KEYWORDS: Facial recognition systems, Deep learning, Image processing, RGB color model, Detection and tracking algorithms, Light sources and illumination, Data modeling, CMOS sensors, Visualization, Super resolution
In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios.
In CNN-based classification for seafloor images, the accuracy may decrease drastically in different sea areas. Therefore, we aim to improve the accuracy by utilizing the dragged environmental sound. Classification by sound includes classification by CNN using logmel images, and we can expect a complementary relationship by using classification by image and sound together. As a concrete method, we propose a robust sediment classification method using transfer learning.
In this paper, we improve the extraction method by GrabCut in order to perform highly accurate stone contour extraction from stone wall images. In previous studies, there was a problem that over-segmentation and under-segmentation occurred because the area considered to be the background characteristics could not be specified properly. As a countermeasure to this problem, in order to make the method of specifying the restrict range for the background characteristics more suitable for stone materials, we aimed to improved the extraction accuracy by using the convex hull of the area obtained by ordinal GrabCut. Good results were obtained by limiting the characteristics due to the contraction of the convex hull, but some stones had insufficient segmentation, the cause was investigated, and future measures were described.
There is an urgent need for three-dimensional analysis of cardiomyocytes using a computer to clarify the mechanism of heart disease. However, because microscopic images include cells other than cardiomyocytes, it is necessary to classify the cells before analysis. Cardiomyocytes are characterized by a relatively low volume fraction of cell nuclei in the cytoplasm compared with other cells. In this study, these features were utilized to extract cell nuclei and cytoplasm from fluorescence microscopy images of neonatal mouse hearts and to classify cardiomyocytes and other cells based on volume ratio. The accuracy of the classification was approximately 90% when the correct answer data were created using the images of fluorescent cardiomyocytes, and the experimental results were compared. This method is considered, based on the experimental results, to be an effective approach for cardiomyocyte classification.
We propose a method for obtaining clear underwater images by tracking the motion of suspended matter from video
images captured in water and by separating the images into foreground and background. We assume that input images
are the superposition of a foreground and a background, and constructed a transition model and the observation model.
An input image is divided into patches and tracking of the foreground in each patch is performed while applying Kalman
filter to separate the input images into the foreground and the background. From the result of the experiment using
simulated images, we confirmed that the background images were successfully estimated and a region that was moving
slowly was also recognized as a part of the background.
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