Given the unwieldy glut of information in online health question-answer (Q&A) service, it is essential to understand what constitutes helpful answers in the medical domain. Despite the fact that studies have examined the impacts of answer content factors on answer helpfulness, there are two gaps that need further analysis. First, the empirical results of the existing relevant studies on the effect of answer emotion are inconsistent. Second, prior studies only have examined the independent impacts of answer content factors and question content cues on answer helpfulness. To fill this gap, a research model reflecting the impacts of emotional content and question-answer congruence on answer helpfulness was developed and empirically examined. Our empirical analyses confirm that emotional content and answer helpfulness are related to one another in the form of an inverted U-shape and indicate that two types of question-answer congruence (emotional intensity congruence and linguistic style matching) positively affect answer helpfulness. Theoretical and practical implications are discussed.

Despite being able to predict accurate user-item interactions, recommender systems also lead to homogeneous information flow, resulting in negative online experiences. However, users’ coping processes for this stressful event remain basically unclear. Drawing on coping theory, this study examines how users appraise two stressors (information narrowing and information redundancy) in homogenous information flow, especially in different user experiences, and thus adopt algorithmic resistance and avoidance coping strategies. Data (N = 407) were collected based on a survey. The empirical results indicate that information narrowing and information redundancy are significantly and negatively related to challenge appraisal, and exert significant and positive effects on threat appraisal. The moderating effect denotes that user experience strengthens the relationship between information narrowing and information redundancy to threat appraisal. Threat appraisal further triggers algorithmic resistance and avoidance of users. The paper provides several implications for theory and research by revealing the mechanism through which recommender systems users cope with homogeneous information flow. Our findings also provide new insights for designers of recommender systems platforms.

The abundance of user ideas in the open innovation community poses a challenge of information overload for enterprises, making it difficult to effectively adopt ideas for product innovation. Existing studies primarily focus on evaluating the usefulness of user ideas, while overlooking the actual adoption outcomes of these ideas. Based on the information adoption model, this paper proposes a user idea adoption model, which integrates the two stages of idea collection and idea application. The authors creatively discuss the factors influencing the process from “perceived usefulness” to “adoption” of a user idea, and explore the complete mechanism of user idea adoption for product innovation. The results show that the information quality and source credibility of user ideas affect the perceived usefulness. The degree of user demand, innovation complexity, and competition intensity have moderating effects on the relationship between the perceived usefulness of user ideas and the results of user ideas adoption.

Common medical image segmentation tasks require large training datasets with pixel-level annotations which are very expensive and time-consuming to prepare. To overcome such limitation and achieve the desired segmentation accuracy, a novel Weakly-Interactive-Mixed Learning (WIML) framework is proposed by efficiently using weak labels. On one hand, utilize weak labels to reduce annotation time for high-quality strong labels by designing a Weakly-Interactive Annotation (WIA) part of the WIML which prudently introduces interactive learning into the weakly-supervised segmentation strategy. On the other hand, utilize weak labels and very few strong labels to achieve desired segmentation accuracy by designing a Mixed-Supervised Learning (MSL) part of the WIML which can boost the segmentation accuracy by providing strong prior knowledge during training. Besides, a multi-task Full-Parameter-Sharing Network (FPSNet) is proposed to better implement this framework. Specifically, to further reduce annotation time, attention modules (scSE) are integrated into FPSNet to improve the class activation map (CAM) performance for the first time. To further improve segmentation accuracy, a Full-Parameter-Sharing (FPS) strategy is designed in FPSNet to alleviate the overfitting of the segmentation task supervised by very few strong labels. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, and experiments demonstrate that the proposed method WIML-FPSNet outperforms several state-of-the-art segmentation methods with minimal annotation efforts.

Abstract The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single‐image haze removal based on dark channel prior with weighted guided coefficient and union self‐adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k ‐means clustering method is adopted to calibrate the original image into bright and non‐bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected‐component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.

Images captured in the sand–dust weather often suffer from serious colour cast and poor contrast, and this has serious implications for outdoor computer vision systems. To address these problems, a normalised gamma transformation‐based contrast‐limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand–dust image enhancement is proposed in this study. This method consists of image contrast enhancement and image colour correction. To avoid producing new colour deviation, the input sand–dust images are first transformed from red, green, and blue colour space into Lab colour space. Then, the contrast of the lightness component (L channel) of the sand–dust image is enhanced using CLAHE. To avoid unbalanced contrast, as well as to reduce the overincreased brightness caused by CLAHE, a normalised gamma correction function is introduced to CLAHE. After that, the a and b chromatic components are recovered by a grey‐world‐based colour correction method. Experiments on real sand–dust images demonstrate that the proposed method can obtain the highest percentage of new visible edges for all testing images. The contrast restoration exhibits good colour fidelity and proper brightness.

Rain image enhancement is important for outdoor computer vision applications. In this study, the authors propose a multi‐stage filtering method for single rainy image enhancement. It is based on their new rainy image model, and consists of two main operations: rain streaks removal and rain fog removal. For rain streaks removal, based on one key observation that the low‐pass version of a rainy image and that of a non‐rainy image of the same scene are almost the same after appropriate low‐pass filtering, they remove rain streaks from rainy images by decomposing an input rainy image (or a rainy component image) into the low‐frequency (LF) part and the high‐frequency (HF) part via an LF smooth filter, i.e. the traditional Gaussian filter with a simple subtraction operation in multiple different stages. After rain streaks removal, dark channel prior‐based method was employed for rain fog removal. Experimental results show that the proposed algorithm generated comparable outputs with most of the state‐of‐the‐art algorithms with low computation cost.

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.

This article addresses the connected-component labeling problem which consists in assigning a unique label to all pixels of each connected component (i.e., each object) in a binary image. Connected-component labeling is indispensable for distinguishing different objects in a binary image, and prerequisite for image analysis and object recognition in the image. Therefore, connected-component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. In this article, we review state-of-the-art connected-component labeling algorithms presented in the last decade, explain the main strategies and algorithms, present their pseudo codes, and give experimental results in order to bring order of the algorithms. Moreover, we will also discuss parallel implementation and hardware implementation of connected-component labeling algorithms, extension for n-D images, and try to indicate future work on the connected component labeling problem.

Labeling connected components and calculating the Euler number in a binary image are two fundamental processes for computer vision and pattern recognition. This paper presents an ingenious method for identifying a hole in a binary image in the first scan of connected-component labeling. Our algorithm can perform connected component labeling and Euler number computing simultaneously, and it can also calculate the connected component (object) number and the hole number efficiently. The additional cost for calculating the hole number is only O(H) , where H is the hole number in the image. Our algorithm can be implemented almost in the same way as a conventional equivalent-label-set-based connected-component labeling algorithm. We prove the correctness of our algorithm and use experimental results for various kinds of images to demonstrate the power of our algorithm.

This paper proposes a new approach to label-equivalence-based two-scan connected-component labeling. We use two strategies to reduce repeated checking-pixel work for labeling. The first is that instead of scanning image lines one by one and processing pixels one by one as in most conventional two-scan labeling algorithms, we scan image lines alternate lines, and process pixels two by two. The second is that by considering the transition of the configuration of pixels in the mask, we utilize the information detected in processing the last two pixels as much as possible for processing the current two pixels. With our method, any pixel checked in the mask when processing the current two pixels will not be checked again when the next two pixels are processed; thus, the efficiency of labeling can be improved. Experimental results demonstrated that our method was more efficient than all conventional labeling algorithms.

In this paper, we proposed a fuzzy approach to segmenting objects using spatial location information from depth image. The optical and location information are combined by proposed fuzzy rules tale which base on K-means clustering. The segmentation of our framework is an efficient graph-based image segmentation algorithm. This framework combined obvious changes in color and physical location to segment reality scenes into uniform regions. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data.

In this paper, a signaling-embedded preamble design is proposed for the orthogonal frequency division multiplexing (OFDM) system with transmit diversity. Utilizing the transmit diversity, two identical training sequences (TSs) are embedded in two OFDM symbols separately in discrete Fourier transform (DFT) domain and transmitted by two different antennas simultaneously. The relative distance between the two TSs in DFT domain could be varied in order to indicate several bits of signaling information for the receiver to acquire the transmission parameters signaling (TPS) quickly. Furthermore, the preamble can also be used for frame synchronization, carrier frequency offset (CFO) estimation, and coarse channel estimation. The computational simulations were carried out under both additive white Gaussian noise (AWGN) and multipath channels, and the results demonstrated the design works accurately on the preamble detection, CFO estimation and signaling demodulation under both static and dynamic channels.

A novel preamble design is proposed for orthogonal frequency division multiplexing systems, which exploits the variable distance between a pair of training sequences for the transmission parameter signalling. Compared to the existing P1-symbol based preamble for the second generation digital terrestrial television broadcasting standard, the proposed design maintains the high performance and robustness in timing and carrier frequency offset estimation while significantly reducing the signalling detection complexity. Simulation results demonstrate that the proposed novel preamble achieves a better signalling detection performance than the P1 symbol design.

In order to accommodate the different applications in wireless transmission environment, communication systems usually provide several configurable transmission modes. Therefore, transmission parameter signalling (TPS) is critical for the receiver to recognize the signal transmission modes. In this paper, the pseudo-noise (PN) training sequence with embedded signalling in the time domain synchronous block transmission (TDS-BT) system is investigated, where the TPS could be conveyed through the transformation of the PN sequences in the frame head. The proposed signalling-embedded PN sequences could provide precise synchronization as well as robust signalling performance without extra transmission resource consumption. Computer simulations are also presented to verify the effectiveness of our proposed methods.

Whenever one wants to distinguish, recognize, and/or measure objects (connected components) in binary images, labeling is required. This paper presents two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. One is voxel based and the other is run based. For the voxel-based one, we present an efficient method of deciding the order for checking voxels in the mask. For the run-based one, instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our voxel-based algorithm is efficient for 3-D binary images with complicated connected components, that our run-based one is efficient for those with simple connected components, and that both are much more efficient than conventional 3-D labeling algorithms.

This paper presents a run- and label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major differences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalence-based algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the first scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the first scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a final label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly efficient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components efficiently.

We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.

This paper presents a fast linear-time two-scan algorithm for labeling connected components in binary images. In the first scan, provisional labels are assigned to object pixels in the same way as do most conventional labeling algorithms. To improve efficiency, we use corresponding equivalent label sets and a representative label table for resolving label equivalences. When the first scan is finished, all provisional labels belonging to each connected component in a given image are combined in the corresponding equivalent label set, and they are assigned a unique representative label with the representative label table. During the second scan, by use of the completed representative label table, all provisional labels belonging to each connected component are replaced by their representative label. Our algorithm is very simple in principle, and is easy to implement. Experimental results demonstrated that the efficiency of our algorithm is superior to that of other labeling algorithms.

Some research has been conducted on applying coloration of painterly art to a photographic image. Most of the proposed methods use some absolute criteria, e.g., the nearest distance in CIEL*a*b* color space, converting a color to another uniquely. However, we think that basic color transfer depends on the subjective feelings and sensitivities of users themselves. We describe an algorithm where every user can produce an original color-transformed image resulting from two inputs, which are a target photograph and a reference painting. Our algorithm is based on interactive evolutionary computation (IEC) and can produce a variety of color-transformed images semi-automatically. Some color-transformed examples similar to reference paintings are illustrated, and the searching efficiency of our method is discussed.

In this paper, three refinements on I-SATCHMO are presented. I-SATCHMO is an application of the backjumping strategy for SATCHMO, which is a model generation theorem prover and is formalized as positive unit hyperresolution tableaux, to pruning away redundant branches. However, I-SATCHMO does not completely implement backjumping. Since marking an atom as useful whenever it contributes to a forward chaining reasoning, I-SATCHMO suffers from two problems. One is that the atoms that only contribute to irrelevant forward chaining reasoning are marked as ‘useful’. The other is that the atoms that are only useful for the sibling branches of the branch under consideration are taken as ‘useful’ for the branch. In both cases, I-SATCHMO might take some irrelevant violated clauses as ‘relevant’ and therefore use them to expand the search space. Moreover, not utilizing the partial reasoning results derivable during reasoning, I-SATCHMO might make repeated reasoning. Addressing these problems, our extension, called R-SATCHMO, refines I-SATCHMO in three ways: (1) only making the atoms that contribute to relevant forward chaining reasoning as useful; (2) it distinguishes between the set of useful atoms in different branches of a proof tree; (3) it summarizes the derived refutations as nogoods and utilizes them to avoid repeated reasoning. Hence, R-SATCHMO always searches a subspace of that which I-SATCHMO does. R-SATCHMO is optimal in the sense that during reasoning no same complete subtree is constructed in a proof tree. Moreover, R-SATCHMO can be used to generate minimal unsatisfiable subsets of an unsatisfiable set of clauses, and the strategies proposed in R-SATCHMO are applicable to minimal model generation procedures. We describe our refinements, present our implementation, prove the correctness, provide examples to demonstrate the power of our refinements, and discuss the application to minimal model generation.

Thinning is a fundamental concept of computer processing of line images, and it plays an important role in pattern recognition. Most existing thinning algorithms have the problem to produce distorted skeletons. Some methods to correct the distorted skeleton have been proposed, but non of them produce complete results. In order to solve the problem, we propose an improved thinning algorithm which is a skeleton revision algorithm using maximal circles (SRAMCs). SRAMCs is a hybrid algorithm and the processing route is divided into three stages: 1) thinning using Hilditch algorithm; 2) removal of spurious pixels using maximal circle; and 3) skeleton reconstruction. The results of experiments indicates that SRAMCs can be useful for detection of lines and curves in digitized line images.