Pasupa, Kitsuchart

Preferred name

Pasupa, Kitsuchart

Main Affiliation

School of Information Technology

kitsuchart.pa@kmitl.ac.th

Now showing 1 - 10 of 13

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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
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Boonchukusol, Pimlak
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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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Analytical incremental learning: Fast constructive learning method for neural network
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Setiawan, Noor Akhmad
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Adji, Teguh Bharata
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural network (SLFN) based on random input weights which usually requires large number of hidden nodes. Recently, novel constructive and destructive parsimonious (CP and DP)-ELM which provide the effectiveness generalization and compact hidden nodes have been proposed. However, the performance might be unstable due to the randomization either in ordinary ELM or CP and DP-ELM. In this study, analytical incremental learning (AIL) algorithm is proposed in which all weights of neural network are calculated analytically without any randomization. The hidden nodes of AIL are incrementally generated based on residual error using least square (LS) method. The results show the effectiveness of AIL which has not only smallest number of hidden nodes and more stable but also good generalization than those of ELM, CP and DP-ELM based on seven benchmark data sets evaluation.
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Hinge loss projection for classification
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Sugimoto, Masanori
Hinge loss is one-sided function which gives optimal solution than that of squared error (SE) loss function in case of classification. It allows data points which have a value greater than 1 and less than −1 for positive and negative classes, respectively. These have zero contribution to hinge function. However, in the most classification tasks, least square (LS) method such as ridge regression uses SE instead of hinge function. In this paper, a simple projection method is used to minimize hinge loss function through LS methods. We modify the ridge regression and its kernel based version i.e. kernel ridge regression so that it can adopt to hinge function instead of using SE in case of classification problem. The results show the effectiveness of hinge loss projection method especially on imbalanced data sets in terms of geometric mean (GM).
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Evaluation of deep learning algorithms for semantic segmentation of car parts
(2022-10-01)
Pasupa, Kitsuchart
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Kittiworapanya, Phongsathorn
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Hongngern, Napasin
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Woraratpanya, Kuntpong
Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = 69.6 % when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.
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Particle-flow interactive animation for painting image
(2015-01-01)
Rewik, Nutchaphon
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Peuwnuan, Kittipop
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
In this paper, we present an interactive multimedia artwork, which awakens motionless images to interactive animations. This animation simulates the colour-flow movement from painting images. Particle movement and interaction present the rhythm of the brushstrokes. This artwork conveys lively image feelings. Human colour perception is the main idea of this work. We use image-processing techniques to extract colour distinction from painting images. After that, we render numerous particles from distinct colour areas information. Steering behaviour directs particle movement to flow smoothly on a touch-screen monitor. This artwork attracts audiences to admire painting in the new aspect that audiences play with it rather than just watching it. This works designed for arbitrary painting images, unlike previous work which ties to a particular image.
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Square Wave Quadrature Amplitude Modulation for Visible Light Communication Using Image Sensor
(2019-01-01)
Alfarozi, Syukron Abu Ishaq
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Pasupa, Kitsuchart
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Hashizume, Hiromichi
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Woraratpanya, Kuntpong
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Sugimoto, Masanori
Most visible light communication (VLC) technologies use a light emitting diode (LED) as a data transmitter and a photodiode as a receiver. In this paper, we alternatively focus on the use of an image sensor or camera as a receiver due to its wide availability. However, the successful use of an image sensor mainly depends on the efficiency of the encoder-decoder and the modulation scheme. Thus, this paper proposes a novel modulation scheme based on a square wave signal called a square wave quadrature amplitude modulation (SW-QAM) method. This method can accommodate different camera settings and overcome the problem of LED flicker that is generally sensed by human eyes when the LED frequency is low. At the transmitter side, multiple LEDs can be used to increase the transmission bit rate, while, at the receiver side, a Wiener filter is used as a complementary technique to SW-QAM for solving the light interference phenomenon due to the closeness of one LED to another. Our experimental results show that the proposed SW-QAM scheme can decode symbols very well either the for close or far communication distances, dark or bright lighting conditions, and single or multiple LED points.
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Local Sigmoid Method: Non-Iterative Deterministic Learning Algorithm for Automatic Model Construction of Neural Network
(2020-01-01)
Alfarozi, Syukron Abu Ishaq
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Pasupa, Kitsuchart
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Sugimoto, Masanori
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Woraratpanya, Kuntpong
A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a randomization technique that requires a large number of hidden nodes to achieve the high accuracy. This leads to a large and complex model, which is slow at the inference time. Previously, we reported analytical incremental learning (AIL) algorithm, which is a compact model and a non-iterative deterministic learning algorithm, to be used as an alternative. However, AIL cannot grow its set of hidden nodes, due to the node saturation problem. Here, we describe a local sigmoid method (LSM) that is also a sufficiently compact model and a non-iterative deterministic learning algorithm to overcome both the ELM randomization and AIL node saturation problems. The LSM algorithm is based on 'divide and conquer' method that divides the dataset into several subsets which are easier to optimize separately. Each subset can be associated with a local segment represented as a hidden node that preserves local information of the subset. This technique helps us to understand the function of each hidden node of the network built. Moreover, we can use such a technique to explain the function of hidden nodes learned by backpropagation, the iterative algorithm. Based on our experimental results, LSM is more accurate than other non-iterative learning algorithms and one of the most compact models.
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Local variance image-based for scene text binarization under illumination effects
(2017-07-18)
Peuwnuan, Kittipop
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Kuroki, Yoshimitsu
Illumination effects, especially shadow and lighting condition, are grand challenges for scene-text localization. With these effects, text localization faces a difficult task to discriminate text regions from a nature scene due to edge and detail of characters affected by surrounding environments. To improve effectiveness of the scene-text localization, this paper proposes a local variance image technique to enhance character's edge for easily segmenting the scene text from the back-ground under illumination effects. In this method, the local variance image plays an important role in indicating how high complexity is in each local area. Then the proposed adaptive kernel size thresholding method is applied to such a local variance image to segment the scene text from the background. When the proposed method is tested with a Thai text dataset, the experimental results show the scene-text binarization is better than those of the state-of-the-art methods.
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Font descriptor construction for printed thai character recognition
(2013-01-01)
Suttapakti, Ungsumalee
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
The font evolution with various types is a great impact on a recognition performance of optical character recognition (OCR) systems. The more diversity of fonts leads to the less accuracy of recognition rate, particularly Thai-fonts. In order to overcome this obstacle, this paper proposes a font descriptor for printed Thai-character recognition. The role of such a descriptor is a representative of various fonts and sizes. The font descriptor construction is based on principal component analysis (PCA) in a combination with predefined patterns in multi-level processing. The proposed font descriptor is tested on Thai character image corpus consisting of consonants, vowels, and tones. The experimental results show that the proposed font descriptor is efficient and robust to font type and size variations.
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Enhancing Thai Food Recognition Through Multimodal Fusion of Image and Fourier Spectrum
(2024-01-01)
Pasupa, Kitsuchart
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Woraratpanya, Kuntpong
Recognizing food is a challenging task in artificial intelligence research because food items may undergo deformations during cooking or serving, and they can be partially or fully occluded, making it difficult for recognition systems to analyze their complete visual information. Therefore, in addition to evaluating object detection effectiveness, consideration must be given to the texture of the food. However, Convolutional Neural Networks may fall short in capturing textural information. In this research, we propose a method to enhance the efficiency of Thai food recognition by employing the concept of multi-modal fusion, incorporating Fourier Spectrum images to take texture representation into account and improve the model’s performance. In the fusion process, we employed the CentralNet framework and compared it with baselines (using only images and conventional concatenation fusion) on three datasets: THFOOD-50, FoodyDudy, and our newly proposed food dataset called iTHFOOD-200. This new dataset encompasses a more diverse range of food types. The experimental results demonstrate that fusion with the CentralNet framework yielded better performance than the baselines using only images (7.1% Top-1 Accuracy and 3.9% Top-5 Accuracy on average) and conventional concatenation fusion (5.0% Top-1 Accuracy and 2.4% Top-5 Accuracy on average).

Pasupa, Kitsuchart

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