The safety and serviceability of civil infrastructures have to be ensured either as part of a periodic inspection program or immediately following a given hazardous event. The use of digital imaging techniques to identify the deformed or deteriorated surfaces of structures is a substantial area of research and aims to investigate a number of unknown parameters, including damage quantification and condition rating. This manuscript illustrates the integration of previously developed fuzzy logic-based decision-making tools with the currently developed image processing algorithm to quantify the damage for the condition rating of civil infrastructures. The proposed integrated framework exploits visual specifics of different elements of the infrastructure to perform automated evaluation of structural anomalies such as cracks and surface degradation. Two different image segmentation tools, (1) bottom hat transform and (2) hue, saturation, color (HSV) thresholding, are applied to identify the surface defects. The developed image processing software is used with the fuzzy set framework proposed in the previous research to gauge the damage indices due to various deterioration types like corrosion, alkali aggregate reaction, freeze-thaw attack, sulfate attack, acid attack or loading, fatigue, shrinkage, and honeycombing. Case studies of a long-span bridge and a warehouse building are illustrated for concept validation. The refined comprehensive method is presented as a graphical user interface (GUI) to facilitate the real-time condition assessment of civil infrastructures.

Digital halftoning deals with transforming a gray or color image into its binary version which is useful in printing applications. Dot diffusion is one of the prominent halftone methods which can yield superior image quality with parallel processing capabilities. In this paper, a rapid watermarking algorithm is proposed for dot-diffusion halftone images using adaptive class-matrix selection and modified error diffusion kernels. To process the image using an adaptive class matrix, the processing order of the class matrix is reversed and transposed, and for error diffusion the coefficients are replaced with different weights. For decoding, an effective strategy is proposed based on a correlation analysis and halftone statistics. The proposed strategy can successfully embed and decode the binary watermark from a single dot-diffused halftone image. From the experimental results, the proposed method is found to be effective in terms of good decoding accuracy, imperceptibility and robustness against various printed distortions.

This study details the development of a lightweight and high performance model, targeting real-time object detection. Several designed features were integrated into the proposed framework to accomplish a light weight, rapid execution, and optimal performance in object detection. Foremost, a sparse and lightweight structure was chosen as the network’s backbone, and feature fusion was performed using modified feature pyramid networks. Recent learning strategies in data augmentation, mixed precision training, and network sparsity were incorporated to substantially enhance the generalization for the lightweight model and boost the detection accuracy. Moreover, knowledge distillation was applied to tackle dropping issues, and a student–teacher learning mechanism was also integrated to ensure the best performance. The model was comprehensively tested using the MS-COCO 2017 dataset, and the experimental results clearly demonstrated that the proposed model could obtain a high detection performance in comparison to state-of-the-art methods, and required minimal computational resources, making it feasible for many real-time deployments.

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Mitochondria are the organelles that generate energy for the cells. Many studies have suggested that mitochondrial dysfunction or impairment may be related to cancer and other neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Therefore, morphologically detailed alterations in mitochondria and 3D reconstruction of mitochondria are highly demanded research problems in the performance of clinical diagnosis. Nevertheless, manual mitochondria segmentation over 3D electron microscopy volumes is not a trivial task. This study proposes a two-stage cascaded CNN architecture to achieve automated 3D mitochondria segmentation, combining the merits of top-down and bottom-up approaches. For top-down approaches, the segmentation is conducted on objects’ localization so that the delineations of objects’ contours can be more precise. However, the combinations of 2D segmentation from the top-down approaches are inadequate to perform proper 3D segmentation without the information on connectivity among frames. On the other hand, the bottom-up approach finds coherent groups of pixels and takes the information of 3D connectivity into account in segmentation to avoid the drawbacks of the 2D top-down approach. However, many small areas that share similar pixel properties with mitochondria become false positives due to insufficient information on objects’ localization. In the proposed method, the detection of mitochondria is carried out with multi-slice fusion in the first stage, forming the segmentation cues. Subsequently, the second stage is to perform 3D CNN segmentation that learns the pixel properties and the information of 3D connectivity under the supervision of cues from the detection stage. Experimental results show that the proposed structure alleviates the problems in both the top-down and bottom-up approaches, which significantly accomplishes better performance in segmentation and expedites clinical analysis.