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Full-Text Articles in Physical Sciences and Mathematics
Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen
Enhancing Informative Frame Filtering By Water And Bubble Detection In Colonoscopy Videos, Ashok Dahal, Junghwan Oh, Wallapak Tavanapong, Johnny S. Wong, Piet C. De Groen
Johnny Wong
Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a …
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Professor Noel Cressie
Texture is a phenomenon in image data that continues to receive wide-spread interest due to its broad range of applications. The paper focuses on but one of several ways to model textures, namely, the class of stochastic texture models. the authors introduce a new spatial stochastic model called partially ordered Markov models, or POMMs. They show how POMMs are a generalization of a class of models called Markov mesh models, or MMMs, that allow an explicit closed form of the joint probability, just as do MMMs. While POMMs are a type of Markov random field model (MRF), the general MRFs …
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
Associate Professor Wanqing Li
This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the …
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Associate Professor Golshah Naghdy
A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
Associate Professor Golshah Naghdy
A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …
Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona
Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona
Associate Professor Golshah Naghdy
Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for …
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona
Professor Philip Ogunbona
A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona
Professor Philip Ogunbona
A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …
Crossing Of Disclinations In Nematic Slabs, T. Ishikawa, Oleg Lavrentovich
Crossing Of Disclinations In Nematic Slabs, T. Ishikawa, Oleg Lavrentovich
Oleg Lavrentovich
It is shown experimentally that crossing and intercommutation of disclinations in a bounded nematic cell depend on surface orientation of the director and the relative strength of disclinations. Lines of opposite strength switch the pinned ends between the bounding plates and vanish independently of each other if the surface orientation is tangential. In contrast, tilted surface orientation preserves the stability of lines.