Semi-supervised learning leverages insights from unlabeled data to enhance generalizability of the model, thereby decreasing the dependence on extensive labeled datasets. Most existing methods overly focus on local representations while neglecting the learning of global structures. On the one hand, given that labeled and unlabeled images are presumed to originate from the same distribution, it is probable that similar regional features observed in both types of images correspond to the same label. Current label propagation techniques, which predominantly propagate label information through the construction of graph structures or similarity matrices, heavily depend on localized information and are prone to converge to local optima. In contrast, optimal transport considers the entire distribution. This facilitates more comprehensive and efficient label propagation. On the other hand, current consistency regularization-based methods focus on the local view, we believe learning from a global geometric view may capture more information. Geometric moment information of the sample itself can constrain the overall geometric structure. Inspired by these observations, this paper introduces a semi-supervised medical image segmentation framework that integrates optimal transport and central moment consistency regularization (OTCMC) from a global perspective. Firstly, we pass label information from labeled data to unlabeled data by optimal transport. Secondly, we incorporate central moment consistency regularization to focus the network on the geometric structure of images. Our method achieves the state-of-the-art (SOTA) performance on a series of datasets, including the NIH pancreas, left atrium, brain tumor, and skin lesion dermoscopy datasets.

Although the thermal infrared remote sensing camera plays a pivotal role in Earth observation, and impacts the target detection, surface temperature inversion, and subsequent space missions significantly, the imaging quality of the camera is constrained by its optics, image sensors, and electronics during on-orbit operation. At the same time, the traditional blind recovery algorithms, which require extensive time for estimating intricate blur kernels, encounter challenges due to varying atmospheric conditions and other factors leading to dissimilar blur kernels across different observation scenes. In this context, this article introduces a rapid image recovery algorithm rooted in the concept of the invariant modulation transfer function (IMTF) specific to on-orbit cameras. The IMTF model remains stable and impervious to influences stemming from ground targets, atmospheric conditions, and orbital or environmental fluctuations, contingent upon the camera’s inherent characteristics. The extraction of the IMTF involves subjecting the transfer function’s region to a modified edge methodology, followed by image recovery through a hyper-Laplacian prior inverse convolution approach. The resolution of the inverse problem is achieved by employing an alternating minimization scheme. This method addresses the mitigation of imaging artifacts originating from the camera’s limitations. Comparative analysis against the state-of-the-art image recovery techniques establishes the competitiveness of the method proposed in this article, both in terms of recovery efficacy and operational efficiency. Substantiating this, experimental validation using in-orbit thermal infrared remote sensing images reveals a notable improvement in the average gradient (AG) (by a factor of 3.2), edge intensity (EI) (by a factor of 2.5), and modulation transfer function (by a factor of 1.3) of the restored images. Consequently, this approach introduces a novel perspective for enhancing the restoration of in-orbit remote sensing images.

Abstract Image outpainting aims at extending the field of view of an existing image. While image inpainting has achieved great success with the deep learning technology, image outpainting still receive less attention. The main challenge is how to generate high‐quality extended images with clear texture and highly consistent semantic information. In order to solve the problem of the effect of invalid pixels on the generated image and the distance of effective pixels is too far. This paper proposes a two‐stage image outpainting method (the EA method), which consists of an edge generation stage and an edge transformation stage. In this paper, the convolutional block attention module (CBAM) is introduced into the generation network to focus on spatial and channel feature and the improved VAE‐GAN structure is used to generate the extended image for more realistic semantics. The EA method is evaluated on the CelebA, Pairs‐streetview and homemade landscapes dataset, and show that the results contain high‐quality textures as well as faithfully extend the semantics. The average PSNR, SSIM, FID index of the EA method on the three datasets is 22.7961, 0.7061, 6.8553 and show that it outperforms existing algorithms in both quantitative and qualitative analysis.

In ride matching, the search results can be personalized for a particular driver. Given a query with trip plans, it is advantageous to rank potential riders in terms of who are most appealing to the driver for increasing occupancy rates. While personalized ranking approaches such as collaborative filtering and factorization are available, they are not suitable for pooling because candidate riders are associated with different preferences, and their travel is sparsely distributed with a long tail of users for a few popular destinations. The user embedding method is a good candidate in terms of alleviating data sparsity, but it has issues such as difficulty encoding user preferences from rich information. In this study, we explore user embedding techniques for the purposes of short-term personalized rider ranking, where the aim is to present to drivers a set of potential riders who share similar itineraries with them and can be picked up on their current route. Considering trip requests, along with the preferences issued in advance, this study uses attribute representations to rank the riders based on the higher-order similarities in the participants’ itineraries in a three-step manner: (i) start with a distributed representation of the riders’ preference regarding the cost of extra distance, (ii) generate user embeddings in a heterogeneous network with the meeting points and associated waiting times, and (iii) match and rank riders for drivers depending on an attribute fusion operation by adopting a personal route and schedule. Our proposed method performs well in an offline estimation on a huge dataset from DiDi in Chengdu, China. Experimental results indicate that with the learned embeddings, we can obtain statistically significant advancements (e.g., 4.6–29.5% increase in mean reciprocal rank (MRR); 2.8–17.4% in normalized discounted cumulative gain (nDCG)) over current methods for pooling ranking. Furthermore, we implement the proposed method on our simulated pooling system. These results validate that personalized ranking can undoubtedly boost the number of trips served, and reduce the total trip distance and waiting time.

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.

In this study, the authors focus on the task of leaf segmentation under different imaging conditions (e.g. backgrounds and shadows). A new method ‐ robust random walk (RW) is proposed to propagate the prior of user's specified pixels. Specifically, they first employ RWs to take the relationship of pairwise pixels into consideration. A superpixel‐consistent constraint is added to make the edges of segmentation smooth. Owing to the effect of illumination, some parts of a leaf surface are brighter than others and it may further harm the subsequent label propagation. To address this problem, they learn a common subspace by taking into account the illumination of local and non‐local pixels. By doing so, it has good adaptability to process noise interfering and non‐uniform illumination. In addition, since RW only considers the pairwise relationship of pixels, it will be sensitive to the specified and connected pixels. Thus, they further employ a log‐likelihood ratio to predict the probability of a pixel belonging to the background and use it to guide the label propagation. Based on the proposed method, they can obtain a smoothed and robust leaf segmentation. Experimental results on unconstrained leaf images demonstrate the efficiency of their algorithm.

Plant leaf recognition is a computer vision task used to automatically recognize plant species. It is very challenging since rich plant leaf morphological variations, such as sizes, textures, shapes, venation, and so on. Most existing plant leaf methods typically normalize all plant leaf images to the same size and recognize them at one scale, resulting in unsatisfactory performances. In this letter, a multiscale fusion convolutional neural network (MSF-CNN) is proposed for plant leaf recognition at multiple scales. First, an input image is down-sampled into multiples low resolution images with a list of bilinear interpolation operations. Then, these input images with different scales are step-by-step fed into the MSF-CNN architecture to learn discriminative features at different depths. At this stage, the feature fusion between two different scales is realized by a concatenation operation, which concatenates feature maps learned on different scale images from a channel view. Along with the depth of the MSF-CNN, multiscale images are progressively handled and the corresponding features are fused. Third, the last layer of the MSF-CNN aggregates all discriminative information to obtain the final feature for predicting the plant species of the input image. Experiments show the proposed MSF-CNN method is superior to multiple state-of-the art plant leaf recognition methods on the MalayaKew Leaf dataset and the LeafSnap Plant Leaf dataset.

This paper proposes a modified two-class LDA based compound distance for similar handwritten Chinese characters discrimination. First the definition of the Intersecting Subspace (IS) between two classes and the modified between-class scatter matrix is given. Then we prove that the modified between-class scatter matrix can supply additional information. Our experiments demonstrate that the additional information can be used to discriminate points in the IS and the proposed method outperforms the previous LDA based method.

This paper proposes a Multiple Instance Learning based method for similar handwritten Chinese characters discrimination. The similar handwritten Chinese characters recognition problem is first defined as a Multiple-instance learning problem. Then the problem is solved by the AdaBoost framework. The proposed method selects some self-adapting critical regions as weak classifiers, and therefore it is more suitable for the wide variability of writing styles. Our experimental results demonstrate that the proposed method outperforms the other state-of-the-art methods.

Detecting objects in images is very important for several application domains in computer vision. This paper presents an experimental study on data transformation of the feature vector in object detection. We use the modified Pyramid of Histograms of Orientation Gradients descriptor and the SVM classifier to form an object detection model. We apply a simple transformation to the histogram features before training and testing. This transformation equals a small change in the kernel function for Support Vector Machines. This change is much quicker than the χ2kernel, but obtains better results. Experimental evaluations on the UIUC Image Database and TU Darmstadt Database show that the transformed features perform better than the raw features, and this transformation improves the linear separability of the histogram feature.