Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼20%. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.8.083690]

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

Performance Improvement of Tomographic Image Reconstruction Based on DSP Processors

This paper presents the use of a multiprocessor architecture for the performance improvement of tomographic image reconstruction. Image reconstruction in computed tomography (CT) is an intensive task for single-processor systems. We investigate the filtered image reconstruction suitability based on DSPs organized for parallel processing and its comparison with the Message Passing Interface (MPI) library. The experimental results show that the speedups observed for both platforms were increased in the same direction of the image resolution. In addition, the execution time to communication time ratios (Rt/Rc) as a function of the sample size have shown a narrow variation for the DSP platform in comparison with the MPI platform, which indicates its better performance for parallel image reconstruction.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

A method for the digital image analysis of ceramic grains based on shape factor segmentation

A very simple and robust method for ceramics grains quantitative image analysis is presented. Based on the use of optimal imaging conditions for reflective light microscopy of bulk samples, a digital image processing routine was developed for shading correction, noise suppressing and contours enhancement. Image analysis was done for grains selected according to their concavities, evaluated by perimeter ratio shape factor, to avoid consider the effects of breakouts and ghost boundaries due to ceramographic preparation limitations. As an example, the method was applied for two ceramics, to compare grain size and morphology distributions. In this case, most of artefacts introduced by ceramographic preparation could be discarded due to the use of perimeter ratio exclusion range.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Is it possible to make pixel-based radar image classification user-friendly?

In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.

Measurements of simulated periodontal bone defects in inverted digital image and film-based radiograph: An in vitro study

Purpose: This study was performed to compare the inverted digital images and film-based images of dry pig mandibles to measure the periodontal bone defect depth. Materials and Methods: Forty 2-wall bone defects were made in the proximal region of the premolar in the dry pig mandibles. The digital and conventional radiographs were taken using a Schick sensor and Kodak F-speed intraoral film. Image manipulation (inversion) was performed using Adobe Photoshop 7.0 software. Four trained examiners made all of the radiographic measurements in millimeters a total of three times from the cementoenamel junction to the most apical extension of the bone loss with both types of images: inverted digital and film. The measurements were also made in dry mandibles using a periodontal probe and digital caliper. The Student's t-test was used to compare the depth measurements obtained from the two types of images and direct visual measurement in the dry mandibles. A significance level of 0.05 for a 95% confidence interval was used for each comparison. Results: There was a significant difference between depth measurements in the inverted digital images and direct visual measurements (p>|t|=0.0039), with means of 6.29 mm (IC95%:6.04-6.54) and 6.79 mm (IC95%:6.45-7.11), respectively. There was a non-significant difference between the film-based radiographs and direct visual measurements (p>|t|=0.4950), with means of 6.64mm (IC95%:6.40-6.89) and 6.79mm(IC95%:6.45-7.11), respectively. Conclusion: The periodontal bone defect measurements in the inverted digital images were inferior to film-based radiographs, underestimating the amount of bone loss. copy; 2012 by Korean Academy of Oral and Maxillofacial Radiology.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Population Parameters Based on Known Pedigree Records from Brazilian Jaffarabadi Buffaloes

The objective of this study was to evaluate the effective number of founders and ancestors, generation intervals and completeness of pedigree in Jaffarabadi breed buffaloes raised in Brazil. Pedigree records of 1,272 animals born from 1966 were used. The parameters were estimated using ENDOG, computational population genetic software. The obtained value for completeness of pedigree was 99.5, 50.9, and 20.5 for, the first, second and third generations, respectively. Generation interval estimates expressed in years and considering different pathways were 12.28 +/- 6.90 (sire-son), 11.55 +/- 6.07 (sire-daughter), 8.20 +/- 2.63 (dam-son) and 8.794 +/-.33 (dam-daughter). The overall average generation interval was 10.17 +/- 5.43 years. The number of founders, equivalent founders and ancestor animals that contributed for the genetic diversity in the reference population (1059) were 136, 130 and 134, respectively. Effective number of founder (f(e)=8) and ancestors (f(a)=7) were small, and the calculated expected inbreeding increase per generation was 4.99%. Four ancestors explained 50% of the genetic variability in the population and the major ancestor contributed with approximately 33% of the total population genetic variation. The genetic diversity within the current population is low as a consequence of a reduced number of ancestors.

A scalable re-ranking method for content-based image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Locating control points in aerial images with a multi-scale approach based on terrestrial image patches

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)