A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

Automatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PD

Phytochemical, Antioxidant and Antimicrobial Activities of Hevea brasiliensis Leaves Extract

Belonging to the Euphorbiaceae family, the Para rubber tree is formally referred to as Hevea brasiliensis in scientific terms. It is commonly known as an important economic commodity in Thailand because the natural rubber primarily originates from the milky latex obtained from the tree. However, the available research on the phytochemicals found in different parts of the rubber tree and their biological effects is quite restricted. This study aimed to determine the phytochemical constituents, antioxidant and antibacterial activity studies on the crude dry leaf extracts of H. brasiliensis. The results indicated the presence of alkaloids, anthraquinones, cardiac glycosides, coumarin, flavonoids, saponin, steroids, tannins, and terpenoids. The total phenolic content was 63.95±4.31 mgGAE/g in the ethanolic leaf extract. The ethanolic extract displayed notable effectiveness in scavenging free radicals (71.2±0.17%) at 500 μg/ml concentration and antioxidant capacity (the lowest IC50 value 42.57±0.91 μg/ml). The ethanol extract of the leaf of H. brasiliensis showed  inhibition zone on all of the selected bacteria (gram-positive; Bacillus subtilis, Staphylococcus aureus, Staphylococcus epidermidis, and gram-negative; Escherichia coli, Pseudomonas aeruginosa) at 200 mg/ml. In conclusion, the dried leaves of H. brasiliensis compose phytochemicals that exhibit antioxidant and antibacterial activities and possesses the potential to act as a reservoir of plant-derived antibiotics and natural antioxidants