H. Tamura, S. Mori, and Y. Yammawaki, "Textural features corresponding to visual perception," IEEE Transactions on Systems, Man, Cybernetics, vol. 8, pp. 460--473, June 1978.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....histograms with WC produce good overall performance. 2. RELATED WORK Commonly used texture features can be divided into two main categories: statistical and spectral. Statistical features characterize textures in terms of local statistical measures (such as coarseness, directionality, contrast [2]) multiresolutional simultaneous autoregressive model (MRSAR) 3] and Markov random field (MRF) 4] Among them, the features of Tamura et al. 2] are used in QBIC [5] and MRSAR is used in PhotoBook [6] The spectral approach is based on the response of a set of band pass filters, typically 2D ....

....and spectral. Statistical features characterize textures in terms of local statistical measures (such as coarseness, directionality, contrast [2] multiresolutional simultaneous autoregressive model (MRSAR) 3] and Markov random field (MRF) 4] Among them, the features of Tamura et al. [2] are used in QBIC [5] and MRSAR is used in PhotoBook [6] The spectral approach is based on the response of a set of band pass filters, typically 2D Gabor [7] and wavelets [6, 7] Each filter responds most strongly to the patterns at a specific spatial frequency and orientation band. These ....

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Trans. SMC, vol. 8, no. 6, pp. 460--47, 1978.

....using a subjective, linguistic or human like texture description. The FI module enters this description into the system using a fuzzy representation of it. The Q2TPT module interprets the query and translates it into a quantitative texture description that is implemented using Tamura Descriptors [7]. This quantitative description is used by the TR module to search the texture in the database (see description in [5] In the case that the texture is not found in the database, the user can choose the automatic generation of it. The TG module generates the texture using Markov Random Fields ....

....used in Survey I. 3.2. Survey H The purpose of this survey was to investigate the most relevant and most frequently human used characteristics for the qualitative description of textures. Supported on characteristics derived from Survey I and on knowledge obtained from technical literature [1, 4, 7], a list of adjective pairs was composed (see Table 1) Of course, the selection of the adjectives was subjective and thus potentially dangerous. Nevertheless, to us this procedure seemed to be the most appropriate to begin with. The adjective pairs 4 ( tasty insipid ) 7 ( fragile robust ) 14 ....

H. Tamura, S. Mori, and T. Yamawaki, Textural features corresponding to visual perception, IEEE Trans. on Sys., Man and Cyb., SMC - 8, no. 6, pp. 460 - 472, 1978.

....features used by humans in distinguishing color patterns. This is consistent with the conclusions from the experimental studies conducted by Rao and Lohse for gray level textures [11] as well as with the set of features corresponding to the perceptional criteria detected by Tamura et al. [10]. Repetitiveness can be modeled by a primitive element and placement rules that specify how this element is to be replicated [1] The feature of repetitiveness is correlated with regularity, uniformity Fig. 5. To confirm and refine the rules following rule of equal pattern, we ....

.... [21] A similar concept can be explored within any orientation sensitive multiresolution decompositions, such as wavelet and Gabor transforms [22] 23] or decomposition with steereable filters [24] Texture directionality and regularity are also among features extracted in Tamura s representation [10]motivatedbypscychophysiologicalstudies in humanperceptionof texture. ThismakesTamura srepresentation very attractive for practical applications involving human interaction or human understanding of texture. This representation is further improved and implemented in the QBIC [25] and MARS image ....

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Trans. Syst., Man, Cybern., vol. SMC-8, pp. 460--473, 1978.

.... content based image search engine with profound effects on later systems was QBIC [2] As color representation, this system uses a k element histogram and average of (R; G; B) Y; i; q) and (L; a; b) coordinates, whereas for the description of texture it implements Tamura s feature set [3]. In a similar fashion, color, texture, and shape are supported as a set of interactive tools for browsing and searching images in the Photobook system developed at the MIT Media Lab [4] In addition to these elementary features, systems such as VisualSeek [5] Netra [6] and Virage [7] support ....

....of color patterns have not yet been identified, a standardized set of features for addressing their important characteristics does not exist, nor are there rules defining how these features are to be combined. Previous investigations in this field concentrated mainly on gray level natural textures [3], 10] 11] Particularly interesting is work of Rao and Lohse [11] their research focused on how people classify textures in meaningful, hierarchically structured categories, identifying relevant features used in the perception of gray level textures. Similarly, here we determine the basic ....

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Trans. Syst., Man, Cybern.,, vol. 8, pp. 460--473, 1982.

....from the moving window. Various experiments were performed on images and the results proved to be well suited to compute the texture of images with both isotropic and non isotropic texture. Often, texture methods have been devised based on our understanding of how human vision works. Tamura et al.[204] proposed textural features corresponding to the visual perception. These are based on six basic textural properties, i.e. coarseness, contrast, directionality, linelikeliness, regularity, and roughness. The paper describes the psychological experiments on 54 basic textural properties and ....

H. Tamura, S. Mori and T. Yamawaki, Textural features corresponding to visual perception, IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460473, 1978.

....categories Ic whose variations humans are the most sensitive. However, how to translate Ihese categories into a reliable set of features, for both texture description and replicability test, is still a very complex prob lem. Therefore, starting from the previous studies and subjective experiments [2, 3, 4], we have selected a set of important visual features for human perception of textures. These latures are: directionality, symmetry, regu. a) b) Figure 1: a) Directional texture, b) regular texture, and (c) vertically symmetric texture. larity and type of regularity. FiguTe t shows three ....

H, Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Transactions Systems, Man and Cybernetics, 8:460473, 1982. 444

....a material. In the digital image, texture is depicted by the spatial interrelationships between, and or spatial arrangement of the image pixels. The most used set of texture features is Haralick s gray level cooccurrence features [10] Other often used texture measurements are (1) Tamura features [21]. He suggested six basic textural features: coarseness, contrast, directionality, line likeness, regularity, and roughness; 2) Unsers sum and difference histogram [22] He proposed 32 features based on calculations over different sums and histograms of the pixel gray levels; 3) Galloways ....

Tamura, H., Mori, S., and Yamawaki,, T., Textural Features Corresponding to Visual Perception, IEEE Transaction on Systems, Man, and Cybernetics, SMC-8 (1978), pp. 460-472.

....momentum, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, maximum correlation coefficient. Other often used texture measurements are (T) Tamura features [29]. He suggested six basic textural features; namely, coarseness, contrast, directionality, line likeness, regularity, and roughness; 2) Unser s sum and difference histogram [33] He proposed 32 features based on calculations over different sums and histograms of the pixel gray levels; 3) ....

Tamura, H., Mori, S., and Yamawaki,, T., Textural Features Corresponding to Visual Perception, 1EEE Transaction on Systems, Man, and Cybernetics, SMC-8 (1978), pp. 460-472.

....of coarseness, contrast, and directionality are used. The query is specified by selecting a texture from a texture sampler, which is a set of pre stored texture images. In VisualSEEk [29] and VideoQ [5] querying by texture feature is supported for images and video frames. The three Tamura [32] texture measures, coarseness, contrast and orientation, are computed as a textural measure of the texture content of the objects residing in images and video frames. In both of the systems, the images are uniformly quantized in HSV color space and Brodatz [2] texture set is used for assigning ....

H. Tamura and S. Mori. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), June 1978.

....precise analysis of anisotropy and regularity at arbitrary orientations. The extended co occurrence was proposed to decouple the magnitude and the angle of the displacement vector. Rao and Lohse [11] demonstrated that regularity plays an important role in human texture perception. Tamura et.al. [12] proposed a set of texture features, including regularity, that correspond to human perception. Their regularity measure is defined via the spatial variation of four other features, such as coarseness and directionality. However, the experimental study in [12] indicated poor correlation between ....

....texture perception. Tamura et.al. 12] proposed a set of texture features, including regularity, that correspond to human perception. Their regularity measure is defined via the spatial variation of four other features, such as coarseness and directionality. However, the experimental study in [12] indicated poor correlation between the proposed computational measure and the visually perceived regularity. D Astous and Jernigan [6] approach texture regularity using the characteristic frequencies of the power spectrum. A similar, power spectrum based view of regularity (periodicity) is ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. Systems, Man, and Cybernetics, 8:460--473, 1978.

....texture space (PTS) to provide the ground truth. Instead of constructing a PTS, it is also possible to measure human s perception of texture similarity by having human subjects rank textures according to subjectively prede ned properties such as directionality, coarseness, busyness, and etc. [2, 27] However, the subjects interpretations of the meaning of these visual properties are expected to vary from one person to the other. Therefore, it is uncertain whether the individual ranking results can be combined into group ranking results that represent the perception of a typical person. ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. SMC, 8(6):460-47, 1978.

.... similarity formula [15] For colors, we employ a variant of histogram based similarity formula that takes into account of perceptually similar colors [6, 17] The color computation is carried out in the CIE L u v color space [4] For texture attribute, we extend the texture measures developed in [16] for similarity computation [17] The similarity values computed at the leaf level are propagated up the concept hierarchy. There are three ways of propagating the similarity values depending on the types of operators used to combine the lower level concepts or attributes. For the OR operator , ....

Tamura H., Mori S. & Yamawaki T. (1978). Textural Features Corresponding to Visual Perception. IEEE Trans. Syst. Man., and Cybern, SMC-8, Jun, 460-472.

....appearance. Consequently, the computational model applied in image indexing should compute features that reflect these perceptual ones. To do so, the IBM QBIC system uses a modified version of the features of coarseness , contrast , and directionality proposed by Tamura for image indexing [76][21] Amadusun and King have proposed another feature set that corresponds to the visual properties of texture: coarseness , contrast , busyness , complexity , and texture strength [1] Picard and Liu, extending the work described in [24] 25] have proposed an indexing scheme based on Word ....

Tamura H., Mori S., Yamawaky T. Textural Features Corresponding to Visual Perception IEEE Transaction on Systems, Man and Cybernetics, Vol. SMC-8(6), pp. 460-473, 1972.

....with similar color intensity can have very different textures, the texture feature can often be used to distinguish between images with similar color distribution, such as sky and sea, or leaves and grass. Measures to compute texture similarity based on visually meaningful texture properties [TMY78] [LP96] that include contrast, coarseness, directionality, regularity, periodicity, and randomness, were used in current CBIR systems such as the QBIC system [NBE 93] and MARS system [HMR96] Other techniques for retrieval by texture include Wavelet transform [MM96] and fractals [KMN98] ....

H. Tamura, T. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Systems, Man, and Cybernetics, 8:460--473, June 1978.

....perception. Moreover, texture matching should be invariant to texture scale and orientation because the same texture can appear in the images in varying scales and orientations. Computational texture features commonly used for image retrieval include the statistical features of Tamura et al. [13] used in IBM s QBIC system, DFT in [12] and the Wold model [6] MRSAR in Wold model, Gabor features in [14] NeTra [9] and invariant texture space [5] Among these models, 5, 12, 14] are scale and orientation invariant. However, our studies [8] show that texture similarity computed based on ....

....in PTS is not confounded by these factors. 2.1. Perceptual Texture Space Several previous works attempted to measure human s perception of texture similarity by having human subjects rank textures along subjectively predefined properties such as directionality, coarseness, busyness, and etc. [13, 1] However, the subjects interpretations of the meaning of these visual properties are expected to vary from one person to the other. To appear in Int. Conf. Image Processing 2001. 1 Therefore, it is uncertain whether the individual ranking results can be combined into group ranking results that ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. SMC, 8(6):460--47, 1978.

....the first critical issue put forth in Section 1: development of perceptual texture space (PTS) First, let us briefly review existing perceptual texture models. 3. 1 Existing Perceptual Texture Models The earliest study of human s perception of texture similarity was conducted by Tamura et al. [Tamura et al. 1978] In their experiments, 48 human subjects were asked to judge the similarity of texture pairs according to six visual properties, namely, coarseness, contrast, directionality, line likeness, regularity, and roughness. Similarity judgments were measured and each texture was assigned a perceptual ....

....image retrieval systems. Statistical models categorize textures according to statistical measurements of visual qualities such as coarseness and granularity. Spectral models characterize textures based on Fourier spectrum or filtering results. The statistical features proposed by Tamura et al. [Tamura et al. 1978] are used in IBM s QBIC system. The Wold model [Liu and Picard, 1996] based on Fourier transform (spectral model) and Multiresolution Simultaneous Autoregressive model (MRSAR, statistical model) is used in MIT s PhotoBook. Gabor features (spectral model) are used in Ma and Manjunath s NeTra [Ma ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. SMC, 8(6):460--47, 1978.

....categories to whose variations humans are the most sensitive. However, how to translate these categories into a reliable set of features, for both texture description and replicability test, is still a very complex problem. Therefore, starting from the previous studies and subjective experiments [2, 3, 4], we have selected a set of important visual features for human perception of textures. These features are: directionality, symmetry, regu (a) b) c) Figure 1: a) Directional texture, b) regular texture, and (c) vertically symmetric texture. larity and type of regularity. Figure 1 shows ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Transactions Systems, Man and Cybernetics, 8:460--473, 1982.

....representative color in the quantized CIE LUV space. It is important to bear in mind that the quantization is not static, and the quantization palette changes with each video shot. The quantization is calculated anew for each sequence with the help of a self organizing map. Texture: Three Tamura [35] texture measures, coarseness, contrast, and orientation, are computed as a measure of the textural content of the object. Fig. 6. Region projection and segmentation of frame n. Motion: The motion of the video object is stored as a list of vectors (where the number of frames in the video is ) ....

....query object is matched with the mean color of a candidate tracked object in the database as follows: 4) where is the weighted Euclidean color distance in the CIELUV space, and the subscripts and refer to the query and the target, respectively. Texture: In our system, we compute three Tamura [35] texture parameters (coarseness, contrast, and orientation) for each tracked object. The distance metric is simply the Euclidean distance weighted along each texture feature with the variances along each channel: 5) where and refer to the coarseness, contrast, and orientation, respectively, and ....

H. Tamura and S. Mori, "Textural features corresponding to visual perception," IEEE Trans. Syst., Man, Cybern., vol. SMC-8, June 1978.

....data visualisation. Texture has been studied extensively in the computer vision and psychology communities (Hallett and Hofmann, 1991; Haralick et al. 1973; Julesz et al. 1973; Julesz, 1975; Julesz et al. 1978; Rao and Lohse, 1993b; Rao and Lohse, 1993a; Reed and Hans Du Buf, 1993; Tamura et al. 1978). One important result was the identification of a collection of perceptual texture dimensions . Rather than computing statistical measurements, perceptual properties like orientation, contrast, regularity, and granularity can be used to segment and classify regions within an image. Julesz et ....

....an image. Julesz et al. 1973; Julesz, 1975; Julesz et al. 1978) conducted numerous experiments that verified a viewer s ability to distinguish among texture patches with different contrast or regularity (corresponding to a difference in their first or second order statistic, respectively) Both (Tamura et al. 1978) and (Rao and Lohse, 1993b; Rao and Lohse, 1993a) asked viewers to divide a collection of texture patterns (Brodatz images) into similar groups. Tamura et al. identified coarseness, contrast, directionality, line likeness, regularity, and roughness as the perceptual properties their subjects used ....

Tamura, H., Mori, S., and Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, SMC-8(6):460--473.

....The fraction of correct textures found versus the total textures retrieved was used as an objective performance criterion. The most accurate early visual model achieved a recall accuracy on par with the best known SAR [29] model, dramatically outperforming benchmark models by Haralick [15] Tamura [43], and Kabir [23] In addition, early visual texture models exhibited very favorable cost performance characteristics: one model attained a recall accuracy of 80 using only 8 floating point numbers to represent each texture in the database. Previous work has shown that humans categorize natural ....

....features were powerful predictors of human judgements on the rank orderings of 16 representative textures. However, simple linear combinations of Tamura s features were unsuccessfull at predicting nearest neighbor similarity judgements. A thorough review of Tamura s work can be found in [43]. The version of Tamura s model used in this thesis represents each texture using 3 floating point numbers: the coarseness, contrast, and directionality features of the texture. Coarseness is computed using a three stage procedure that essentially averages the spatial scale at which neighboring ....

[Article contains additional citation context not shown here]

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, (6):460--473, 1978.

....and output ( output parameters ) parameters to execute the code. Information about the type (extraction or similarity) the language (e.g. Java or C) and the version of the code are defined as attributes of the code element. The example below includes the description of a Tamura texture [21] feature that provides the specific feature values (coarseness, contrast, and directionality) and also links to external code for feature extraction and similarity matching. In the feature extraction example, information about input and output parameters is also provided. This description could ....

H. Tamura, S. Mori, and T. Yamawaki, "Textural Features Corresponding to Visual Perception", IEEE Trans. on Systems, Man, and Cybernetics, Vol. 8, No. 6, Jun. 1978.

....vision and graphics applications. Although it is difficult to construct a formal definition, texture is intuitively related to luminance variations in the image and can be characterised using properties such as regularity, coarseness, contrast, local and global structure, and directionality [4, 14]. A system that is required to deliver meaningful judgements concerning texture, needs to be able to extract a description of the image data in a form that explicitly captures these properties. It has been suggested that the Fourier domain may provide a more favourable environment in which to ....

Hideyuki Tamura, Shunji Mori, and Takashi Yamawaki. Textural features corresponding to visual perception. IEEE Trans. Systems, Man, and Cybernetics, 8(6):460--473, 1978.

....of texture definitions in the computer vision literature and we give some examples here. We may regard texture as what constitutes a macroscopic region. Its structure is simply attributed to the repetitive patterns in which elements or primitives are arranged according to a placement rule. [2] . A region in an image has a constant texture if a set of local statistics or other local Chapter 2.1 The Handbook of Pattern Recognition and Computer Vision (2nd Edition) by C. H. Chen, L. F. Pau, P. S. P. Wang (eds. pp. 207 248, World Scientific Publishing Co. 1998. 2 properties of the ....

Tamura, H., S. Mori, and Y. Yamawaki, "Textural Features Corresponding to Visual Perception, " IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, pp. 460-473, 1978.

....Methods for comparing images based on texture generally consist of extracting features that describe a texture pattern and comparing those features using a similarity metric. 2.3. 1 Tamura Texture Features Some of the earliest work for extracting textural features was conducted by Tamura et al.[20]. Through intensive psychological studies Tamura et al..found that humans group textures into three groups based on coarseness, contrast, and directionality. Tamura proposed techniques for determining these three dimensions of texture. The Tamura texture model has been adopted by the QBIC system ....

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception, " IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, pp. 460--473, June 1978.

....A properly de ned measure of pattern regularity could therefore serve as a highly invariant, perceptually motivated feature. Previous attempts to evaluate pattern regularity were mostly aimed at textures viewed as planar patterns under two dimensional shift, rotation and zoom. In an early study [29], Tamura and co authors introduced a set of texture features, including regularity, which were designed to correspond to human perception. Their regularity feature was de ned via the spatial variation of four other features, such as coarseness and directionality. However, experiments indicated ....

....This results in a model that can cope with both stochastic and regular patterns. The main lesson is that short range analysis is not sucient to discover and assess periodicity. Any approach to regularity that only uses local statistics will be intrinsically limited, like the pilot attempt [29]. Long range analysis is 2 indispensable if one wishes to understand the structure of a regular pattern. The approach presented in this paper follows the line of the early studies by Conners and Harlow [12] and Chetverikov [3] Conners and Harlow calculated the second moment of the co occurrence ....

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. Systems, Man, and Cybernetics, 8:460-473, 1978.

No context found.

H. Tamura, S. Mori, and Y. Yammawaki, "Textural features corresponding to visual perception," IEEE Transactions on Systems, Man, Cybernetics, vol. 8, pp. 460--473, June 1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Transactions on Systems, Man and Cybernetics, vol. SMC--8, pp. 460--473, June 1978.

No context found.

Tamura H., Mori S., Yamawaki T., "Textural Features Corresponding to Visual Perceptron" IEEE Trans on SMC, vol. Smc-8, no. 6, pp. 460-473

No context found.

Tamura, H., Mori, S.: Textural features corresponding to visual perception. IEEE Trans. on Systems, Man, and Cybernetics 8 (1978)

No context found.

H. Tamura, S. Mori, and T. Yamawaki. Textural Features Corresponding to Visual Perception. IEEE Transaction on Systems, Man, and Cybernetcs, 8(6):460--472, June 1978.

No context found.

Tamura H, Mori S, Yamawaki T: Textural Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man, and Cybernetics; SMC-8(6), 460-472, 1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, "Textural features corresponding to visual perception", IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473,1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, "Textural features corresponding to visual perception", IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473,1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, "Textural features corresponding to visual perception", IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473,1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, "Textural features corresponding to visual perception", IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473,1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki. Textural Features Corresponding to Visual Perception. IEEE Trans. Systems, Man, and Cybernetcs, SMC-8(6):460--472, June 1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, "Textural features corresponding to visual perception", IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473,1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Trans. on Systems, Man, and Cybernetics, Vol. SMC-8, No. 6, June 1978.

No context found.

H. Tamura, S. Mori, T. Yamawaki, Textural features corresponding to visual perception, IEEE Trans. on System, Man and Cybernetics SMC-8 (1978) 460-- 473.

No context found.

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. Systems, Man, and Cybernetics, 8(6):460--472, June 1978.

No context found.

H. Tamura, S. Mori, and Y. Yamawaki. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8:460--473, 1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki. Textural Features Corresponding to Visual Perception. IEEE Trans. Systems, Man, and Cybernetcs, SMC-8(6):460--472, June 1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki. "Textural Features Corresponding to Visual Perception ". IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 460--473, June 1978.

No context found.

Tamura H, Mori S, Yamawaki T: Textural Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man, and Cybernetics; SMC-8(6), 460-472, 1978.

No context found.

Tamura, H., Mori, S.: Textural features corresponding to visual perception. IEEE Trans. on Systems, Man, and Cybernetics 8 (1978)

No context found.

H.Tamura, S.Mori, T. Yamawaki, "Textural Features Corresponding to Visual Perception", IEEE Trans. on Systems, Man and Cybernetics, 8, 6, 1978

No context found.

H. Tamura, S. Mori, and T. Yamawaki, "Textural Features Corresponding to Visual Perception," IEEE Transactions on Systems, Man, and Cybernetics, SMC-8:6, June 1978.

No context found.

H. Tamura, S. Mori, and T. Yamawaki. Textural features corresponding to visual perception. IEEE Trans. on SMC, 8(6):460--473, 1978.

No context found.

H. Tamura, S. Mori and T. Yamawaki, \Textural Features Corresponding to Visual Perception," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 8, NO. 6, 1978, pp. 460-473.

No context found.

H. Tamura, S. Mori, and T. Yamawaki, Textural features corresponding to visual perception, IEEE Transactions on Systems, Man, and Cybernetics SMC-8 (1978), no. 6, 460--473.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC