Takio Kurita

Maximum likelihood thresholding based on population mixture models

Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of the conditional distribution in the population mixture model under the assumption of normal distributions with a common variance. It is also shown that Kittler and Illingworths thresholding, which minimizes a criterion related to the average classification error rate assuming normal distribution with different variances, is equivalent to the maximization of the likelihood of the joint distribution in the population mixture model. A multi-thresholding algorithm based on Dynamic Programming is also presented.

A sketch retrieval method for full color image database-query by visual example

Gives a basic idea and its fundamental algorithms of the visual interface for image database systems. The QVE (Query by Visual Example) accepts a sketch roughly drawn by a user to retrieve the original image and the similar images. The system evaluates the similarity between the rough sketch, i.e. a visual example, and each of the image data in the database automatically. The QVE interface is implemented and examined on an experimental electronic art gallery called ART MUSEUM. This paper also gives some experimental results and a current evaluation. The algorithms are quite effective for content based image retrieval.<<ETX>>

A face recognition method using higher order local autocorrelation and multivariate analysis

Proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions: first, higher order local autocorrelation features which are shift-invariant and additive are extracted from an input image; then those features are linearly combined on the basis of multivariate analysis methods so as to provide new effective features for face recognition in learning from examples.<<ETX>>

Jijo-2: an office robot that communicates and learns

Describes how the authors have combined speech recognition, dialogue management, and statistical learning procedures to develop Jijo-2; an office robot that can communicate with humans and learn about its environment.

An efficient agglomerative clustering algorithm using a heap

Abstract An efficient algorithm for agglomerative clustering is presented. The algorithm uses a heap in which distances of all pairs of clusters are stored. Then the nearest pair of clusters is given by the element of the root node of the binary tree corresponding to the heap. Updating the heap at each stage of the hierarchy is easily implemented by shifting up or down the elements of the heap along the path of the heap tree. The computation time of the algorithm is at most O(N2 log(N)) when N objects are going to be classified.

Complex autoregressive model for shape recognition

A complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane is presented. A fast algorithm to calculate complex autoregressive coefficients and complex PARCOR coefficients of the model is also shown. The coefficients are invariant to rotation around the origin and to choice of the starting point in tracing a boundary. It is possible to make them invariant to scale and translation. Experimental results that the complicated shapes like nonconvex boundaries can be recognized in high accuracy, even in the low-order model. It is seen that the complex PARCOR coefficients tend to provide more accurate classification than the complex AR coefficients. >

Selection of Histograms of Oriented Gradients Features for Pedestrian Detection

Histograms of Oriented Gradients (HOG) is one of the well-known features for object recognition. HOG features are calculated by taking orientation histograms of edge intensity in a local region. N.Dalal et al.proposed an object detection algorithm in which HOG features were extracted from all locations of a dense grid on a image region and the combined features are classified by using linear Support Vector Machine (SVM). In this paper, we employ HOG features extracted from all locations of a grid on the image as candidates of the feature vectors. Principal Component Analysis (PCA) is applied to these HOG feature vectors to obtain the score (PCA-HOG) vectors. Then a proper subset of PCA-HOG feature vectors is selected by using Stepwise Forward Selection (SFS) algorithm or Stepwise Backward Selection (SBS) algorithm to improve the generalization performance. The selected PCA-HOG feature vectors are used as an input of linear SVM to classify the given input into pedestrian/non-pedestrian. The improvement of the recognition rates are confirmed through experiments using MIT pedestrian dataset.

Learning of personal visual impression for image database systems

Visual impression may differ with each person. User-friendly interfaces for image database systems require special retrieval methods which can adapt to the visual impression of each user. Algorithms for learning personal visual impressions of visual objects are described. The algorithms are based on multivariate data analysis methods. These algorithms provide a model on visual perception process of each user from a small set of training examples. This model is referred to as a personal index to retrieve desired images for the user. These algorithms were implemented and examined in a graphical symbol database system called TRADEMARK and a full color image database called ART MUSEUM.<<ETX>>

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system

BackgroundEarly diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD.MethodsWe employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing.ResultsThe sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively.ConclusionOur results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.

A method of block truncation coding for color image compression

A basic color block truncation coding (CBTC) algorithm for color image compression is described. A modification of the algorithm that reduces truncation errors is also described. The block statistics related to CBTC methods are investigated. Some experimental results are given for a 256-*256-pixel color image with 24 b/pixel. >