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    Eye Landmarks Detection using RT-DETR with Rules
    (2024-01-01)
    Boonnithititikul, Chatree
    ;
    Jaknamon, Teetouch
    ;
    Chawuthai, Rathachai
    In order to help ophthalmologists diagnose eye problems, it is necessary to scan for eye landmarks such as the pupil, the reflection point on the retina, and the boundary of the eye. An individual's eye landmarks on their face can be obtained via some facial landmarks' detection methods, including Haar Cascade. Two problematic aspects of the current approaches, however, are that the pupil and reflection point information is not provided, and the detection is ineffective when confronted with a picture of the upper half of the face or a person wearing a mask. In this study, we intend to develop a deep learning model for eye landmark identification using the Realtime identification Transformer (RT-DETR) approach together with our rules. As a consequence, nine landmark points-two for the eye, six for the pupil, and one for the reflection, are computed with an accuracy of 0.974. Since the focus of this paper is on eye landmark recognition, the next stage will be to build an application and a machine learning model for the diagnosis of eye disorders. - Keywords Deep Learning, Detection, Eye Landmarks, Facial Landmarks, Ophthalmology, RT-DETR
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    ThaiTC:Thai Transformer-based Image Captioning
    (2022-01-01)
    Jaknamon, Teetouch
    ;
    Marukatat, Sanparith
    For problems with image captioning is a technique that has been used for a long time. In the past, there was a way to use convolutional neural network (CNN) for feature extraction and recurrent neural network (RNN) for generating text, and especially in Thai language, It has to be developed further in the era of the popular use of transformers. This paper proposes an end-to-end image captioning with pretrained vision Transformers (ViT) and text transformers in Thai language models namely ThaiTC, Which leverages the transformer architecture both. We has experiment pretrained vision transformer and text transformer in Thai language that best for Thai image captioning and tested on 3 Thai image captioning datasets 1) Travel 2) Food 3) Flickr 30k(tr anslate) with different challenges. Includes freeze vision transformers weight training for image captioning dataset training with less image features, From the experiment, We found that ThaiTC performed much better in the Food and Flickr30k datasets than the Travel datasets, Which allowed us to automatically create subtitles about food and travel.