Patrick S. P. Wang

A new method of color image segmentation based on intensity and hue clustering

A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.

A comment on “a fast parallel algorithm for thinning digital patterns”

A fast parallel thinning algorithm for digital patterns is presented. This algorithm is an improved version of the algorithms introduced by Zhang and Suen [5] and Stefanelli and Rosenfeld [3]. An experiment using an Apple II and an Epson printer was conducted. The results show that the improved algorithm overcomes some of the disadvantages found in [5] by preserving necessary and essential structures for certain patterns which should not be deleted and maintains very fast speed, from about 1.5 to 2.3 times faster than the four-step and two-step methods described in [3] although the resulting skeletons look basically the same.

PERFORMANCE COMPARISONS OF FACIAL EXPRESSION RECOGNITION IN JAFFE DATABASE

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71% by using leave-one-out strategy and 94.13% by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256.

Automatic bankcheck processing

Automatic bankcheck processing - a new engineered system, G. Dimauro et al a prototype for Brazilian bankcheck recognition, L.L. Lee et al automatic reading of handwritten amounts on French checks, M. Leroux et al bankcheck recognition using cross validation between legal and courtesy amounts, G. Kim and V. Govindaraju an evolutionary approach to the use of neural networks in the segmentation of handwritten numerals, J.M. Westall and M.S. Narasimha an off line cursive handwritten word recognition system and its application to legal amount interpretation, K. Han and I.K. Sethi optimal order of Markov models applied to bankchecks, C. Olivier et al a cognitive approach to off-line signature verification, N.A. Murshed et al. (Part contents).

Image Pattern Recognition: Synthesis and Analysis in Biometrics

Analysis in Biometrics: A Statistical Model for Biometric Verification (S N Srihari & H Srinivasan) Force Field Feature Extraction for Ear Biometrics (D J Hurley) Behavior Biometrics for Online Computer User Monitoring (A A E Ahmed & I Traore) Synthesis in Biometrics: Introduction to Synthesis in Biometrics (S N Yanushkevich et al.) Local B-Spline Multiresolution with Example in Iris Synthesis and Volumetric Rendering (F F Samavati et al.) Computational Geometry and Biometrics: On the Path to Convergence (M L Gavrilova) Biometric Systems and Applications: Large-Scale Biometric Identification: Challenges and Solutions (N K Ratha et al.) Issues Involving the Human Biometric Sensor Interface (S J Elliott et al.) Signature Analysis, Verification and Synthesis in Pervasive Environments (D V Popel) and other papers.

EXTRACTING FACES AND FACIAL FEATURES FROM COLOR IMAGES

In this paper, we present image processing and pattern recognition techniques to extract human faces and facial features from color images. First, we segment a color image into skin and non-skin regions by a Gaussian skin-color model. Then, we apply mathematical morphology and region filling techniques for noise removal and hole filling. We determine whether a skin region is a face candidate by its size and shape. Principle component analysis (PCA) is used to verify face candidates. We create an ellipse model to locate eyes and mouths areas roughly, and apply the support vector machine (SVM) to classify them. Finally, we develop knowledge rules to verify eyes. Experimental results show that our algorithm achieves the accuracy rate of 96.7% in face detection and 90.0% in facial feature extraction.

Character segmentation techniques for handwritten text-a survey

The paper is a survey of techniques for segmenting images of handwritten text into individual characters. The topic is broken into two categories: segmentation and segmentation-recognition techniques. Several approaches to each are outlined, and each is analyzed for its relevance to printed, cursive, on-line and off-line input data.<<ETX>>

FACIAL BIOMETRICS USING NONTENSOR PRODUCT WAVELET AND 2D DISCRIMINANT TECHNIQUES

A new facial biometric scheme is proposed in this paper. Three steps are included. First, a new nontensor product bivariate wavelet is utilized to get different facial frequency components. Then a ...

NONITERATIVE 3D FACE RECONSTRUCTION BASED ON PHOTOMETRIC STEREO

3D face reconstruction is a popular area within the computer vision domain. 3D face reconstruction should ideally be achieved easily and cost-effectively, without requiring specialized equipment to estimate 3D shapes. As a result of this, many techniques for retrieving 3D shapes from 2D images have been proposed. In this paper, a novel method for 3D face reconstruction based on photometric stereo, which estimates the surface normal from shading information in multiple images, hence recovering the 3D shape of a face, is proposed. In order to overcome the problems of previous approaches related to prior-knowledge regarding lighting conditions and iterative algorithms, the exemplar is synthesized with known lighting conditions from at least three images, under arbitrary lighting conditions and using an illumination reference. Experiments in 3D face reconstruction were made by verifying the proposed approach using the illumination subset of the Max-Planck Institute face database and Yale face database B. Experimental results demonstrate that the proposed method is effective for 3D shape reconstruction of faces from 2D images.

Face Representation By Using Non-tensor Product Wavelets

This paper presents a new approach to represent face by using non-tensor product bivariate wavelet filters. A new non-tensor product bivariate wavelet filter banks with linear phase are constructed from the centrally symmetric matrices. Our investigations demonstrate that these filter banks have a matrix factorization and they are capable of representing facial features for recognition. The implementations of our algorithm are made of three parts: First, face images are represented by the lowest resolution sub-bands after 2-level new non-tensor product wavelet decomposition. Second, the principal component analysis (PCA) feature selection scheme is adopted to reduce the computational complexity of feature representation. Finally, support vector machines (SVM) is applied for classification. The experimental results show that our method is superior to other methods in terms of recognition accuracy and efficiency