1,684 research outputs found

    Image Retrieval Based on Texton Frequency-Inverse Image Frequency

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    In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method

    Reflectance Hashing for Material Recognition

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    We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials

    Impairment in preattentive visual processing in patients with Parkinson's disease

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    We explored the possibility of whether preattentive visual processing is impaired in Parkinson's disease. With this aim, visual discrimination thresholds for orientation texture stimuli were determined in two separate measurement sessions in 16 patients with idiopathic Parkinson's disease. The results were compared with those of 16 control subjects age-matched and 16 young healthy volunteers. Discrimination thresholds were measured in a four-alternative spatial forced-choice paradigm, in which subjects judged the location of a target embedded in a background of distractors. Four different stimulus configurations were employed: (i) a group of vertical targets among horizontal distractors (`vertical line targets'); (ii) targets with varying levels of orientation difference on a background of spatially filtered vertically oriented noise (`Gaussian filtered noise'); (iii) one `L' among 43 `+' signs (`texton'), all of which assess preattentive visual processing; and (iv) control condition, of one `L' among 43 `T' distractors (`non-texton' search target), which reflects attentive visual processing. In two of the preattentive tasks (filtered noise and texton), patients with Parkinson's disease required significantly greater orientation differences and longer stimulus durations, respectively. In contrast, their performance in the vertical line target and non-texton search target was comparable to that of the matched control subjects. These differences were more pronounced in the first compared with the second session. Duration of illness and age within the patient group correlated significantly with test performance. In all conditions tested, the young control subjects performed significantly better than the more elderly control group, further indicating an effect of age on this form of visual processing. The results suggest that, in addition to the well documented impairment in retinal processing, idiopathic Parkinson's disease is associated with a deficit in preattentive cortical visual processing

    Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

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    We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure
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