Presentation

Computational Classification of Texture Contents in the Shitsukan Research Database

November 2019

Conference: The 26th International Display Workshops (IDW'19)
At: Sapporo Convention Center, Sapporo, Japan
Affiliation: Tokyo University of Science

Authors:

Kanazawa Gakuin University

HEVC Image Evaluation and Analysis between Contrast and Rotation Based on Material Data Set of the Shitsukan Perception Standard Problem

Article

Oct 2024

Norifumi Kawabata

ResearchGate has not been able to resolve any references for this publication.

Conference Paper

Hybrid Optical Digital Implementation Of Laws "Texture Energy Measure"

June 1986 · Proceedings of SPIE - The International Society for Optical Engineering

K.I. Laws proposed a texture analysis method called "Texture energy measure", yieldina good classification results. We show that this method maybe implemented optically using birefringent crystals and polarisers. This optical processor can replace the computer for the major part of the computation required in texture processing. The ontical version is particularly attractive due to its reasonable ... [Show full abstract] cost and its high efficiency that may be estimated in the neighbourhood of 1 G operations per second using presently available components. We present some results that show the feasability of the setup.

Conference Paper

A High-Speed Document Image Classifier.

January 1994

Lejun Shao

In this paper, a high-speed document image clas- sification algorithm is presented. The algorithm is based on the bottom-up strategy which can success- fully segment and classify any type of sophisticated layout documents without the limitation of Manhat- tan rules. Special techniques are employed to over- come the slow speed problem facing most of the bottom- up algorithms. During segmentation ... [Show full abstract] process, the al- gorithm used a byte-based operation to establish a list of connected components within a scan line, and to merge touching connected components in adjacent scan lines. The texture features of each connected component are also obtained during the process. This greatly reduced the computation time spent on clas- sification stage. For a typical A4 size document image with 2OODPI resolution, the average processing time on a Pentium based IBM-PC computer is 1.2 second.

Article

Fast image-data classifier

June 1990 · Proceedings of SPIE - The International Society for Optical Engineering

The classification based on the minimum distance classifier has been found to take lesser computing time than any of the maximum likelihood classifiers. An efficient algorithm for classifying image data based on the threshold distance from the 'means' of the classes is presented. The naive algorithm computes the distance of a pixel from every class mean and the pixel is classified to the ... [Show full abstract] nearest class. Bryant has reduced the number of computations by first calculating the distance of the pixel from the class, to which the previous pixel was assigned to, and truncating the computation of the distance from subsequent classes whenever the latter is greater. The computation of the distances from subsequent classes is avoided for most cases, in the algorithm presented, by finding the threshold distance for each class, i.e. if the pixel to be classified lies within the threshold distance of the class, it is classified to that class. The minimum of the distances of the class mean from all other class means is calculated. The threshold value of the class is half this minimum. This algorithm is computationally efficient.

Conference Paper

Semivariogram and SGLDM Methods Comparison for the Diagnosis of Solitary Lung Nodule

January 1970 · Lecture Notes in Computer Science

The present work seeks to develop a computational tool to suggest the malignancy or benignity of Solitary Lung Nodules by means of analyzing texture measures obtained from computerized tomography images.Two methods are proposed, that analyze the nodules’ texture by means of the Spatial Gray Level Dependence Method and a geostatistical function denominated semivariogram. A sample with 36 nodules, ... [Show full abstract] 29 benign and 7 malignant, was analyzed and the preliminary results of these methods are very promising in characterizing lung nodules. The obtained results suggested that the proposed methods have great potential in the discrimination and classification of Solitary Lung Nodules.

Chapter

Full-text available

New approach of higher order textural parameters for image classification using statistical methods

January 2008

Many researchers have demonstrated that textural data increase the precision of image classification when they are combined with grey level information. However, textural parameters of order two take too long computation time. The problem is more complex when one must compute higher order textural parameters, which however can considerably improve the precision of a classification. In this work, ... [Show full abstract] we propose a new formulation for the calculation of statistical textural parameters. The principle consists in reducing the calculation of a n-summation of type \sum^{L-1}_{i_{0}=0}\sum^{L-1}_{i_{1}=0}\sum^{L-1}_{i_{2}=0} \cdots\sum^{L-1}_{i_{n-1}=0}\psi[i_{0},i_{1},\cdots,i_{(n-1)},P_{i_{0},i_{1},\cdots i_{n-1}}]

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Conference Paper

Texture Classification Using Dominant Wavelet Packet Energy Features.

January 2000

This paper proposes a high performance texture classification method using dominant energy features from wavelet packet decomposition. We decompose the texture images with a family of real orthonormal wavelet bases and compute the energy signatures using the wavelet packet coefficients. Then we select a few of the most dominant energy values as features and employ a Mahalanobis distance ... [Show full abstract] classifier to classify a set of distinct natural textures selected from the Brodatz album. In our experiments, the proposed method employed a reduced feature set and involved less computation in classification time while still archiving high accuracy rate (94.8%) for classifying twenty classes of natural texture images

Article

Full-text available

Robust Eigenspace Construction

January 1970

In this paper we extend our previous work on constructing multiple eigenspaces from a set of training im- ages based on the Minimum Description Length (MDL) prin- ciple. To enhance the robustness we propose a robust frame- work for calculating the eigenspaces. We also propose a mechanism for incrementally adding new eigenspaces to in- crease the computational efficiency.We illustrate the robust ... [Show full abstract] performance of the new algorithm on 2D point data, and demonstrate the ability to form object specific eigenspaces which enhances the classification accuracy on the ORL face recognition database.

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Chapter

Computational Classification of Texture Contents in the Shitsukan Research Database

November 2019 · Proceedings of the International Display Workshops

Norifumi Kawabata

Article

Directional statistical Gabor features for texture classification

May 2018 · Pattern Recognition Letters

In texture classification, methods using multi-resolution directional (MRD) filters such as Gabor have not often shown significantly better performance than simple methods using local binary patterns, although they have a robust theoretical background and high computational complexity. We expect that this is because such methods usually make use of only the modulus parts of complex-valued ... [Show full abstract] MRD-filtered images and do not fully utilize their phase parts and other directional information. This letter presents a rotation-invariant feature using four types of directional statistics obtained from both the modulus and phase parts of Gabor-filtered images. First, modulus statistics, scale-shift cross-correlations, and orientation-shift cross-correlations are computed over all directions for each pixel of Gabor-filtered images, and global autocorrelations are computed over all pixels of each Gabor-filtered image. Global means and standard deviations for the three types of directional statistics and directional means and standard deviations for the global autocorrelations are then computed to form a feature vector. Experimental results with Brodatz, STex, CUReT, KTH-TIPS, UIUC, UMD, ALOT, and Kylberg databases show that the proposed method yields excellent performance compared with several conventional methods.

Article

Full-text available

New Formulation of Textural Parameters of Order n>1

January 1970

Many researchers have demonstrated that textural data increase the precision of image classification when they are combined with grey level information. However, textural parameters of order two take too long computation time. The problem is more complex when one must compute higher order textural parameters, which however can considerably improve the precision of a classification. In this work, ... [Show full abstract] we propose a new formulation for the calculation of statistical textural parameters. The principle consists in reducing the calculation of a n-summation of type , generally utilized in the evaluation of

View full-text

Article

Structure and Complexity of Relational Queries

December 2002

Ashok Chandra

This paper is an attempt at laying the foundations for the classification of queries on relational data bases according to their structure and their computational complexity. Using the operations of composition and fixpoints, a Z--// hierarchy of height w 2, called the fixpoint query hierarchy, is defined, and its properties investigated. The hierarchy includes most of the queries considered in ... [Show full abstract] the literathre including those of Codd and Aho and Ullman

Preprint

Score-Based Explanations in Data Management and Machine Learning

July 2020

Leopoldo Bertossi

We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual ... [Show full abstract] nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.

Computational Classification of Texture Contents in the Shitsukan Research Database

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