De La Salle University

About: De La Salle University is a education organization based out in Manila, Philippines. It is known for research contribution in the topics: Population & Computer science. The organization has 2951 authors who have published 4374 publications receiving 49567 citations. The organization is also known as: Pamantasang De La Salle.

Hair artifact removal and skin lesion segmentation of dermoscopy images

Abstract: Objective: The objective of this research is to perform automatic hair artifact removal and skin lesion segmentation on dermoscopy images. Methods: Dermoscopy images are images from the examination of the skin lesion using a dermatoscope. There are different types of skin lesion artifacts, structures, or objects that are present in dermoscopy images. This is a pertinent problem that can inhibit the proper examination and accurately segment the skin lesion from the surrounding skin area. Artifacts, such as hair strands, introduce additional features that can also cause problems during classification. Our process starts with hair removal using a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Then, skin lesion segmentation is performed using a binary conversion, a dilation, a perimeter detection and morphological opening, and then the removal of small connected pixels. Results: Experiments were carried out on the PH2 dermoscopy images. The border of the lesion was quantified for evaluation by four statistical metrics with the lesions identified by the PH2 as the reference image, resulting with a true detection rate (TDR) of 82.31 and a false detection rate of 5.69. Conclusions: The results obtained in the research work on hair artifacts removal and skin lesion segmentation provides acceptable results in terms of TDR and low false-positive rates.

Terahertz emission enhancement in low-temperature-grown GaAs with an n-GaAs buffer in reflection and transmission excitation geometries

Abstract: The growth of low-temperature-grown GaAs (LTG-GaAs) on semi-insulating GaAs substrate with a 0.2 μm n-GaAs buffer demonstrated enhanced terahertz (THz) emission in reflection and transmission excitation geometries via time-domain spectroscopy. The transient photocurrent of the sample in reflection and transmission geometries resulting from ultrafast excitation yielded a 215% and 165% increase in the THz emission, respectively, as compared with a sample grown with an undoped GaAs buffer. The LTG-GaAs film with n-GaAs buffer exhibited a significant increase in its built-in field as supported by calculations and photoreflectance experiments. The enhanced THz emission intensity was comparable with bulk p-InAs.

Vision-based Crowd Counting and Social Distancing Monitoring using Tiny-YOLOv4 and DeepSORT

Abstract: With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The application is able to process video files or live camera feed such as CCTV or surveillance cameras and generate reports indicating people detected per unit time, percentage of social distancing per unit time, detection and social distancing logs as well as color-coded bounding boxes to indicate if the detected people are following social distancing protocols.