Chitsobhuk, Orachat

Preferred name

Chitsobhuk, Orachat

Main Affiliation

School of Engineering

orachat.ch@kmitl.ac.th

Now showing 1 - 9 of 9

Some of the metrics are blocked by your
consent settings
An improvement of PDLZW implementation with a modified WSC updating technique on FPGA
(2009-12-01)
Vichitkraivin, Perapong
;
Chitsobhuk, Orachat
In this paper, an improvement of PDLZW implementation with a new dictionary updating technique is proposed. A unique dictionary is partitioned into hierarchical variable word-width dictionaries. This allows us to search through dictionaries in parallel. Moreover, the barrel shifter is adopted for loading a new input string into the shift register in order to achieve a faster speed. However, the original PDLZW uses a simple FIFO update strategy, which is not efficient. Therefore, a new window based updating technique is implemented to better classify the difference in how often each particular address in the window is referred. The freezing policy is applied to the address most often referred, which would not be updated until all the other addresses in the window have the same priority. This guarantees that the more often referred addresses would not be updated until their time comes. This updating policy leads to an improvement on the compression efficiency of the proposed algorithm while still keep the architecture low complexity and easy to implement.
Some of the metrics are blocked by your
consent settings
Storm eye identification using fuzzy inference system
(2016-08-01)
Warunsin, Kulwarun
;
Chitsobhuk, Orachat
In this paper, a study of the novel technique based on Fuzzy Inference System (FIS) for storm eye identification has been presented. The ocean wind vectors are provided by the NASA QuikSCAT satellite to predict the significance of tropical cyclogenesis. This database is slightly noisy, incomplete and indirect. For this reason, the cloud satellite image can be an alternative option. However, the cloud shape may be ambiguous, which can introduce a long search time. As a result, utilizing combined information from both resources can lead to a reduction in resource deficiency. The FIS is used to describe the uncertain behavior of the complex system consisting of several factors. It provides ability to model the dynamic behavior of the storm and designates the best candidate eye position in the region of interest. Then, the spiral cloud model is adopted to enhance the search results in order to achieve the accurate eye position. The experimental results are conducted based on six reference storms. The proposed system offers higher flexibility in analyzing the storm eye position with the minimum average distance error of 92.8 km and approximately 16.25% less average distance error compared to the reference. This demonstrates the significant performance improvement in detecting the eye location of the storm.
Some of the metrics are blocked by your
consent settings
A CNN-BASED MULTI-MODEL ENSEMBLE METHOD FOR INDOOR AND OUTDOOR MULTI-VIEW STEREO RECONSTRUCTION
(2022-01-01)
Wattanacheep, Bhattarabhorn
;
Chitsobhuk, Orachat
Camera poses estimation is a critical process that ensures the success of Three-Dimensional (3D) modelling. We present a Convolutional Neural Network (CNN)-based multi-model ensemble method for indoor and outdoor multi-view stereo reconstruction capable of learning across multiple domains, including images from both indoor and outdoor environments. Each domain’s images have distinct properties and shooting view-points, which leads to difficulty in efficient learning such a large difference and requires large amount of computational resources. In order to reduce complexity of the end-to-end single model, the proposed model is divided into multiple learning agents consisting of domain-specific agents and domain relationship agent. The domain-specific agent is trained independently on its own set of unique image characteristics, for example, one for indoor datasets and another for outdoor datasets. The domain relationship agent then ensembles and analyzes the multiple domain features and finalizes the estimation. In terms of average root mean square error, we compare the performance of the combined domain single model with the suggested ensemble CNN model. The experimental results indicate that the proposed model outperforms the others, with rotation and translation prediction errors of 0.112012266.
Some of the metrics are blocked by your
consent settings
Ultrasonic echo image adaptive watermarking using the just-noticeable difference estimation
(2009-12-01)
Khawne, Amnach
;
Hamamoto, Kazuhiko
;
Chitsobhuk, Orachat
Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
Some of the metrics are blocked by your
consent settings
Filter Pruning with Convolutional Approximation Small Model Framework
(2023-09-01)
Intraraprasit, Monthon
;
Chitsobhuk, Orachat
Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-tuning pruned models necessitates substantial computing power and a large retraining dataset. To restore model performance after pruning each layer, we propose the Convolutional Approximation Small Model (CASM) framework. CASM involves training a compact model with the remaining kernels and optimizing their weights to restore feature maps that resemble the original kernels. This method requires less complexity and fewer training samples compared to basic fine-tuning. We evaluate the performance of CASM on the CIFAR-10 and ImageNet datasets using VGG-16 and ResNet-50 models. The experimental results demonstrate that CASM surpasses the basic fine-tuning framework in terms of time acceleration (3.3× faster), requiring a smaller dataset for performance recovery after pruning, and achieving enhanced accuracy.
Some of the metrics are blocked by your
consent settings
HUMAN ACTIVITY RECOGNITION USING LONG SHORT-TERM MEMORY NETWORK
(2023-06-01)
Warunsin, Kulwarun
;
Promjiraprawat, Kamphol
;
Chitsobhuk, Orachat
Human Activity Recognition (HAR) plays a significant role in the Ambient Assisted Living (AAL) system, which aims to provide sustainable healthcare for an aging population and those with special needs. HAR automatically categorizes people’s activities while they wear wearable sensors. With an effective HAR system, we should be able to monitor the behavior of individuals as well as their activities and issue specific warn-ings as necessary. The goal of this paper is to propose a methodological framework for developing the HAR model based on an application of Long Short-Term Memory (LSTM) network. We investigated the model selection and parameters based on Cross Validation (CV) and learning rate optimization across two well-known public HAR datasets, Mo-biAct and WISDM. An analysis of the CV variance becomes a considerable impact on the generalization of the model’s learning capability. The relationship between the CV variance and accuracy can be used to guide the selection of the fold number in k-fold CV. Our studies had shown the scientific evidence and technical guidance for solving the HAR problem with improvements not only in the proposed model’s accuracy and AUC of more than 99% on average, but also in its generalization performance, which could be useful for future related studies.
Some of the metrics are blocked by your
consent settings
FILTER PRUNING BASED ON LOCAL GRADIENT ACTIVATION MAPPING IN CONVOLUTIONAL NEURAL NETWORKS
(2023-12-01)
Intraraprasit, Monthon
;
Chitsobhuk, Orachat
Convolutional Neural Network (CNN) is a well-known Deep learning model utilized extensively in the field of computer vision. The structure of convolutional neural networks is quite complicated and necessitates a substantial amount of computational time and storage resources. As a result, it is difficult to adopt a CNN model on a resource-constraint device. Model pruning can help to reduce computation time and storage re-quirements. In this research, we propose a filter pruning technique based on Localized Gradient Activation heatmaP (LGAP) for the purpose of pruning CNNs. Analyzing a filter based on statistical criterion of single neuron can lead to a loss in spatial relations within the filter activation itself, the relationship to target prediction, as well as the relationship among filters in that specific layer. To minimize the limitations, we evaluate the significance of a filter through the spatial information of local gradient activation related to the target prediction in terms of the layer-wise loss of the investigated filter. The effect of loss of an investigated filter demonstrates the significance or insignificance of the filter. Our pruning criteria ensure that these significant filters are preserved, while maintaining the model accuracy. The performance of our pruning method was validated using VGG-16 and ResNet-50. With pruning ratio of 50%, VGG-16 tends to decrease 1.66% of its accuracy, 3.6× of FLOP and 3.9× of storage reduction. For ResNet-50, with 50% pruning ratio, the results show that Top-1 and Top-5 of our pruning techniques outperform all the baseline techniques with a reduction of top-1 accuracy by 3.56%, top-5 accuracy by 1.89%, Floating Point Operation by 2.3×, and storage by 2.05×.
Some of the metrics are blocked by your
consent settings
Adaptive quantization via fuzzy classified priority mapping for liver ultrasound compression
(2016-01-01)
Sombutkaew, Rattikorn
;
Chitsobhuk, Orachat
;
Prapruttam, Duangkamon
;
Ruangchaijatuporn, Thumanoon
This paper proposes adaptive quantization based on fuzzy classified priority mapping in order to achieve higher encoding efficiency. The priority map serves as a quantization mask, which is adaptively adjusted according to the statistical characteristics in terms of histograms based on the results of Fuzzy C-mean clustering. With its soft clustering property, the results illustrate robustness to ambiguity of the data and thus retain much more information than hard clustering. The priority map represents levels of significance as the Most Significant Group (MSG), the Normal Significant Group (NSG), and the Lowest Significant Group (LSG). The significant candidates of irregular liver tissues requiring special doctor attention will be assigned with higher priority than those from the regular ones. The higher the priority, the greater the number of bits assigned for encoding. An analysis of suitable quantization step size has been conducted. With the selection of appropriate quantization parameters for each priority level, the blocking artifacts can be greatly reduced. This helps to reduce the encoding bit rate and enhance the compression efficiency for the transmission and storage while maintaining an acceptable diagnostic image quality.
Some of the metrics are blocked by your
consent settings
Image watermarking based on adaptive models of human visual perception
(2010-01-01)
Khawne, Amnach
;
Hamamoto, Kazuhiko
;
Chitsobhuk, Orachat
This paper proposes a digital image watermarking based on adaptive models of human visual perception. The algorithm exploits the local activities estimated from wavelet coefficients of each subband to adaptively control the luminance masking. The adaptive luminance is thus delicately combined with the contrast masking and edge detection and adopted as a visibility threshold. With the proposed combination of adaptive visual sensitivity parameters, the proposed perceptual model can be more appropriate to the different characteristics of various images. The weighting function is chosen such that the fidelity, imperceptibility and robustness could be preserved without making any perceptual difference to the image quality. © 2010 The Institute of Electrical Engineers of Japan.

Chitsobhuk, Orachat

Filters

Settings

Sort By

Results per page