Description: This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent
brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and
texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while
texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we
introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure
of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of
normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results
on a wide range of images are shown. Platform: |
Size: 159744 |
Author:aan |
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Description: An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles.-An automatic facial feature extraction method is
presented in this paper. The method is based on the edge
density distribution of the image. In the preprocessing stage a
face is approximated to an ellipse, and genetic algorithm is
applied to search for the best ellipse region match. In the
feature extraction stage, genetic algorithm is applied to
extract the facial features, such as the eyes, nose and mouth,
in the predefined sub regions. The simulation results
validates that the proposed method is capable of
automatically extracting features from various video images
effectively under natural lighting environments and in the
presence of certain amount of artificial noise and of multi-
face oriented with angles. Platform: |
Size: 324608 |
Author:fais |
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Description: In this paper we present Discriminative Random Fields (DRF), a discrim- inative framework for the classification of natural image regions by incor- porating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classifica- tion problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.
Platform: |
Size: 151552 |
Author:asdf12341234 |
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Description: object classification using recognized scene text in natural images. While the state-of-the-art relies on visual cues only, this paper is the first work which proposes to combine textual and visual cues. Another novelty is the textual cue extraction. Unlike the state-of-the-art text detection methods, we focus more on the background instead of text regions. Once text regions are detected, they are further processed by two methods to perform text recognition Platform: |
Size: 8192 |
Author:MANIKANDAN |
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Description: The sky region of restored images often appears serious noise and color distortion using classical dark channel prior algorithm. To address this is- sue, we propose an improved dark channel prior algorithm which recognizes the sky regions in hazy image by gradient threshold combined with the absolute value of the difference of atmospheric light and dark channel. And then we es- timate the transmission in sky and non-sky regions separately. At last, we en- hance the brightness and contrast of results. Experimental results show that our restored images are more natural and smooth in sky regions. Platform: |
Size: 6621184 |
Author:YaqingChang |
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Search - Regions in Natural Images