Titijaroonroj, Taravichet

Preferred name

Alternative Name

Titijaroonrog, Taravichet

Main Affiliation

School of Information Technology

taravichet.ti@kmitl.ac.th

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Text-background decomposition for thai text localization and recognition in natural scenes
(2014-01-12)
Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Suttapakti, Ungsumalee
;
Boonchukusol, Pimlak
;
Titijaroonroj, Taravichet
Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-en-tropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.
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Texture analysis assessment for images
(2017-02-23)
Titijaroonroj, Taravichet
;
Kaewaramsri, Yothin
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Suttapakti, Ungsumalee
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Woraratpanya, Kuntpong
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Kuroki, Yoshimitsu
Commonly, the existing metrics such as mean square error (MSE), peak signal-To-noise ratio (PSNR), quality index (QI), structural similarity index metric (SSIM), and quality index based on local variance (QILV) use the image intensity-based statistics approach to assess the quality of distorted images. These metrics are successful in discriminating the quality of distorted images, such as de-noising, JPEG compressed, and blur images. However, they are unsuccessful in discriminating the quality of channel decomposition images. Therefore, this paper proposes the texture analysis assessment (TAA) to measure the quality of both normally distorted images and channel decomposition images. The proposed metric uses image intensity statistics in conjunction with texture analysis for quality discrimination of slightly different distorted and channel decomposition images. The texture analysis based on edge orientation is an important part employed to measure precise image errors. The experimental results illustrate that the TAA metric can evidently discriminate the quality of normally distorted images and channel decomposition images, when compared with state-of-The-Art metrics. Furthermore, the perceived visual quality and the quality value of TAA are corresponding; the lower visual quality human-eye perceives, the lower quality value TAA measures, and vice versa.
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Seven Segment Display Detection and Recognition via Deep Learning Technique
(2022-01-01)
Suttapakti, Ungsumalee
;
Titijaroonroj, Taravichet
;
Nunsong, Walairach
;
Kakanopas, Donyarut
Seven segment display detection and recognition play an important role in determining the status of manufacturing machines. However, in some industrial factories, employees are still assigned to manually record the status of the seven segment displays. This is not real-Time tracking status and it is easy to make the typos or mistakes while collecting data. Hence, image processing and machine vision are used to automatically detect and recognize images from the seven segment displays. In this paper, the Cascade R-CNN is applied to automatically detect and recognize seven-segment displays in a single model-End-To-end learning because this method is efficient and flexible. The Cascade R-CNN method achieves precision, recall, and F1-score of 0.999 which are higher than conventional methods and the state-of-The-Art methods, including Faster R-CNN, RetinaNet, NAS-FPN, CornerNet, and CenterNet. Although the recognition accuracy of Cascade R-CNN is slightly lower than those of YOLOv3 and CornerNet, its accuracy is still higher than the Faster R-CNN, SSD, RetinaNet, NAS-FPN, and CenterNet. This method can automatically detect and recognize the digits on seven-segment display in a single model, thus improving the effectiveness for detecting and recognizing seven-segment display images.
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Spatial-Frequency Redistribution-based Saliency Region Detection for Thai Text Localisation
(2022-03-01)
Titijaroonroj, Taravichet
;
Suttapakti, Ungsumalee
;
Nunsong, Walairach
Saliency region detection plays an important role in computer vision applications and related areas, such as human fixation, figure-ground separation, face detection, and image compression. Nevertheless, state-of-the-art methods can moderately detect the saliency regions of a psychological pattern dataset. Therefore, this paper proposes a spatial-frequency redistribution (SFR) method to improve the efficiency of the detection of the saliency regions. The proposed SFR method consists of two main procedures: (i) adaptive cosine image construction-and-implantation and (ii) saliency region detection using complete multiple-object-implanted images. The former procedure constructs an adaptive cosine image by using a cosine function based on an object structure and then individually implants it into each object detectable. This stage provides the first significant property, spatial redistribution, to the object implanted. The adaptive cosine image redistributes the objects for controllability. Then, the latter procedure transforms a complete multiple object-implanted image into the frequency domain. At this point, the second significant property, frequency redistribution, provides the simple technique for identifying and separating the target object from the unwanted objects and background. In this paper, these properties are referred to as spatial-frequency redistribution. This method was realized as a computer program, and then such program was tested with a psychological pattern dataset and a Thai text dataset. The experimental results, when compared with state-of-the-art methods, show that the proposed SFR method can achieve the clear detection of the attention region in the psychological pattern dataset. Moreover, the proposed method can achieve a F-value of 80.28% with a recall and precision of 76.32% and 85.23%, respectively, for Thai text localisation in the Thai text dataset.

Titijaroonroj, Taravichet

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