nils-herrmann/clean_scirepeval huggingface dataset:
Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.
Secure SIFT-based sparse representation for image copy detection and recognition

In this paper, we formulate the problems of image copy detection and image recognition in terms of sparse representation. To achieve robustness, security, and efficient storage of image features, we propose to extract compact local feature descriptors via constructing the basis of the SIFT-based feature vectors extracted from the secure SIFT domain of an image. Image copy detection can be efficiently accomplished based on the sparse representations and reconstruction errors of the features extracted from an image possibly manipulated by signal processing or geometric attacks. For image recognition, we show that the features of a query image can be represented as sparse linear combinations of the features extracted from the training images belonging to the same cluster. Hence, image recognition can also be cast as a sparse representation problem. Then, we formulate our sparse representation problem as an l1-minimization problem. Promising results regarding image copy detection and recognition have been verified, respectively, through the simulations conducted on several content-preserving attacks defined in the Stirmark benchmark and Caltech-101 dataset.
Cross-camera complementary vehicle matching via feature expandsion for video forensics

In this paper, we will investigate a more challenging vehicle matching problem. The problem is formulated as invariant image feature matching among opposite viewpoints of cameras, i.e. complementary object matching. For example, a front vehicle object may be given as a query to retrieve a rear vehicle object of the same vehicle. To solve the complementary object matching, invariant image feature is first extracted based on ASIFT (affine and scale-invariant feature transform) for each detected vehicle in a camera network. Then, the ASIFT feature is expanded via a special vehicle database. As a result, cross-camera vehicle matching with the help of complementary part can be greatly improved. Experimental results demonstrate the effectiveness of the proposed algorithm and the feasibility to video forensics applications.
An error resilient coding scheme for H.264 video transmission based on data embedding

For entropy-coded H.264 video frames, a transmission error in a codeword will not only affect the underlying codeword but also may affect subsequent codewords, resulting in a great degradation of the received video frames. In this study, an error resilient coding scheme for H.264 video transmission is proposed. At the encoder, for an H.264 I frame, the important data for each macroblock (MB) are extracted and embedded into the next frame by the proposed MB-interleaving slice-based data embedding scheme for I frames. For an H.264 P frame, two types of important data for each MB are extracted and embedded into the next frame by the proposed MB-interleaving slice-based data embedding scheme for P frames. At the decoder, if the important data for a corrupted MB can be correctly extracted, the extracted important data for the corrupted MB will facilitate the employed error concealment scheme to conceal the corrupted MB; otherwise, the employed error concealment scheme is used to conceal the corrupted MB. Based on the simulation results obtained in this study, the proposed scheme can recover high-quality H.264 video frames from the corresponding corrupted video frames up to a video packet loss rate of 20%.
Visual depth guided image rain streaks removal via sparse coding

Rain removal from an image is a challenging problem since no motion information can be obtained from successive images. In this work, an input image is first decomposed into low-frequency part and high-frequency part by using guided image filter. So that the rain streaks would be in the high-frequency part with non-rain textures, and then the high-frequency part is decomposed into a “rain component” and a “non-rain component” by performing dictionary learning and sparse coding. To separate rain streaks from high-frequency part, a hybrid feature set is exploited which includes histogram of gradient (HoG) and difference of depth (DoD). With the hybrid feature set applied, most rain streaks can be removed; meanwhile, non-rain components can be enhanced. Compared with the state-of-the-art method , our proposed approach shows that not only the rain components can be removed more effectively, but also the visual quality of restored images can be improved.
Comparative study of leaf image recognition with a novel learning-based approach

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.
Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image

A highly compressed image is usually not only of low resolution, but also suffers from compression artifacts (blocking artifact is treated as an example in this paper). Directly performing image super-resolution (SR) to a highly compressed image would also simultaneously magnify the blocking artifacts, resulting in an unpleasing visual experience. In this paper, we propose a novel learning-based framework to achieve joint single-image SR and deblocking for a highly-compressed image. We argue that individually performing deblocking and SR (i.e., deblocking followed by SR, or SR followed by deblocking) on a highly compressed image usually cannot achieve a satisfactory visual quality. In our method, we propose to learn image sparse representations for modeling the relationship between low- and high-resolution image patches in terms of the learned dictionaries for image patches with and without blocking artifacts, respectively . As a result, image SR and deblocking can be simultaneously achieved via sparse representation and morphological component analysis (MCA)-based image decomposition. Experimental results demonstrate the efficacy of the proposed algorithm.
Self-learning-based rain streak removal for image/video

Rain removal from an image/video is a challenging problem and has been recently investigated extensively. In our previous work, we have proposed the first single-image-based rain streak removal framework via properly formulating it as an image decomposition problem based on morphological component analysis (MCA) solved by performing dictionary learning and sparse coding. However, in this previous work, the dictionary learning process cannot be fully automatic, where the two dictionaries used for rain removal were selected heuristically or by human intervention. In this paper, we extend our previous work to propose an automatic self-learning-based rain streak removal framework for single image. We propose to automatically self-learn the two dictionaries used for rain removal without additional information or any assumption. We then extend our single-image-based method to video-based rain removal in a static scene by exploiting the temporal information of successive frames and reusing the dictionaries learned by the former frame(s) in a video while maintaining the temporal consistency of the video. As a result, the rain component can be successfully removed from the image/video while preserving most original details. Experimental results demonstrate the efficacy of the proposed algorithm.
Context-Aware Single Image Rain Removal

Rain removal from a single image is one of the challenging image denoising problems. In this paper, we present a learning-based framework for single image rain removal, which focuses on the learning of context information from an input image, and thus the rain patterns present in it can be automatically identified and removed. We approach the single image rain removal problem as the integration of image decomposition and self-learning processes. More precisely, our method first performs context-constrained image segmentation on the input image, and we learn dictionaries for the high-frequency components in different context categories via sparse coding for reconstruction purposes. For image regions with rain streaks, dictionaries of distinct context categories will share common atoms which correspond to the rain patterns. By utilizing PCA and SVM classifiers on the learned dictionaries, our framework aims at automatically identifying the common rain patterns present in them, and thus we can remove rain streaks as particular high-frequency components from the input image. Different from prior works on rain removal from images/videos which require image priors or training image data from multiple frames, our proposed self-learning approach only requires the input image itself, which would save much pre-training effort. Experimental results demonstrate the subjective and objective visual quality improvement with our proposed method.
Learning sparse representation for leaf image recognition

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.
Video super-resolution via dynamic texture synthesis

This paper addresses the problem of hallucinating the missing high-resolution (HR) details of a low-resolution (LR) video while maintaining the temporal coherence of the hallucinated HR details by using dynamic texture synthesis (DTS). Most existing multi-frame-based video super-resolution (SR) methods suffer from the problem of limited reconstructed visual quality due to inaccurate sub-pixel motion estimation between frames in a LR video. To achieve high-quality reconstruction of HR details for a LR video, we propose a texture-synthesis-based video super-resolution method, in which a novel DTS scheme is proposed to render the reconstructed HR details in a time coherent way, so as to effectively address the temporal incoherence problem caused by traditional texture synthesis based image SR methods. To further reduce the complexity of the proposed method, our method only performs the DTS-based SR on a selected set of key-frames, while the HR details of the remaining non-key-frames are simply predicted using the bi-directional overlapped block motion compensation. Experimental results demonstrate that the proposed method achieves significant subjective and objective quality improvement over state-of-the-art video SR methods.
Image retrieval using local color features

In this study, an image indexing and retrieval approach, using local color features and a modified weighted color distortion measure, is proposed. In the proposed approach, each image is segmented into several regions by a watershed segmentation algorithm, and then the mutual relationships between connected color regions are extracted as local color features. That is, an image can be represented as a set of connected (adjacent) color regions and the mutual relationships between connected color regions. In the image retrieval stage, the similarity between a query image and a target image will contain not only direct region correspondence but also the mutual relationships between connected color regions. A modified weighted color distortion measure is proposed, in which different color elements in the YUV color space receive different weights so that the illumination variation effect is greatly reduced.
Dictionary learning-based distributed compressive video sensing

We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is, most acquired raw video data will be discarded in the (possibly) complex compression stage. In this paper, we propose a dictionary learning-based distributed compressive video sensing (DCVS) framework to “directly” acquire compressed video data. Embedded in the compressive sensing (CS)-based single-pixel camera architecture, DCVS can compressively sense each video frame in a distributed manner. At DCVS decoder, video reconstruction can be formulated as an l1-minimization problem via solving the sparse coefficients with respect to some basis functions. We investigate adaptive dictionary/basis learning for each frame based on the training samples extracted from previous reconstructed neighboring frames and argue that much better basis can be obtained to represent the frame, compared to fixed basis-based representation and recent popular “CS-based DVC” approaches without relying on dictionary learning.
Image copy detection via dictionary learning and sparse coding

In this paper, a new robust image hashing scheme for image authentication via dictionary-based sparse representation of images is proposed. For image hash extraction, we create an over-complete dictionary containing the prototype image atoms to build the hash for an image, where each image patch can be represented by sparse linear combinations of these atoms. The major contribution is to formulate the image authentication problem as a sparse coding problem. Based on the energy distribution of nonzero coefficients of the sparse representation for an image, the authentication of the image can be achieved. Simulation results have shown the proposed scheme is robust to several content-preserving image attacks defined in StirMark.
An error resilient image camouflaging scheme for secret image transmission

In the proposed error resilient image camouflaging scheme, a secret image is camouflaged at the sender into multiple virtual JPEG-2000 images, resulting in multiple cipher-images. Then, instead of the secret image, only the cipher-images are transmitted to the receiver. Because both the secret and cipher-images are significant, the cipher-images do not pique the interest of illegal users who may try to get the secret image. At the receiver, if all the cipher-images are completely received, the secret image can be recovered from the cipher-images, whereas if the cipher-images are not completely received, the secret image cannot be recovered. Additionally, because the transmitted JPEG-2000 cipher-images may be corrupted by transmission errors, to enhance the degree of transmission reliability, three proposed techniques, namely, side-match vector quantization, round-robin like data embedding, and duplicate data embedding, are employed to provide error resilience for robust image transmission.
Wyner-Ziv Video Coding with Coding Mode-Aided Motion Compensation

In distributed video coding, individual frames are encoded independently but decoded conditionally. The Wyner-Ziv theorem-based source coding with side information only available at the decoder states that an intraframe encoder with interframe decoder system can approach the efficiency of a conventional interframe encoder and decoder system. In this paper, a new block discrete cosine transform (DCT)-based Wyner-Ziv video codec with coding mode-aided motion compensation at the decoder is proposed. The key is that for each block, a large amount of candidate blocks are evaluated based on some criteria derived from Reed-Solomon (RS) decoding and best neighborhood matching to find the best candidate block as the side information. Another characteristic is that error correction code (ECC) decoding is proposed to participate in generating side information. Compared with some known Wyner-Ziv video coding systems, in the proposed video codec, no extra information should be transmitted and feedback channel is unnecessary. The coding performance of our method has been verified through simulations.
Context-aware single image super-resolution using locality-constrained group sparse representation

We present a novel learning-based method for single image super-resolution (SR). Given a single input low-resolution (LR) image (and its image pyramid), we propose to learn context-specific image sparse representation, which aims at modeling the relationship between low and high-resolution image patch pairs of different context categories in terms of the learned dictionaries. To predict the SR image, we derive the context-specific sparse representation of each image patch in the LR input with additional locality and group sparsity constraints. While the locality constraint searches for the most similar image patches and uses the corresponding highresolution outputs for SR, the group sparsity constraint allows us to utilize the information from most relevant context categories for predicting the final SR output. Experimental results show the proposed method is able to quantitatively and qualitatively achieve state-of-the-art performance.
Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior

Images/videos of outdoor scenes are usually degraded by the turbid medium in the atmosphere. In this paper, a novel single image-based dehazing framework is proposed to remove haze effects from image/video, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze imaging model, we propose to accurately estimate the atmospheric light via haze density analysis. We can then accurately estimate the transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free image can be recovered with lower computational complexity, which can be naturally extended to video dehazing.
Single-frame-based rain removal via image decomposition

Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image has been rarely studied in the literature, where no temporal information among successive images can be exploited, making it more challenging. In this paper, to the best of our knowledge, we are among the first to propose a single-frame-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis (MCA). Instead of directly applying conventional image decomposition technique, we first decompose an image into the low-frequency and high-frequency parts using a bilateral filter. The high-frequency part is then decomposed into “rain component” and “non-rain component” via performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.
Temporally Coherent Superresolution of Textured Video via Dynamic Texture Synthesis

This paper addresses the problem of hallucinating the missing high-resolution (HR) details of a low-resolution (LR) video while maintaining the temporal coherence of the reconstructed HR details using dynamic texture synthesis (DTS). Most existing multiframe-based video superresolution (SR) methods suffer from the problem of limited reconstructed visual quality due to inaccurate subpixel motion estimation between frames in an LR video. To achieve high-quality reconstruction of HR details for an LR video, we propose a texture-synthesis (TS)-based video SR method, in which a novel DTS scheme is proposed to render the reconstructed HR details in a temporally coherent way, which effectively addresses the temporal incoherence problem caused by traditional TS-based image SR methods. To further reduce the complexity of the proposed method, our method only performs the TS-based SR on a set of key frames, while the HR details of the remaining nonkey frames are simply predicted using the bidirectional overlapped block motion compensation. After all frames are upscaled, the proposed DTS-SR is applied to maintain the temporal coherence in the HR video. Experimental results demonstrate that the proposed method achieves significant subjective and objective visual quality improvement over state-of-the-art video SR methods.