Browsing by Subject "Structural information"

Now showing 1 - 5 of 5

Open Access
Automated detection of objects using multiple hierarchical segmentations
(IEEE, 2007-07) Akçay H. Gökhan; Aksoy, Selim
We introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed by combining structural information extracted by morphological processing with spectral information summarized using principal components analysis. Then, segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity are selected as candidate structures for object detection. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the object-conditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on two data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. © 2007 IEEE.
Open Access
Detection of compound structures using clustering of statistical and structural features
(IEEE, 2012) Akçay, H. Gökhan; Aksoy, Selim
We describe a new method for detecting compound structures in images by combining the statistical and structural characteristics of simple primitive objects. A graph is constructed by assigning the primitive objects to its vertices, and connecting potentially related objects using edges. Statistical information that is modeled using spectral, shape, and position data of individual objects as well as the structural information that is modeled in terms of spatial alignments of neighboring object groups are also encoded in this graph. Experiments using WorldView-2 data show that hierarchical clustering of the graph vertices can discover high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE.
Open Access
Detection of compound structures using hierarchical clustering of statistical and structural features
(IEEE, 2011) Akçay, H. Gokhan; Aksoy, Selim
We describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE.
Open Access
Scalable image quality assessment with 2D mel-cepstrum and machine learning approach
(Elsevier, 2011-07-19) Narwaria, M.; Lin, W.; Çetin, A. Enis
Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.
Open Access
Supramolecular nanostructure formation of coassembled amyloid inspired peptides
(American Chemical Society, 2016-06) Cinar, G.; Orujalipoor, I.; Su, C.-J.; Jeng, U.-S.; Ide, S.; Güler, Mustafa O.
Characterization of amyloid-like aggregates through converging approaches can yield deeper understanding of their complex self-assembly mechanisms and the nature of their strong mechanical stability, which may in turn contribute to the design of novel supramolecular peptide nanostructures as functional materials. In this study, we investigated the coassembly kinetics of oppositely charged short amyloid-inspired peptides (AIPs) into supramolecular nanostructures by using confocal fluorescence imaging of thioflavin T binding, turbidity assay and in situ small-angle X-ray scattering (SAXS) analysis. We showed that coassembly kinetics of the AIP nanostructures were consistent with nucleation-dependent amyloid-like aggregation, and aggregation behavior of the AIPs was affected by the initial monomer concentration and sonication. Moreover, SAXS analysis was performed to gain structural information on the size, shape, electron density, and internal organization of the coassembled AIP nanostructures. The scattering data of the coassembled AIP nanostructures were best fitted into to a combination of polydisperse core-shell cylinder (PCSC) and decoupling flexible cylinder (FCPR) models, and the structural parameters were estimated based on the fitting results of the scattering data. The stability of the coassembled AIP nanostructures in both fiber organization and bulk viscoelastic properties was also revealed via temperature-dependent SAXS analysis and oscillatory rheology measurements, respectively.