Chitsobhuk, Orachat

Preferred name

Chitsobhuk, Orachat

Main Affiliation

School of Engineering

orachat.ch@kmitl.ac.th

Now showing 1 - 10 of 33

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Color descriptor for image retrieval in wavelet domain
(2006-01-01)
Utenpattanant, Ariya
;
Chitsobhuk, Orachat
;
Khawne, Amnach
This paper presents an approach to manage a large database using a compact color descriptor and a statistical method for accurately pruning the database. A compact color descriptor adopted in the proposed content-based image retrieval system is 63-bit binary Haar color histogram, which is very compact and can be effectively used for fast image search. In addition to fast searching using this compact descriptor, we further improve retrieval time by applying pruning technique, which looks for the candidate images similar to the query image from the database and ignore the rest that are not likely to the query image. The descriptors of the candidate images are then matched with that of the query. The most similar images will be retrieved and ordered according to their distance to the query. The proposed retrieval system can efficiently retrieve the most similar images from the database while can help reducing the retrieval time and the storage space.
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Image registration using hough transform, phase correlation and best-first search algorithm
(2007-12-01)
Chunhavittayatera, Siwaphon
;
Chitsobhuk, Orachat
This paper presents image registration using a combination of Hough transform, phase correlation and best-first search algorithm to estimate rotation and translation parameters. These parameters are used to register input images and create a seamless representation of the registered image. In the first step, the translation of angles are pre-calculated based on ID phase correlation in Hough space and used as candidate angles. Then, best-first search algorithm is applied to obtain the best translation of angle from candidate angles, which is used to de-rotated the input images. In the second step, the translation in x-y axis is computed using 2D phase correlation. Finally, the input images are registered using the estimated translation parameter. The experimental results using various image details and sizes show the accuracy of the proposed technique to detect the translation parameters. The best-first search algorithm can help to increase the precision of rotation parameter after Hough transform and phase correlation while require less amount of processing time compared to full search algorithm.
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Multi-pipeline architecture for face recognition on FPGA
(2009-11-18)
Visakhasart, Sathaporn
;
Chitsobhuk, Orachat
In this paper, a new multi-pipeline architecture is proposed for face recognition system on FPGA. The proposed structure consists of four main units: Multi-Pipeline Control Unit (MPCU), Process Element Unit (PEU), Region Summing Unit (RSU), and Recognition Indexing Unit (RIU). Four recognition techniques: Principal Component Analysis (PCA), Modular PCA (MPCA), Weight MPCA (WMPCA), and Wavelet based techniques are adopted to evaluate the efficiency of the proposed architecture using several standard face databases. The experimental results show that the proposed architecture helps minimizing processing time through its multi-pipeline processes while still maintains high recognition rate. Moreover, the design has encouraged the reduction in hardware resources by utilizing the proposed reusable modules. © 2009 IEEE.
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Image retrieval using connected color region and moment invariants
(2007-12-01)
Dongphontong, Daungkamol
;
Chitsobhuk, Orachat
In this paper, combined descriptors of Connected Color Region (CCR) and moment invariants are proposed as color features for a Content-Based Image Retrieval system. CCR provides the spatial information and maximum co-occurrence color while moment invariants help to better distinguish different distribution of colors in the image. From the experiments, the retrieval results using only CCR descriptors depend on CCR block size. The smaller the size of the block, the higher the retrieval performance. However, if the block size is small, it requires longer retrieval time. Therefore, in this paper, color moment is introduced as additional feature to CCR descriptors to help compromising between retrieval performance and time. The retrieval results using both CCR with large block size and moment descriptors are comparable to those of using only CCR with small block size while require less amount of retrieval time.
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Variance training data in image enhancement
(2019-07-01)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
This paper presents a study of neuro-fuzzy behavior in clustering gradient profile spectral characteristics. Various types of image scene are chosen to evaluate neuro-fuzzy performance. The combinations of training data subsets are learned by ANFIS model to generate gradient profile priors, which are used as optimum weight selection criteria for image enhancement. The experimental results illustrate quantitative performance improvement and perceptual improvement in recovery of the high-resolution details in various images.
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Cross domain sentiment classification of Thai reviews using co-train model
(2019-01-01)
Boonpetch, Warakorn
;
Chitsobhuk, Orachat
Online reviews are significant sources of information, which is useful for supporting customer and entrepreneur decision in terms of product and service satisfaction analysis. Online reviews containing feedback from various domains makes it difficult to analyze and classify all comments at once. The proposed technique analyses the cross-domain Thai review data using a co-train machine learning model. The co-train model consists of multiple single domain specific models followed by refinement analysis for the final sentiment classification. This allows for full flexibility in training of each individual domain, which can lessen the limitation on training complexity due to simple training on single domain. The experiments have been conducted on Wongnai restaurant domain and IMDB movie domain data. Our co-train model can achieve the highest average accuracy of 86.10 percent for cross-domain sentiment classification with approximately 38 seconds processing time.
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Multidestination Indoor Navigation Using Path Planning and WiFi Fingerprint Localization
(2018-09-11)
Chitsobhuk, Orachat
;
Warunsin, Kulwarun
;
Udomthanapong, Sornchai
This paper presents an indoor navigation system based on multi-destination path planning and WiFi fingerprint localization. A user is allowed to specify multiple destinations and can detour the route at any time. Path planning will automatically update path using 2-opt and A∗ algorithms. The revised route will be analyzed according to user's current position supplied from the WiFi RSS fingerprint positioning. Naïve Bayes classification is adopted to learn from the RSS fingerprint priors stored in the database. Extensive experiments are conducted and performance comparison is analyzed and demonstrates significant performance improvement and higher noise tolerance with integration of the probabilistic priors. It can be seen that the proposed system enables user experience for indoor navigation service with support for automatic route updating and navigation refinement according to localization.
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An FFT-Based technique and best-first search for image registration
(2008-12-01)
Samritjiarapon, Olan
;
Chitsobhuk, Orachat
The image registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different time, from different sensors, or from different viewpoints. One of the major challenges related to image registration is the estimation of large motion, when input images contain small overlapped area. Common image registration using the search algorithm can accurately finds large motion but requires high computation cost due to large search space. Fourier-based technique is an alternative approach since it can rapidly achieve the registration results through its FFT algorithm. However, only Fourier-based technique cannot produce the correct results in the case of large translation. Thus, this paper presents a Fourier-based technique cooperated with best-first search algorithm to analyze the correct translation between two input images. The Fourier-based technique is used to estimate the candidate translations to decrease searching space while best-first search algorithm is used to further search for the correct translation. The proposed technique can estimate large translations, scalings, and rotations in images by an extension of well-known phase correlation technique. The experimental results using various image details show the accuracy of the proposed technique to detect large translations compared to the other techniques in frequency domain. © 2008 IEEE.
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Image retrieval using haar color descriptor incorporating with pruning techniques
(2007-07-03)
Utenpattanant, Ariya
;
Chitsobhuk, Orachat
This paper presents a content-based image retrieval system using a compact color descriptor (63-bit binary Haar color descriptor) incorporating with pruning techniques. The objective of pruning is to look for the candidate images similar to the query image from the database and ignore the rest that are not likely to the query image. Several statistical pruning criteria are experimented in order to achieve faster retrieval time while preserve good retrieval performance. After the pruning process, the descriptors of the candidate images are then matched with that of the query. The most similar images will be retrieved and ordered according to their distance to the query. The proposed technique is evaluated based on different sizes of the database. The experimental results show good retrieval performance over a variety of image collections.
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Image enhancement based on edge boosting algorithm
(2015-01-01)
Ngernplubpla, Jaturon
;
Chitsobhuk, Orachat
In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.

Chitsobhuk, Orachat

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