Textural Features for Image Classification
01 Nov 1973-Vol. 3, Iss: 6, pp 610-621
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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TL;DR: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends to discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
Abstract: The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
5,318 citations
Cites background from "Textural Features for Image Classif..."
...There are various texture descriptors: Gray-Level Cooccurrence Matrices (GLCM s) [Haralick et al. 1973] (a 2D histogram which shows the cooccurrences of intensities in a speci.ed direction and distance), Law s texture measures [Laws 1980] (twenty-.ve 2D .lters generated from .ve 1D .lters…...
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01 Jan 1979
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
5,112 citations
TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Abstract: In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
4,773 citations
TL;DR: The first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler is described, which can address a variety of biological questions quantitatively.
Abstract: Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
4,578 citations
Cites background from "Textural Features for Image Classif..."
...CellProfiler measures a large number of features for each identified cell or subcellular compartment, including area, shape, intensity, and texture (each feature is described in Additional data file 4). This includes many standard features [39,40], but also complex measurements like Zernike shape features [41], and Haralick and Gabor texture features [ 42- 44 ], which are described in detail in the online help and manual....
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TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
3,894 citations
Cites methods from "Textural Features for Image Classif..."
...The texture are computed using second-order statistical features (SGLD) [59] on subimages of 16 16 pixels....
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...Dai and Nakano also applied SGLD model to face detection [32]....
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References
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TL;DR: Informational considerations lead to a natural generalization of the classical correlation coefficient of a normal distribution and the generalized coefficient, here called the informational coefficient of correlation, is a function of the joint probability density distribution p.
Abstract: Summary Informational considerations lead to a natural generalization of the classical correlation coefficient of a normal distribution. The generalized coefficient, here called the informational coefficient of correlation , is a function of the joint probability density distribution p ( x, y ) of the two variables x and y , is invariant under a change of parameterization x′ = f ( x ), y′ = g ( y ), and reduces to the classical correlation coefficient when p ( x, y ) is normal.
207 citations
TL;DR: Several preprocessing techniques for enhancing selected features and removing irrelevant data are described and compared and a practical image pattern recognition problem is solved using some of the described techniques.
Abstract: Feature extraction is one of the more difficult steps in image pattern recognition. Some sources of difficulty are the presence of irrelevant information and the relativity of a feature set to a particular application. Several preprocessing techniques for enhancing selected features and removing irrelevant data are described and compared. The techniques include gray level distribution linearization, digital spatial filtering, contrast enhancement, and image subtraction. Also, several feature extraction techniques are illustrated. The techniques are divided into spatial and Fourier domain operations. The spatial domain operations of directional signatures and contour tracing are first described. Then, the Fourier domain techniques of frequency signatures and template matching are illustrated. Finally, a practical image pattern recognition problem is solved using some of the described techniques.
207 citations
01 Jun 1970
TL;DR: In this article, differentiating between the coarsenesses of samples of a given texture may be successfully effected using any of the following measures: (1) amount of edge per unit area, (2) self-match (as measured by sum of absolute differences) over a unit shift, (3) Gray value dependency, and (4) number of relative extrema per area.
Abstract: Differentiation between the coarsenesses of samples of a given texture may be successfully effected using any of the following measures: (1) Amount of edge per unit area, (2) Self-match (as measured by sum of absolute differences) over a unit shift, (3) Gray value dependency, and (4) Number of relative extrema per unit area.
161 citations
146 citations
TL;DR: In this paper, a procedure is developed to extract numerical features which characterize the pore structure of reservoir rocks, based on a set of descriptors which give a statistical description of porous media, and a simple identification rule using piecewise linear discriminant functions is developed for categorizing the photomicrograph images.
Abstract: A procedure is developed to extract numerical features which characterize the pore structure of reservoir rocks. The procedure is based on a set of descriptors which give a statistical description of porous media. These features are evaluated from digitized photomicrographs of reservoir rocks and they characterize the rock grain structure in term of (1) the linear dependency of grey tones in the photomicrograph image, (2) the degree of "homogeneity" of the image and (3) the angular variations of the image grey tone dependencies. On the basis of these textural features, a simple identification rule using piecewise linear discriminant functions is developed for categorizing the photomicrograph images. The procedure was applied to a set of 243 distinct images comprising 6 distinct rock categories. The coefficients of the discriminant functions were obtained using 143 training samples. The remaining (100) samples were then processed, each sample being assigned to one of 6 possible sandstone categories. Eighty-nine per cent of the test samples were correctly identified.
109 citations
"Textural Features for Image Classif..." refers background in this paper
...Forfurther details theinterested reader isreferred to[16]-[ 18 ]....
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