Agus Harjoko

Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm

This paper carried out to develop the concept of CBIR by using Wavelet Transform Methods for feature form and texture extraction and adaptive histogram for feature color extraction. The method is not only recognize the images that have been stored in database, but also be able to find some resemblance ornament image or texture as well as form. Although They are different size, direction of slope, and the layout of texture and color. In calculating the percentage of similarity is not only based on performance measurement precision but also the image of the relevant. Basically Grade value calculation is using the fuzzyfication process to get the similarity values with the S-curve which is then used as input to perform retrieval of image with the threshold algorithm. It will display the image based on the representation of the highest grade in each query image, which has been compared with the image database. High grade values indicates that the characteristic image of the sample (query) is similar to the image database and others. After that proceed by comparing the value of grade representation of the image by using the min operator in fuzzy logic. The Advantage threshold algorithm is the simplicity of similarity image process when the performance of CBIR becomes more reliable.

CONTENT BASED BATIK IMAGE RETRIEVAL

Content Based Batik Image Retrieval (CBBIR) is an area of research that focuses on image processing based on characteristic motifs of batik. Basically the image has a unique batik motif compared with other images. Its uniqueness lies in the characteristics possessed texture and shape, which has a unique and distinct characteristics compared with other image characteristics. To study this batik image must start from a preprocessing stage, in which all its color images must be removed with a grayscale process. Proceed with the feature extraction process taking motifs characteristic of every kind of batik using the method of edge detection. After getting the characteristic motifs seen visually, it will be calculated by using 4 texture characteristic function is the mean, energy, entropy and stadard deviation. Characteristic function will be added as needed. The results of the calculation of characteristic functions will be made more specific using the method of wavelet transform Daubechies type 2 and invariant moment. The result will be the index value of every type of batik. Because each motif there are the same but have different sizes, so any kind of motive would be divided into three sizes: Small, medium and large. The perfomance of Batik Image similarity using this method about 90-92%.

Segmentation on the Dental Periapical X-Ray Images for Osteoporosis Screening

Segmentation on the trabecular of dental periapical X-Ray images is very important for osteoporosis screening. Existing methods do not perform well in segmenting the trabecular of dental periapical in X-Ray images due to the presence of large amount of spurious edges. This paper presents a combination of tophat-bothat filtering, histogram equalization contrasting and local adaptive thresholding approach for automatic segmentation of dental periapical in X-Ray images. The qualitative evaluation is done by a dentist and shows that the proposed segmentation algorithm performed well the porous of trabecular features of dental periapical. The quantitative evaluation used fuzzy classification based on neural network to classify these features. It were found accuracy rate to be 99,96% for training set and around 65% for testing set for a dataset of 60 subjects.

PLANT RECOGNITION USING STEREO LEAF IMAGE USING GRAY-LEVEL CO-OCCURRENCE MATRIX

Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recogni tion based on leaf characteristics. The leaf image- based plant recognition in view of different angles is a new challenge. In this study, the research on the p lant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision t heory, which can provide more detailed information of an object. One of the information that can be obtai ned is about the position of the object in its imag e with the background as well as of the camera. One of the ways used to obtain such information is to calcula te the disparity. However, this method will only tell the position of the object compared to other objects wi thout that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necess ary to improve the quality of resultant images. Furthermor e, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance a nd in the recognition of three plant varieties was 83.3%.

ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection

Application of Group Decision Support System (GDSS) can assist for delivering the decision of various opinions (preference) cancer detection based on the preferences of various expertise. In this paper we propose ELECTRE-Entropy for GDSS Modeling. We propose entropy weighting for each criteria under ELECTRE Method.ELECTRE is one method in Multi-Attribute Decision Making (MADM). Modeling of Group Decision Support Sytemapplyfor multi-criteria which the simulation data mutated genes that can cause cancer and solution recommended.

Development of Copeland Score Methods for Determine Group Decisions

Voting method requires to determine group decision of decision by each decision maker in group. Determination of decisions by group of decision maker requires voting methods. Copeland score is one of voting method that has been developed by previous researchers. This method does not accommodate the weight of the expertise and interests of each decision maker. This paper proposed the voting method using Copeland score with added weighting. The method has developed of considering the weight of the expertise and interests of the decision maker. The method accordance with the problems encountered of group decision making . Expertise and interests of decision makers are given weight based on their expertises of decision maker contribution of the problems faced by the group to determine the decision. Decision making is the selection process of various alternative actions that might be chosen through a specific mechanism to make the best decision. The decision maker is done in order to achieve certain goals or objectives for solving problems. Organizational leaders rarely can solve the problem alone. Committees, working teams, project teams and task forces were formed in many organizations is approach to problem solving by group. GDSS is a computer-based interactive system to facilitate the achievement solution of problem by a group of decision makers. That is consistent with the statement of Turban (2005): A group decision support system (GDSS) is as interactive components of the facilities based system that solution of semi structured or unstructured problems by a group of decision makers in unstructured nature. GDSS was developed to address challenges to the quality and effectiveness of decision-making is done by more than one person (group of people). Issues that need to be highlighted in decision-making by a group of people, among others, is the number of decision-makers, the time should be allocated, and the increase the existing participants. GDSS provides support in solving the problem by providing a setting that supports communication for members who joined the group. The problem solving is done by a group of people who are members of the GDSS who need a voting method to obtain a group decision. Copeland score method is one method of voting to earn wages, which is a joint decision-making. So far, existing methods Copeland score considers that all of the decision maker has the same weight, but sometimes the decision maker has a different weight in determining a joint decision. For it is necessary to develop a method of voting with respect to the weight of each decision maker based on the level of expertise and interests to the problem.

IMPROVED REAL-TIME FACE RECOGNITION BASED ON THREE LEVEL WAVELET DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS AND MAHALANOBIS DISTANCE

The development of research in the field of real-time face recognition is a study that is being developed in the last decade. Face recognition is used to identify person from an image or video. Recognition rate and computation time of real-time face recognition is one of the big challenges that must be developed. This study proposes a model of face recognition using the method of feature extraction by combining three level wavelet decomposition and Principal Component Analysis (PCA) and using the method of mahalanobis distance for the classification section (3WPCA-MD). A 3-level wavelet decomposition is used to decompose images by reducing the resolution used for those images. Using wavelet decomposition up to level 3 will produce an image with a very low resolution so as to reduce the value of the resulting computation time to be processed using PCA. Mahalanobis distance method is used to determine the degree of similarity among the features to produce a more optimal face recognition. Based on the results of experiments that have been done, they generated improved face recognition with high face recognition accuracy of up to 96% in average and produced faster computation results of face recognition if compared to ordinary PCA method. The average computation speed value obtained using the method of 3WPCA-MD was 5-7 milli-second (ms) for each face recognition process.

Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation

This research proposes a robust method for disc localization and cup segmentation that incorporates masking to avoid misclassifying areas as well as forming the structure of the cup based on edge detection. Our method has been evaluated using two fundus image datasets, namely: D-I and D-II comprising of 60 and 38 images, respectively. The proposed method of disc localization achieves an average

The use of video processing for quadrotor flight stability control monitoring

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