Research on robust salient object extraction in image

制度:新 ; 文部省報告番号:甲2641号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature

Fish fillet authentication by image analysis

The work aims at developing an image analysis procedure able to distinguish high value fillets of Atlantic cod (Gadus morhua) from those of haddock (Melanogrammus aeglefinus). The images of fresh G. morhua (n \ubc 90) and M. aeglefinus (n \ubc 91) fillets were collected by a flatbed scanner and processed at different levels. Both untreated and edge-based segmented (Canny algorithm) regions of interest were submitted to surface texture evaluation by Grey Level Co-occurrence Matrix analysis. Twelve surface texture variables selected by Principal Component Analysis or by SELECT algorithm were then used to develop Linear Discriminant Analysis models. An average correct classification rate ranging from 86.05 to 92.31% was obtained in prediction, irrespective the use of raw or segmented images. These findings pave the way for a simple machine vision system to be implemented along fish market chain, in order to provide stakeholders with a simple, rapid and cost-effective system useful in fighting commercial frauds

Computer Vision System as a Tool to Estimate Pork Marbling

Currently pork marbling is assessed subjectively in the industry, because of the limited methods and tools that are suitable for the industry. In this dissertation, we are devoted to develop a computer vision system for objective measurement of pork which suits the industrial needs. Experiment 1 examined the possibility of using computer vision system (CVS) to predict marbling in a lab-based experiment using pork samples that were already trimmed of subcutaneous fat and connective tissue. Experiment 2 an industrial scale CVS was built to predict the 3rd and 10th rib pork chop’s marbling. Experiment 3 the industrial scale CVS was tested in the meat plant and images of whole boneless pork loin were collected. The CVS predicted marbling were compared with subjective marbling score using crude fat percentage (CF%) as standard. In experiment 1 subjective marbling score had a correlation of 0.81 with CF% while CVS had a 0.66 correlation. CVS has shown an accuracy of 63% for stepwise regression model and 75% for support vector machine model. These results indicate that CVS has the potential to be used as an tool to predict pork intramuscular fat (IMF)%. In experiment 2 the accuracy of CVS predicting pork chop CF% was 68.6% and subjective marbling was 70.1%. A drop of accuracy in predicting anterior chop CF% for both CVS and objective marbling score was observed when compared to posterior chop, this suggest that there is a discrepancy in accuracy between the anatomy location of samples collected. In experiment 3 the accuracy of CVS predicting boneless whole loin was 58.6% and subjective marbling score was 53.3%. In this research, CVS has demonstrated a consistency of accuracies using different pork samples. CVS has shown higher accuracy when predicting whole boneless loin IMF% when compared to subjective assessment.National Pork BoardColeman Natura

Semi-automated detection of eagle nests: an application of very high-resolution image data and advanced image analyses to wildlife surveys

Very high-resolution (VHR) image data, including from unmanned aerial vehicle (UAV) platforms, are increasingly acquired for wildlife surveys. Animals or structures they build (e.g. nests) can be photointerpreted from these images, however, automated detection is required for more efficient surveys. We developed semi-automated analyses to map white-bellied sea eagle (Haliaeetus leucogaster) nests in VHR aerial photographs of the Houtman Abrolhos Islands, Western Australia, an important breeding site for many seabird species. Nest detection is complicated by high environmental heterogeneity at the scale of nests (~1–2 m), the presence of many features that resemble nests and the variability of nest size, shape and context. Finally, the rarity of nests limits the availability of training data. These challenges are not unique to wildlife surveys and we show how they can be overcome by an innovative integration of object-based image analyses (OBIA) and the powerful machine learning one-class classifier Maxent. Maxent classifications using features characterizing object texture, geometry and neighborhood, along with limited object color information, successfully identified over 90% of high quality nests (most weathered and unusually shaped nests were also detected, but at a slightly lower rate) and labeled <2% of objects as candidate nests. Although this overestimates the occurrence of nests, the results can be visually screened to rule out all but the most likely nests in a process that is simpler and more efficient than manual photointerpretation of the full image. Our study shows that semi-automated image analyses for wildlife surveys are achievable. Furthermore, the developed strategies have broad relevance to image processing applications that seek to detect rare features differing only subtly from a heterogeneous background, including remote sensing of archeological remains. We also highlight solutions to maximize the use of imperfect or uncalibrated image data, such as some UAV-based imagery and the growing body of VHR imagery available in Google Earth and other virtual globes

Ethnically Segmented Markets: Korean-Owned Black Hair Stores

Races often collide in segmented markets where buyers belong to one ethnic group while sellers belong to another. This Article examines one such market: the retail of wigs and hair extensions for African Americans, a multi-billion-dollar market controlled by Korean Americans. Although prior scholarship attributed the success of Korean American ventures to rotating communal credit, this Article argues that their dominance in ethnic beauty supplies stems from collusion and exclusion. This Article is the first to synthesize the disparate treatment of ethnically segmented markets in law, sociology, and economics into a comprehensive framework. Its primary contribution is to forge the concept of ethnically segmented and misaligned (ESM) markets, where buyers and sellers are ethnically distinct from one another. ESM markets challenge entrenched paradigms in antitrust. In the wigs and extensions market, the endurance of Korean American retailers confounds conventional notions of market power, which is measured at the firm level. This market suggests that numerous in-group incumbents can compete intensely with one another but collaborate to stymie out-group insurgents

Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products

Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images.info:eu-repo/semantics/acceptedVersio

RENDERING PRINCIPAL DIRECTION CONTOUR LINES WITH ORIENTED TEXTURES

In this paper we explore the use of contour lines in computer graphics as a means of conveying shape to the end-user. Contour lines provide an alternative to traditional realistic rendering styles and may even provide a more appropriate visualization for certain situations. For our images, contour line orientation is established in accordance with principal curvature directions. We present a method for rendering a texture, oriented in the principal curvature direction, across a traditionally-modeled geometric surface that effectively forms suggestive contour lines to enhance the visualization of that surface. We further extend the method to create animated contour textures, wherein lines move across a surface to suggest its shape. We demonstrate how the animation can be made more intuitive and easier to follow through a meaningful generalization of the generated vector space

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations