Showing papers in "The Kips Transactions:partb in 2010"

An Implementation of Gaze Recognition System Based on SVM

Abstract: The researches about gaze recognition which current user gazes and finds the location have increasingly developed to have many application. The gaze recognition of existence all about researches have got problems because of using equipment that Infrared(IR) LED, IR camera and head-mounted of high price. This study propose and implement the gaze recognition system based on SVM using a single PC Web camera. The proposed system that divide the gaze location of 36 per 9 and 4 to recognize gaze location of 4 direction and 9 direction recognize user's gaze. Also, the proposed system had apply on image filtering method using difference image entropy to improve performance of gaze recognition. The propose system was implements experiments on the comparison of proposed difference image entropy gaze recognition system, gaze recognition system using eye corner and eye's center and gaze recognition system based on PCA to evaluate performance of proposed system. The experimental results, recognition rate of 4 direction was 94.42% and 9 direction was 81.33% for the gaze recognition system based on proposed SVM. 4 direction was 95.37% and 9 direction was 82.25%, when image filtering method using difference image entropy implemented. The experimental results proved the high performance better than existed gaze recognition system.Keywords:Support Vector Machine : SVM, Gaze Recognition, Difference Image Entropy

Spam Filter by Using X 2 Statistics and Support Vector Machines

Abstract: We propose an automatic spam filter for e-mail data using Support Vector Machines(SVM). We use a lexical form of a word and its part of speech(POS) tags as features and select features by chi square statistics. We represent each feature by TF(text frequency), TF-IDF, and binary weight for experiments. After training SVM with the selected features, SVM classifies each e-mail as spam or not. In experiment, the selected features improve the performance of our system and we acquired overall 98.9% of accuracy with TREC05-p1 spam corpus.

High-Capacity Reversible Watermarking through Predicted Error Expansion and Error Estimation Compensation

Abstract: Reversible watermarking which can preserve the original quality of the digital contents and protect the copyright has been studied actively. Especially, in medical, military, and art fields, the need for reversible watermarking is increasing. This paper proposes a high-capacity reversible watermarking through predicted error expansion and error estimation compensation. Watermark is embedded by expanding the difference histogram between the original value and the predicted value. Differently from previous methods calculating the difference between adjacent pixels, the presented method calculates the difference between the original value and the predicted value, and that increases the number of the histogram value, where the watermark is embedded. As a result, the high capacity is achieved. The inserted watermark is extracted by restoring the histogram between the original value and the predicted value. To prove the performance, the presented algorithm is compared with other previous methods on various test images. The result supports that the presented algorithm has a perfect reversibility, a high image quality, and a high capacity.

Traffic Light and Speed Sign Recognition by using Hierarchical Application of Color Segmentation and Object Feature Information

Abstract: A method of the region extraction and recognition of a traffic light and speed sign board in the real road environment is proposed. Traffic light was recognized by using brightness and color information based on HSI color model. Speed sign board was extracted by measuring red intensity from the HSI color information We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. The proposed algorithm shows a robust recognition rate in the image sequence which includes traffic light and speed sign board.Keywords:Color Segmentation, Sign Recognition, Light Recognition, Template Matching 1. 서 론 * 오늘날 자동차의 보유량은 급격하게 증가하고 있고 이것에 비례하여 교통사고 역시 급격하게 증가하고 있다. 교통사고의 원인으로는 교통 혼잡, 운전자의 노령화, 운전자 계층의 다양화 등이 있다. 과거에는 교통사고 방지를 운전자 개인의 능력에만 의존했다면 현대에는 자동차와 기계, 전자, 통신, 제어, 인공지능 등 각종 첨단 기술을 접목시켜 운전자에게 부담을 덜어주어 운전 미숙에 대한 교통사고를 방지하기 위한 연구인 지능형 교통 시스템(ITS:Intelligent Trans-portation Systems)이 활발히 진행되고 있다. 이러한 지능형 교통 시스템 서비스 중 AVHS(Advanced Vehicle and Highway System)란 서비스는 차량에 교통상황, 장애물 인

Detection of Music Mood for Context-aware Music Recommendation

Abstract: To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people’s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time.To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.Keywords:Context-aware Music Recommendation; Musical Genre Classification; Musical Structure Analysis; Salient Segment Detection; Content-based Musical Feature Extraction

Color Laser Printer Identification through Discrete Wavelet Transform and Gray Level Co-occurrence Matrix

Abstract: High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.Keywords:Digital Forensics, Discrete Wavelet Transform, Gray Level Co-occurrence Matrix, Support Vector Machine Classifier

Product Evaluation Summarization Through Linguistic Analysis of Product Reviews

Abstract: In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like `design` and `material` for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.

Example-based Super Resolution Text Image Reconstruction Using Image Observation Model

Abstract: Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.

An Easy Camera-Projector Calibration Technique for Structured Light 3-D Reconstruction

Abstract: The structured-light 3D reconstruction technique uses a coded-pattern to find correspondences between the camera image and the projector image. To calculate the 3D coordinates of the correspondences, it is necessary to calibrate the camera and the projector. In addition, the calibration results affect the accuracy of the 3D reconstruction. Conventional camera-projector calibration techniques commonly require either expensive hardware rigs or complex algorithm. In this paper, we propose an easy camera-projector calibration technique. The proposed technique does not need any hardware rig or complex algorithm. Thus it will enhance the efficiency of structured-light 3D reconstruction. We present two camera-projector systems to show the calibration results. Error analysis on the two systems are done based on the projection error of the camera and the projector, and 3D reconstruction of world reference points.

Model based Facial Expression Recognition using New Feature Space

Abstract: This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.Keywords:Facial Expression Recognition, Features Space Generation, Wrapper Approach, Multi-Tier Cross Validation

A Method of Optimal Sensor Decision for Odor Recognition

Abstract: In this paper, we propose method of correlation coefficients between sensors by statistical analysis that selects optimal sensors in odor recognition system of selective multi-sensors. The proposed sensor decision method obtains odor data from Metal Oxide Semiconductor(MOS) sensor array and then, we decide optimal sensors based on correlation of obtained odors. First of all, we select total number of 16 sensors eliminated sensor of low response and low reaction rate response among similar sensors. We make up DB using 16 sensors from input odor and we select sensor of low correlation after calculated correlation coefficient of each sensor. Selected sensors eliminate similar sensors' response therefore proposed method are able to decide optimal sensors. We applied to floral scent recognition for performance evaluation of proposed sensors decision method. As a result, application of proposed method with floral scent recognition using correlation coefficient obtained recognition rate of 95.67% case of using 16 sensors while applied floral scent recognition system of proposed sensor decision method confirmed recognition rate of 94.67% using six sensors and 96% using only 8 sensors.Keywords:Sensors decision method, Correlation Coefficient, Floral scent Recognition

Automatic Matching of Protein Spots by Reflecting Their Topology

Abstract: Matching spots between two sets of 2-dimensional electrophoresis can make it possible to find out the generation, extinction and change of proteins. Generally protein spots are separated by 2-dimensional electrophoresis. This process makes the position of the same protein spot a little different according to the status of the tissue or the experimental environment. Matching the spots shows that the relation of spots is non-uniform and non-linear transformation. However we can also find that the local relation preserves the topology. This study proposes a matching method motivated by the preservation of the topology. To compare the similarity of the topology, we compared the distance and the angle between neighbour spots. Experimental result shows that the proposed method is effective.Keywords:Spot Matching, Protein, Topology, Electrophoresis 1. 서 론 1) 단백질 연구의 첫 단계는 단백질을 분리하는 것이다. 그 후에 분리한 단백질이 무엇인지를 밝히고, 이어서 그 효과를 결정한다. 세포나 조직에서 단백질을 분리하는 방법으로 2차원 젤 전기영동을 이용한다[1, 2, 25]. 이 방법은 단백질의 두 가지 독립적인 특성인 질량과 전하량을 이용한다[3]. 즉, 질량을 이용하여 수직방향으로 분리하고, 전하량을 이용하여 수평방향으로 단백질을 분리한다. 이 방법을 이용하여 한 번의 분석으로 약 수 백에서 수 천 개의 서로 다른 단백질을 분리할 수 있다. 분리된 단백질은 염료나 은 화합물

An Ant Colony Optimization Heuristic to solve the VRP with Time Window

Abstract: The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

Selective Mutation for Performance Improvement of Genetic Algorithms

Abstract: Since the premature convergence phenomenon of genetic algorithms (GAs) degrades the performances of GAs significantly, solving this problem provides a lot of effects to the performances of GAs. In this paper, we propose a selective mutation method in order to improve the performances of GAs by alleviating this phenomenon. In the selective mutation, individuals are additionally mutated at the specific region according to their ranks. From this selective mutation, individuals with low ranks are changed a lot and those with high ranks are changed small in the phenotype. Finally, some good individuals search around them in detail and the other individuals have more chances to search new areas. This results in enhancing the performances of GAs through alleviating of the premature convergence phenomenon. We measured the performances of our method with four typical function optimization problems. It was found from experiments that our proposed method considerably improved the performances of GAs.

Baleen Whale Sound Synthesis using a Modified Spectral Modeling

Abstract: Spectral modeling synthesis (SMS) has been used as a powerful tool for musical sound modeling. This technique considers a sound as a combination of a deterministic plus a stochastic component. The deterministic component is represented by the series of sinusoids that are described by amplitude, frequency, and phase functions and the stochastic component is represented by a series of magnitude spectrum envelopes that functions as a time varying filter excited by white noise. These representations make it possible for a synthesized sound to attain all the perceptual characteristics of the original sound. However, sometimes considerable phase variations occur in the deterministic component by using the conventional SMS for the complex sound such as whale sounds when the partial frequencies in successive frames differ. This is because it utilizes the calculated phase to synthesize deterministic component of the sound. As a result, it does not provide a good spectrum matching between original and synthesized spectrum in higher frequency region. To overcome this problem, we propose a modified SMS that provides good spectrum matching of original and synthesized sound by calculating complex residual spectrum in frequency domain and utilizing original phase information to synthesize the deterministic component of the sound. Analysis and simulation results for synthesizing whale sounds suggest that the proposed method is comparable to the conventional SMS in both time and frequency domain. However, the proposed method outperforms the SMS in better spectrum matching.

Constructing 3D Outlines of Objects based on Feature Points using Monocular Camera

Abstract: This paper presents a method to extract 3D outlines of objects in an image obtained from a monocular vision. After detecting the general outlines of the object by MOPS(Multi-Scale Oriented Patches) -algorithm and we obtain their spatial coordinates. Simultaneously, it obtains the space-coordinates with feature points to be immanent within the outlines of objects through SIFT(Scale Invariant Feature Transform)-algorithm. It grasps a form of objects to join the space-coordinates of outlines and SIFT feature points. The method which is proposed in this paper, it forms general outlines of objects, so that it enables a rapid calculation, and also it has the advantage capable of collecting a detailed data because it supplies the internal-data of outlines through SIFT feature points.

Robust AAM-based Face Tracking with Occlusion Using SIFT Features

Abstract: Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features

Abstract: This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.

Question Classification Based on Word Association for Question and Answer Archives

Abstract: Word mismatch is the most significant problem that causes low performance in question classification, whose questions consist of only two or three words that expressed in many different ways. So, it is necessary to apply word association in question classification. In this paper, we propose question classification method using translation-based language model, which use word translation probabilities for question-question pair that is learned in the same category. In the experiment, we prove that translation probabilities of question-question pairs in the same category is more effective than question-answer pairs in total collection.

Real Time Lip Reading System Implementation in Embedded Environment

Abstract: This paper proposes the real time lip reading method in the embedded environment. The embedded environment has the limited sources to use compared to existing PC environment, so it is hard to drive the lip reading system with existing PC environment in the embedded environment in real time. To solve the problem, this paper suggests detection methods of lip region, feature extraction of lips, and awareness methods of phonetic words suitable to the embedded environment. First, it detects the face region by using face color information to find out the accurate lip region and then detects the exact lip region by finding the position of both eyes from the detected face region and using the geometric relations. To detect strong features of lighting variables by the changing surroundings, histogram matching, lip folding, and RASTA filter were applied, and the properties extracted by using the principal component analysis(PCA) were used for recognition. The result of the test has shown the processing speed between 1.15 and 2.35 sec. according to vocalizations in the embedded environment of CPU 806Mhz, RAM 128MB specifications and obtained 77% of recognition as 139 among 180 words were recognized.

Two-Phase Shallow Semantic Parsing based on Partial Syntactic Parsing

Abstract: A shallow semantic parsing system analyzes the relationship that a syntactic constituent of the sentence has with a predicate. It identifies semantic arguments representing agent, patient, instrument, etc. of the predicate. In this study, we propose a two-phase shallow semantic parsing model which consists of the identification phase and the classification phase. We first find the boundary of semantic arguments from partial syntactic parsing results, and then assign appropriate semantic roles to the identified semantic arguments. By taking the sequential two-phase approach, we can alleviate the unbalanced class distribution problem, and select the features appropriate for each task. Experiments show the relative contribution of each phase on the test data.

A Novel Feature Map Generation and Integration Method for Attention Based Visual Information Processing System using Disparity of a Stereo Pair of Images

Abstract: Human visual attention system has a remarkable ability to interpret complex scenes with the ease and simplicity by selecting or focusing on a small region of visual field without scanning the whole images. In this paper, a novel feature map generation and integration method for attention based visual information processing system is proposed. The depth information obtained from a stereo pair of images is exploited as one of spatial visual features to form a set of topographic feature maps in our approach. Comparative experiments show that correct detection rate of visual attention regions improves by utilizing depth feature compared to the case of not using depth feature.

Active Contour Model for Boundary Detection of Multiple Objects

Abstract: Most of previous algorithms of object boundary extraction have been studied for extracting the boundary of single object. However, multiple objects are much common in the real image. The proposed algorithm of extracting the boundary of each of multiple objects has two steps. In the first step, we propose the fast method using the outer and inner products; the initial contour including multiple objects is split and connected and each of new contours includes only one object. In the second step, an improved active contour model is studied to extract the boundary of each object included each of contours. Experimental results with various test images have shown that our algorithm produces much better results than the previous algorithmsKey words : Snake, Active Contour Model, Multiple Objects, Boundary Extraction, Highly Irregular Boundary, Splitting and Connecting of Snake Points 1. 서 론 1) 객체윤곽 추출은 내용기반 검출시스템 및 대화형 멀티미디어시스템에서 매우 중요하다[1, 2]. 이러한 시스템을 사용하여 서비스를 성공적으로 제공하기 위해서는 영상질의를 하는데 기본 정보로 객체 모양을 사용한다. 실 영상에서는 단일객체 보다는 복수 객체가 일반적이고 복수객체 윤곽을 효율적으로 추출하면 활용되는 분야가 다양화 될 것으로 기대된다. 복수 객체의 윤곽추출의 일반적인 방법은 먼저 복수객체를 단일객체로 분리하고, 그 다음 단일 객체의 모양 즉 윤곽을 추출하여야 한다.복수객체의 윤곽추출에는 여러 방법이 제안되었다. 예를 들면, 영역분할이나, Watershed 등의 알고리즘이 제안되었

Multiple-Classifier Combination based on Image Degradation Model for Low-Quality Image Recognition

Abstract: In this paper, we propose a multiple classifier combination method based on image degradation modeling to improve recognition performance on low-quality images. Using an image degradation model, it generates a set of classifiers each of which is specialized for a specific image quality. In recognition, it combines the results of the recognizers by weighted averaging to decide the final result. At this time, the weight of each recognizer is dynamically decided from the estimated quality of the input image. It assigns large weight to the recognizer specialized to the estimated quality of the input image, but small weight to other recognizers. As the result, it can effectively adapt to image quality variation. Moreover, being a multiple-classifier system, it shows more reliable performance then the single-classifier system on low-quality images. In the experiment, the proposed multiple-classifier combination method achieved higher recognition rate than multiple-classifier combination systems not considering the image quality or single classifier systems considering the image quality.

Automatic Method for Extracting Homogeneity Threshold and Segmenting Homogeneous Regions in Image

Abstract: In this paper, we propose the method for extracting Homogeneity Threshold() and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with . The is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu`s single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum() of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute . To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds() that is added a coefficient for adjusting scope of . We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.

A Multiresolution Stereo Matching Based on Genetic Algorithm using Edge Information

Abstract: In this paper, we propose a multiresolution stereo matching method based on genetic algorithm using edge information. The proposed approach considers the matching environment as an optimization problem and finds the solution by using a genetic algorithm. A cost function composes of certain constraints which are commonly used in stereo matching. We defines the structure of chromosomes using edge pixel information of reference image of stereo pair. To increase the efficiency of process, we apply image pyramid method to stereo matching and calculate the initial disparity map at the coarsest resolution. Then initial disparity map is propagated to the next finer resolution, interpolated and performed disparity refinement. We valid our approach not only reduce the search time for correspondence but alse ensure the validity of matching.

Anaphora Resolution System for Natural Language Requirements Document in Korean based on Syntactic Structure

Abstract: When a system is developed, requirements document is generated by requirement analysts and then translated to formal specifications by specifiers. If a formal specification can be generated automatically from a natural language requirements document, system development cost and system fault from experts' misunderstanding will be decreased. A pronoun can be classified in personal and demonstrative pronoun. In the characteristics of requirements document, the personal pronouns are almost not occurred, so we focused on the decision of antecedent for a demonstrative pronoun. For the higher accuracy in analysis of requirements document automatically, finding antecedent of demonstrative pronoun is very important for elicitation of formal requirements automatically from natural language requirements document via natural language processing. The final goal of this research is to automatically generate formal specifications from natural language requirements document. For this, this paper, based on previous research [3], proposes an anaphora resolution system to decide antecedent of pronoun using natural language processing from natural language requirements document in Korean. This paper proposes heuristic rules for the system implementation. By experiments, we got 92.45%, 69.98% as recall and precision respectively with ten requirements documents.Keywords:Anaphora Resolution, Antecedent Decision, Natural Language Processing, Requirements Elicitation

2D Planar Object Tracking using Improved Chamfer Matching Likelihood

Abstract: In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

Gradient Descent Approach for Value-Based Weighting

Abstract: Naive Bayesian learning has been widely used in many data mining applications, and it performs surprisingly well on many applications. However, due to the assumption that all attributes are equally important in naive Bayesian learning, the posterior probabilities estimated by naive Bayesian are sometimes poor. In this paper, we propose more fine-grained weighting methods, called value weighting, in the context of naive Bayesian learning. While the current weighting methods assign a weight to each attribute, we assign a weight to each attribute value. We investigate how the proposed value weighting effects the performance of naive Bayesian learning. We develop new methods, using gradient descent method, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general Naive bayesian, and the value weighting method showed better in most cases.