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One of the current issues for picture archiving and communication systems (PACS) is extending retrieval technologies to deal with multimedia information. This is particularly important for medical applications that assist in diagnostic processes and pathology studies. Accordingly, this paper presents a new approach to content-based image retrieval (CBIR) for a clinical ultrasound image database (DB). The proposed algorithm consists of two stages so as to maximize the retrieval efficiency. In the first stage, a coarse retrieval is performed using the statistical characteristics of the wavelet coefficients that narrow the search by eliminating up to 70% of the total DB images. In the second stage, a fine retrieval is carried out using the Legendre moment of the global histogram pdf on the reduced image set preretrieved by the coarse retrieval. When tested on an abdominal ultrasound image DB and compared with various other methods, the proposed algorithm gave promising results for applying CBIR to clinical ultrasound images.

With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.

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