Inspecting X-ray images is an essential aspect of medical diagnosis. However, due to an X-ray's low contrast and low dynamic range, important aspects such as organs, bones, and nodules become difficult to identify. Hence, contrast adjustment is critical, especially because of its ability to enhance the details in both bright and dark regions. For X-ray image enhancement, we therefore propose a new concept based on component attenuation. Notably, we assumed an X-ray image could be decomposed into tissue components and important details. Since tissues may not be the major primary focus of an X-ray, we proposed enhancing the visual contrast by adaptive tissue attenuation and dynamic range stretching. Via component decomposition and tissue attenuation, a parametric adjustment model was deduced to generate many enhanced images at once. Finally, an ensemble framework was proposed for fusing these enhanced images and producing a high-contrast output in both bright and dark regions. We have used measurement metrics to evaluate our system and achieved promising scores in each. An online testing system was also built for subjective evaluation. Moreover, we applied our system to an X-ray data set provided by the Japanese Society of Radiological Technology to help with nodule detection. The experimental results of which demonstrated the effectiveness of our method.

Deep learning methods, especially CNNs, have achieved many promising results in a wide range of computer vision applications. However, few studies focused on designing suitable deep learning methods for parking space status inference. As we have known, it is challenging to detect parking spaces in an outdoor environment due to dynamic lighting variations, weather changes, and perspective distortion. By off-the-shelf CNNs, lighting variations might be handled well. However, to realize a practical and robust inference system, we also need to address troublesome problems, such as parking displacements, non-unified car sizes, inter-object occlusion, and perspective distortion. These problems may become even challenging if also considering the difference of space sizes. To overcome the problems, we proposed a custom-tailored deep convolutional and contrastive network with three contributions. First, we introduced a Siamese architecture to learn the contrastive and robust feature descriptor. This helps to reduce the effects owing to the variety of inter-object occlusion. Second, we integrated a convolutional Spatial Transformer Network (STN) to adaptively transform a 3-space input patch according to vehicle sizes and parking displacement. STN also helps to overcome the perspective distortion problem. Third, a multi-task loss function was designed to train the network by simultaneously considering the accuracy of inferring the status of the target space and the semantic smoothness of high-level features. Thereby, the errors caused by inter-object occlusion could be alleviated. To verify the proposed network, we visualized the learned features and analyzed their functionality. Experiments and evaluations have shown the robustness of our system in parking status inference. The real-time system currently running in public parking lots also demonstrates the effectiveness of the proposed deep network.

3D point-cloud upsampling, a crucial perceptual module to help us understand the complex scene and object, aims to generate a high-resolution point cloud given a sparse point set. While considerable attention has been paid to single object point-cloud upsampling, literature on upsampling complex scenes has emerged slowly. Remarkably, few related works target LiDAR-based point clouds, which inherently have a density imbalance problem over distance. This paper proposed LiUpNet, a LiDAR-based point cloud upsampling network. Given a sparse and imbalanced point cloud of a natural and complex scene, LiUpNet extracts robust regional features and generates a uniformly distributed dense point cloud that preserves the fine-grained structural architecture. Specifically, in LiUpNet, an attentive and transformer-based feature extractor is applied to learn the detailed regional representation to model the underlying complex local structure. Also, a novel densityinvariant feature consistency loss is introduced to improve the robustness of the learned features against the sparsity changes. Finally, given the regional representations, we append a manifold-based upsampler to super-resolve the regional point clouds up to different scales according to the sparsity and implicitly regresses non-linear 3D scene surfaces. A more uniformly distributed and denser scene point cloud is then achieved with the scheme of dynamic regional upsampling rates. The experimental results on single-object and scene point-cloud upsampling show that LiUpNet outperforms related works qualitatively and quantitatively. Additionally, we demonstrate that the enhanced scene point clouds can efficiently improve the performance of downstream tasks such as point-cloud-based 3D object detection and depth completion.

In a practical environment, the viewing angle and height of a video surveillance camera are uncontrollable. This may cause severe interobject occlusion and complicate the detection problem. In this paper, we proposed a novel inference framework with multiple layers for vacant parking space detection. The framework consists of an image layer, a patch layer, a space layer, and a lot layer. In the image layer, image patches were selected based on the 3D parking lot structure. We found that the occlusion pattern within each patch reveals cues of the parking status. Thus, our system extracted lighting-invariant features of patches and trained weak classifiers for the recognition of the occlusion pattern in the patch layer. The outputs of the classifiers, presenting the types of interobject occlusion, were treated as the mid-level features and inputted to the space layer. Next, a boosted space classifier was trained to recognize the mid-level features and output the status of a three-space unit in a probability fashion. In the lot layer, we regarded the local status decision of three-space units as high-level evidence and proposed a Markov random field to refine the parking status. In addition, we extended the framework to bridge multiple cameras and integrate the complementary information for vacant space detection. Our results show that the proposed framework can overcome the interobject occlusion and achieve better status inference in many environmental variations and under different weather conditions. We also presented a real-time system to demonstrate the computing efficiency and the system robustness.

This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism (PE) lesion areas in computed tomography pulmonary angiogram (CTPA) images. In the current study, all of the PE CTPA image segmentation methods were trained by supervised learning. However, when CTPA images come from different hospitals, the supervised learning models need to be retrained and the images need to be relabeled. Therefore, this study proposed a semi-supervised learning method to make the model applicable to different datasets by the addition of a small number of unlabeled images. By training the model with both labeled and unlabeled images, the accuracy of unlabeled images was improved and the labeling cost was reduced. Our proposed semi-supervised segmentation model included a segmentation network and a discriminator network. We added feature information generated from the encoder of the segmentation network to the discriminator so that it could learn the similarities between the prediction label and ground truth label. The HRNet-based architecture was modified and used as the segmentation network. This HRNet-based architecture could maintain a higher resolution for convolutional operations to improve the prediction of small PE lesion areas. We used a labeled open-source dataset and an unlabeled National Cheng Kung University Hospital (NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised learning model, and the resulting mean intersection over union (mIOU), dice score, and sensitivity reached 0.3510, 0.4854, and 0.4253, respectively, on the NCKUH dataset. Then we fine-tuned and tested the model with a small number of unlabeled PE CTPA images in a dataset from China Medical University Hospital (CMUH) (IRB number: CMUH110-REC3-173). Comparing the results of our semi-supervised model with those of the supervised model, the mIOU, dice score, and sensitivity improved from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively. In conclusion, our semi-supervised model can improve the accuracy on other datasets and reduce the labor cost of labeling with the use of only a small number of unlabeled images for fine-tuning.