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Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

The fast scenario transfer for portrait styles has been a universal concern in the area of image processing. Conventionally, it was realized mostly by manual manipulation based on expertise experience, which costs a large amount of human labor. To fit the growing business volume in the era of Internet 4.0, it is supposed to investigate reliable autonomous workflow that generates portrait-style results via intelligent algorithms. Therefore, this paper proposes a fast scenario transfer method for portrait styles through collaborative awareness of convolutional neural network (CNN) and generative learning (GAL). On the one hand, a CNN structure is designed to make feature extraction towards the initial images, which can be used for following image generation. On the other hand, the GAL is utilized to generate new images with post-transferred scenario styles, so that the expected scenario style transfer results can be output by generating new images through long-term iterations. Especially, the loss function is designed by simultaneously considering local and global characteristics. Finally, the analysis and experimental results show that the correction process can be completed by injecting the extracted input face features into the feature maps of corresponding sizes in the generation process using a pre-trained feature extractor. Thus, the face conversion efficiency can be improved. [ABSTRACT FROM AUTHOR]

Feature extraction and matching are the basic procedures of the so-called Visual Odometer (VO), Simultaneous Localization and Mapping (SLAM) and many other image processing algorithms. Oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm are widely used since they are computationally faster. In this paper, we proposed a method to generate a value for a feature, the value is called signature. In the matching step, we only compute Hamming distances of descriptors with the same signatures. Hence, the matching time is shortened. Compared with the original ORB algorithm, features to be matched dropped 69.63% on TUM datasets and 85.7% on VGG datasets by adopting our strategy. In addition, the precision is above 85% on both VGG and TUM datasets. We design a customized hardware architecture for ORB feature extraction and matching based on the proposed method. The hardware structure is implemented on Xilinx ZCU102 evaluation board. The clock frequency is set to 150 MHz. Our Field Programmable Gate Arrays (FPGA) system achieves 193 fps on 1 2 8 0 × 7 2 0 images with 1984 features on average and 314 fps on 6 4 0 × 4 8 0 images with 700 features on average, which is more efficient compared to the state-of-the-art works. [ABSTRACT FROM AUTHOR]