Krisana Chinnasarn

Removing salt-and-pepper noise in text/graphics images

Documents containing text and graphics components are usually acquired as binary images for computer processing purposes. Salt-and-pepper noise is a prevalent artifact in such images. Removing this noise usually requires iterative or multiple-pass processing, some techniques even cause distortions in document components. In this paper, we propose a novel method based on the kFill algorithm that can be accomplished in single-pass scan over the image. The algorithm is capable of removing simultaneously both salt noise and pepper noise of any sizes that are smaller than the size of document objects. Results of the proposed method are given in comparison with the well-known morphological operations.

Pathfinding of 2D & 3D game real-time strategy with depth direction A∗ algorithm for multi-layer

The pathfinding analysis has importance for various working such as logistics, transportation, operation management, system analysis and design, project management, network and production line. Especially, game programming technology has effect to economic and dominates culture, increasingly. The shortest path analysis is artificial intelligent which developed capability about think cause and effect, learning and thinking like human. Heuristic technique is method for solving a problem which gets result or not. It may return unexpected value which depends on each problem. 3D game realtime strategy uses shortest path analysis control path for movement of character, wheeled vehicle, animals, and fantasy buildings. It uses path for movement ever time, both in sea, terra and sky (three layers). The position of 3D object use refers 2D coordinate. Generally, the most of 3D game strategy both on LAN and online will play on terrain more than inside building or the room. This research proposes two essences. It proposes comparison rapidity, intelligence and efficiency about seven path finding algorithms which is principle algorithm are familiar. These algorithms used in game computer extensively as follows [1] Depth First Search [2] Iterative Deepening [3] Breadth First Search [4] Dijkstras Algorithm [5] Best First Search [6] A-Star Algorithm (A∗) [7] Iterative Deepening A∗. Besides, this research proposes Depth Direction A∗ algorithm method which new method which use linear graph theory cooperate with A∗ Algorithm calculate increase efficiency of avoid barrier object on maps and search for shortest path of multi-layer. It movement are natural characteristic more other method. The cost node grows less than A∗ algorithm in clear area and plan about expand node before processed. It guarantee about expand node less than A∗ algorithm node certainly. It takes time movement and avoid barrier object less than uses other algorithm. This work able decrease expand node 22.12–70.67% depend on property of area between pathway by define 3D rendering rate are stable.

Image-processing-oriented optical mark reader

This paper describes the development of an optical mark reader that can be used for counting the examination score from the multiple-choice answer sheet. The system is developed based on PC-type microcomputer connecting to an image scanner. The system operations can be distinguished into two modes: learning mode and operation mode. In the learning mode, the model corresponding to each type of answer sheet is constructed by extracting all significant horizontal and vertical lines in the blank-sheet image. Then, every possibly cross-line will be located to form rectangular area. In the operation mode, each sheet fed into the system has to be identified by matching the horizontal lines detected with every model. The data extraction from each area can be performed based on the horizontal and vertical projections of the histogram. For the answer checking purpose, the number of black pixels in each answer block is counted, and the difference of those numbers between the input and its corresponding model is used as decision criterion. Finally, the database containing a list of subjects, students, and scores can be created. The experimental results on many styles of answer sheets show the effectiveness of such a system.

Migration planning using modified Cuckoo Search Algorithm

Mobile agent is a distributed computing technology which allows the agents to carry their jobs on behalf of users from current processing server to the others. Hence, the migration planning is a significant issue for improving performance of mobile agent. In this paper, we propose an improving method for finding path of mobile agent migration planning based on Cuckoo Search Algorithm. Our proposed method modified the original Cuckoo Search Algorithm in two ways. That is, for the first one: in finding new nest, we propose random replacement instead of Lévy fight algorithm. Then, for the second one: in destroying egg, we propose a context sensitive parameter instead of a constant parameter. The experimental results, comparing with migration using the former proposed method, original Cuckoo Search Algorithm and Ant Colony System, confirm that the proposed migration algorithm improves the path finding within the limited time scale.

Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms

A new aspect of imbalanced data classification was studied. Unlike the classical imbalanced data classification where the cause of problem is due to the difference of data sizes, our study concerns only the situation when there exists an overlap between two classes. When one class overlaps another class, there are three regions induced from the overlap. The first region is the overlapped region between two classes. The rest is the non-overlapped region of each class. The imbalance situation is obviously caused by the different amount of data at the overlapped region and non-overlapped region. In this situation, the difference of data sizes from different classes is not the main concern and has no effect on the accuracy of classification. In this research, a combined technique, called Soft-Hybrid algorithm, was proposed for improving classification performance. The technique was divided into two main phases: boundary region determination and responsive classification algorithms for each sub-area. In the first phase, data were grouped as (1) non-overlapping data, (2) borderline data, and (3) overlapping data. Learning data using modified Hausdorff Distance, Radial Basis Function Network and K-Means clustering technique with Mahalanobis Distance. Then, modified Kernel Learning Method, modified DBSCAN and RBF network were applied to classify the data into proper groups based on statistical values from the classification phase. Finally, the results of all techniques were combined. The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data having large overlapping sections based on TP rate, F-measure and G-mean measures. Moreover, the computational times of the proposed method were lower than the standard algorithms used for this type of this problem.

Fall detection using directional bounding box

Falls are significant public health problem. In the last few years, several researches based on computer vision system have been developed to detect a person who has fallen to the ground. This paper presents a novel fall detection technique namely the directional bounding box (DBB) to detect a falls event especially a situation of fall direction paralleling the line of cameras sight. The DBB is constructed with perspective side view transformation of depth information. Moreover, a new aspect ratio namely the center of gravity point (COG) is proposed to monitor human movement. The proposed technique was evaluated with the video data set gathering from a RGB-D sensor. The experimental result of the proposed technique was better both accuracy and response times than previous works.

Automatic exudates detection in retinal images using efficient integrated approaches

Diabetic Retinopathy with exudates causes a major problem in human visualization and becomes a cause of blindness to diabetic patients. In addition, the numbers of diabetic retinopathy patients are increasing while the numbers of doctors are not easily increased in the same proportion. This circumstance causes a heavy work load for doctors. In the past, the medical image processing research has shown that simply getting a second opinion can significantly help physicians diagnosis. This research proposes a method to detect exudates from diabetic retinopathy images. The early exudates detection of diabetic retinopathy patients will reduce seriousness in diabetic retinopathy. The proposed method for detecting exudates consists of 5 major steps as follows: 1) To improve the quality of images by using the contrast limited adaptive histogram equalization (CLAHE) 2) To apply the object attribute thresholding algorithm (OAT) for non-retinal object removal, 3) To implement Frangis algorithm based on Hessian filtering for blood vessel detection 4) To detect the retinal optic disc by applying the combination between multi-resolution analysis and Hough transform and 5) To classify exudates in the remaining region with algorithms of hierarchical fuzzy-c-mean clustering. The performance of the proposed method is evaluated on DIARETDB, which is the retinal image database of the Lappeenranta University of Technology, where the performance is good enough for exudates detection.

Semi-automated detection of breast mass spiculation using active contour

The use of computer research for breast cancer diagnosis in digital mammograms has been studied by some researchers for years. The researches based on medical image processing were developed and published continuously. Theirs objective are to create a diagnostic tool that can increase the accuracy of risk analysis for breast cancer. At the early stage, cancers may be identified as spiculated masses revealing architectural distortion. This research proposes a semi-automated method to detect architectural distortion characterized by thin lines radiating from its margins. It will help physicians as second or minor opinion before biopsy operation. The proposed method involves following major steps in sequence. A combination of the object attributes thresholding, hill-climbing and region growing algorithm is applied to digital mammogram for background and breast pectoral muscle removal. The second is a region of interest (ROI) selection based on image splitting and breast ratio estimation. In the third step, the shade corrections of ROI are considered by using the contrast-limited adaptive histogram equalization. Next, we apply the modified hierarchical clustering to detect and enhance the possible cluster of spiculated masses. The other clusters will be a significant reduction. The final step is established to segment spiculated shape by employing the parametric active contour method. The numerical experiments of the proposed method are performed by testing on the digital database for screening mammography (DDSM) made up by the University of South Florida.

Intrusion feature selection using Modified Heuristic Greedy Algorithm of Itemset

This paper proposes the Modified Heuristic Greedy Algorithm of Itemset (MHGIS) as a feature selection method for Network Intrusion Data. The proposed method can be use as an alternative method to gain the proper attributes for the proposed domain data: Network Intrusion Data. MHGIS is modified from original Heuristic Greedy Algorithm of Itemset (HGIS) to increase efficiency for finding proper feature. In our work, we compare our result with the common method of feature selection is which the Chi-Square (Chi2) feature selection. There are 4 main steps in our experiment: Firstly, we start with data pre-processing to discard unnecessary attributes. Secondly, MHGIS feature selection and Chi2 feature selection have been employed on the pre-processed data, to reduce the number of attributes. Thirdly, we measure the recognition performance by using supervised learning algorithms which are C4.5, BPNN, RBF and SVM. Lastly, we evaluate the results received from MHGIS and Chi2. From the KDDCup99 dataset, we got 13,499 randomly sampling patterns with 34 data dimensions. With the use of MHGIS and Chi2 algorithms, we obtain 14 and 26 features respectively. The result shows that, the classification accuracies measure by C4.5 over the MHGIS selection algorithm produces better accuracies as compare to the Chi2 feature selection and HGIS feature selection over all types of classification methods.

Vertebral pose estimation using horizontal gradient vector field

Automatic and accurate detection of vertebral pose of low resolution x-ray images with different bone structure is essential to diagnose patient condition. Low resolution and incomplete x-ray image is created from low level of irradiating that can avoid radiation over doze. In this research, we propose a novel approach to estimate vertebral pose. There are three main steps including Vertical Projection, Gradient Vector Flow and Feature of Vector Field. Firstly, we segment vertebral zone from full image using Vertical Projection Average with Normal Distribution. Secondly, we introduce Gradient Vector Flow to identify background and foreground of image. Energy calculation helps to divide different objects on image. Then vector field algorithm will help to promote the exact border of the object. Finally, the horizontal field is chosen as the best information to identify the vertebral pose. From the experimental results, we can extract six main bone layouts. There are two normal layouts and four abnormal layouts. For each layout, we deploy different criteria to identify vertebral pose. For the layouts that located between 0-67 index, the separation hyperplane needs to be shifted up, otherwise shifted down. We use 80 low resolution and incomplete x-ray images from a local hospital. The accuracy rate is 79.25% compared with the ground-truth.