Scalable Multiresolution Image Segmentation and Its Application in Video Object Extraction Algorithm

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for the scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multiresolution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. Application of the spatial segmentation in video segmentation, compared to traditional image/video object extraction algorithms, produces more visually pleasing shape masks at different resolutions which is applicable for object-based video wavelet coding. Moreover it allows for larger motion, better noise tolerance and less computational complexity. In addition to spatial scalability, the proposed algorithm outperforms the standard image/video segmentation algorithms, in both objective and subjective tests

Adaptive thresholding in dynamic scene analysis for extraction of fine line

This paper presents an adaptive threshold method whereby a fine thin line of one-pixel width lines could be detected in a gray level images. The proposed method uses the percentage difference between the mean of the pixels within a window and the center pixel. The minimum threshold value however is heuristically set to 32. If the percentage difference is greater than 40% then the threshold value will be set to the difference value. This method has been applied in detecting moving objects with fine lines and the results showed that the method was able to pickup straight thin edges that belong to the moving objec

An Automated Algorithm for Approximation of Temporal Video Data Using Linear B'EZIER Fitting

This paper presents an efficient method for approximation of temporal video data using linear Bezier fitting. For a given sequence of frames, the proposed method estimates the intensity variations of each pixel in temporal dimension using linear Bezier fitting in Euclidean space. Fitting of each segment ensures upper bound of specified mean squared error. Break and fit criteria is employed to minimize the number of segments required to fit the data. The proposed method is well suitable for lossy compression of temporal video data and automates the fitting process of each pixel. Experimental results show that the proposed method yields good results both in terms of objective and subjective quality measurement parameters without causing any blocking artifacts.Comment: 14 Pages, IJMA 201

Performance of machine learning algorithms for forest species classification using WorldView-3 data in the Southern Alentejo region, Portugal

Recent advances in remote sensing technologies and the increased availability of high spatial resolution satellite data allow the acquisition of detailed spatial information. These data have been used for monitoring the Earth's surface, namely monitoring land use land cover, quantifying biomass and carbon, and evaluating the protection and conservation of forest areas. O WorldView-3 is a high spatial resolution satellite (0.50m) with 8 multispectral bands (visible and infrared) which allows obtaining detailed data from the Earth's surface. This study aims to map the forest occupation by specie with two WoldView-3 images, and to evaluate the performance of machine learning classifiers (maximum likelihood, support vector machine and random forest) in two regions of Alentejo, south of Portugal. The main forest species are Quercus suber in one region and Quercus rotundifolia in another. The procedures performed were multiresolution image segmentation and object-oriented classification based on 4 bands (blue, green, red and near infrared). As auxiliary data, vegetation indices (NDVI and SAVI) and principal components were calculated. In the object-oriented classification process, the three classifiers were tested. The support vector machine classifier was the one that presented the best accuracy (kappa and overall accuracy), for both images, allowing to obtain good results in the identification of forest species. In the image dominated by Quercus suber, the values of kappa and overall accuracy were 90% and 95%, and for the image where Quercus rotundifolia predominated, 90% and 96% respectively. The methodology applied to the high spatial resolution satellite data showed very good results in the identification and mapping of main forest species. Higher precision values stand out for the image where the Quercus rotundifolia predominates, where there is less spectral variation, namely fewer land use classes, thus reducing errors between classes that may be spectrally similar

Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.

Mammography is by far the proven method of early detection of breast cancer. However, mammography is not without its problems. It is amongst the most difficult of radiological images to interpret as the images are of low contrast and features indicative of abnormalities are very subtle and minute. In this study, a new method of automated edge detection technique is proposed to detect the abnormalities in a region of interest in a mammogram

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

Rational-operator-based depth-from-defocus approach to scene reconstruction

This paper presents a rational-operator-based approach to depth from defocus (DfD) for the reconstruction of three-dimensional scenes from two-dimensional images, which enables fast DfD computation that is independent of scene textures. Two variants of the approach, one using the Gaussian rational operators (ROs) that are based on the Gaussian point spread function (PSF) and the second based on the generalized Gaussian PSF, are considered. A novel DfD correction method is also presented to further improve the performance of the approach. Experimental results are considered for real scenes and show that both approaches outperform existing RO-based methods