Then, we decompose the initial rain map into basis vectors based on the sparse representation, and classify those basis vectors into rain streak ones and outliers with a support vector machine. We then refine the rain map by excluding the outliers. Finally, we remove the detected rain streaks by employing a low-rank matrix completion technique. Furthermore, we extend the proposed algorithm to stereo video deraining. Experimental results demonstrate that the proposed algorithm detects and removes rain or snow streaks efficiently, outperforming conventional algorithms.
View-dependent compression of 3D voxel models based on skeleton representation

We propose a view-dependent compression algorithm for 3D binary voxel models, based on the skeleton representation. An input model is first partitioned into several segments. The segmentation increases the number of skeleton points, requiring more bits than the original model. Therefore, we develop a remodeling algorithm, which reduces the number of skeleton points in each segment effectively without any loss of information. Each segment is then compressed with a context-based arithmetic coder. Before the transmission, the encoder is informed of the viewing parameters via feedback channel. Then, the encoder transmits visible segments in detail, while cutting off invisible segments. Simulation results show that the proposed algorithm requires much less bitrate than the conventional skeleton-based algorithm, while exhibiting faithful image quality.
Video object segmentation using multiple random walkers with GMM restart rule

In this work, we propose a weakly supervised online video object segmentation algorithm, which accepts a bounding box as user annotation. First, we estimate the initial distributions of the foreground and the background by employing a visual saliency detector. Next, we simulate movements of double random walkers, one for the foreground and the other for the background. To this end, we introduce a novel restart rule based on Gaussian mixture models (GMMs). We update the GMMs during the random walk simulation to encourage interactions between the two random walkers. To achieve video segmentation, from the second to the last frames, we sequentially propagate the segmentation labels and the GMMs of the previous frame in order to maintain temporal consistency. Experimental results demonstrate that the proposed algorithm outperforms conventional video object segmentation algorithms.
Fast object tracking using color histograms and patch differences

A fast visual object tracking algorithm using novel object appearance models is proposed in this work. We develop a color histogram model and a patch difference model to extract color and texture feature vectors, respectively. Then, we apply k-nearest neighbor classifiers to the color and texture feature vectors and obtain the foreground probability map. We then perform a hierarchical mean shift process on the map to identify the object window. Experimental results demonstrate that proposed algorithm outperforms the conventional algorithms in terms of both tracking accuracy and processing speed.
Multiple Description Coding of Plane-Based 3-D Surfaces

We present a multiple description coding (MDC) scheme for 3D plane-based surfaces. First, planes are split into two disjoint subsets, called descriptions, each of which provides an equal contribution in 3D surface reconstruction. To optimize the quality of the decoded surface, planes in each description are adaptively compressed according to the channel error condition. Then, the two compressed bitstreams are transmitted over distinct channels to the decoder. At the decoder, if both channels are available, the two bitstreams are decoded and merged together to reconstruct a high quality surface. If only one channel is available, we employ a hole filling method to fill visual holes and reconstruct a smooth 3D surface. Therefore, the proposed algorithm provides an acceptable 3D surface, even when one channel is totally lost.
Frame rate up-conversion using perspective transform

In this paper, we propose a frame rate up-conversion (FRUC) algorithm to increase the temporal resolution of video sequences at the decoder side. First, the proposed algorithm segments a frame into several objects. Then, perspective transforms are used to motion-compensate each object, while the translational block matching algorithm (BMA) is applied to the background. Furthermore, the overlapped block motion compensation (OBMC) technique is used to reduce blocking artifacts in boundary blocks. Experimental results show that the proposed algorithm provides better performance than the conventional approach.
Multihypothesis trajectory analysis for robust visual tracking

The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work. We employ multiple component trackers using texture, color, and illumination invariant features, respectively. Each component tracker traces a target object forwardly and then backwardly over a time interval. By analyzing the pair of the forward and backward trajectories, we measure the robustness of the component tracker. To this end, we extract the geometry similarity, the cyclic weight, and the appearance similarity from the forward and backward trajectories. We select the optimal component tracker to yield the maximum robustness score, and use its forward trajectory as the final tracking result. Experimental results show that the proposed MTA tracker improves the robustness and the accuracy of tracking, outperforming the state-of-the-art trackers on a recent benchmark dataset.
Error Resilient 3-D Mesh Compression

An error resilient three-dimensional (3-D) mesh coding system is proposed in this paper. The encoder uses a shape adaptive data partitioning scheme to alleviate the effect of error propagation. An input mesh surface is coarsely divided into smooth and detailed regions, and each region is further divided into partitions of similar sizes. Then, those partitions are progressively compressed and their joint boundaries are compressed using the boundary edge collapse rule. At the decoder, the boundary edge collapse rule facilitates the seamless assembly of the partitions. When no data is available for a partition due to transmission errors, we employ a concealment scheme based on the projection onto convex sets (POCS) theory. Simulation results demonstrate that the proposed algorithm reconstructs 3-D mesh surfaces faithfully even in severe error prone environments