Revealing Cultural Ecosystem Services through Instagram Images : The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance

With the prevalence of smartphones, new ways of engaging citizens and stakeholders in urban planning and govern-ance are emerging. The technologies in smartphones allow citizens to act as sensors of their environment, producing and sharing rich spatial data useful for new types of collaborative governance set-ups. Data derived from Volunteered Geographic Information (VGI) can support accessible, transparent, democratic, inclusive, and locally-based governance situations of interest to planners, citizens, politicians, and scientists. However, there are still uncertainties about how to actually conduct this in practice. This study explores how social media VGI can be used to document spatial tendencies regarding citizens’ uses and perceptions of urban nature with relevance for urban green space governance. Via the hashtag #sharingcph, created by the City of Copenhagen in 2014, VGI data consisting of geo-referenced images were collected from Instagram, categorised according to their content and analysed according to their spatial distribution patterns. The results show specific spatial distributions of the images and main hotspots. Many possibilities and much potential of using VGI for generating, sharing, visualising and communicating knowledge about citizens’ spatial uses and preferences exist, but as a tool to support scientific and democratic interaction, VGI data is challenged by practical, technical and ethical concerns. More research is needed in order to better understand the usefulness and application of this rich data source to governance.

Development of a real time Image analysis system for traffic monitoring applications.

Image processing offers unparalleled potential for traffic monitoring and control. For many years engineers have attempted to perfect the art of automatic data abstraction from sequences of video images. This paper outlines a research project undertaken at Napier University by the authors in the field of image processing for automatic traffic analysis. A software based system implementing TRIP algorithms to count cars and measure vehicle speed has been developed by members of the Transport Engineering Research Unit (TERU) at the University. The TRIP algorithm has been ported and evaluated on an IBM PC platform with a view to hardware implementation of the pre-processing routines required for vehicle detection. Results show that a software based traffic counting system is realisable for single window processing. Due to the high volume of data required to be processed for full frames or multiple lanes, system operations in real time are limited. Therefore specific hardware is required to be designed. The paper outlines a hardware design for implementation of inter-frame and background differencing, background updating and shadow removal techniques. Preliminary results showing the processing time and counting accuracy for the routines implemented in software are presented and a real time hardware pre-processing architecture is described.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).

Image summarisation: human action description from static images

Dissertação de Mestrado, Processamento de Linguagem Natural e Indústrias da Língua, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2014

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.

Hyperspectral imaging and other optical techniques for (in-field/in-lab) physico-chemical attributes estimation of agri-food vegetal products

In the agri-food sector, measurement and monitoring activities contribute to high quality end products. In particular, considering food of plant origin, several product quality attributes can be monitored. Among the non-destructive measurement techniques, a large variety of optical techniques are available, including hyperspectral imaging (HSI) in the visible/near-infrared (Vis/NIR) range, which, due to the capacity to integrate image analysis and spectroscopy, proved particularly useful in agronomy and food science. Many published studies regarding HSI systems were carried out under controlled laboratory conditions. In contrast, few studies describe the application of HSI technology directly in the field, in particular for high-resolution proximal measurements carried out on the ground. Based on this background, the activities of the present PhD project were aimed at exploring and deepening knowledge in the application of optical techniques for the estimation of quality attributes of agri-food plant products. First, research activities on laboratory trials carried out on apricots and kiwis for the estimation of soluble solids content (SSC) and flesh firmness (FF) through HSI were reported; subsequently, FF was estimated on kiwis using a NIR-sensitive device; finally, the procyanidin content of red wine was estimated through a device based on the pulsed spectral sensitive photometry technique. In the second part, trials were carried out directly in the field to assess the degree of ripeness of red wine grapes by estimating SSC through HSI, and finally a method for the automatic selection of regions of interest in hyperspectral images of the vineyard was developed. The activities described above have revealed the potential of the optical techniques for sorting-line application; moreover, the application of the HSI technique directly in the field has proved particularly interesting, suggesting further investigations to solve a variety of problems arising from the many environmental variables that may affect the results of the analyses.

Intraductal Carcinoma Of The Prostate: Interobserver Reproducibility Survey Of 39 Urologic Pathologists.

The diagnosis of intraductal carcinoma (IDC) of the prostate remains subjective because 3 sets of diagnostic criteria are in use. An internet survey was compiled from 38 photomicrographs showing duct proliferations: 14 signed out as high-grade prostatic intraepithelial neoplasia (HGPIN), 17 IDC, and 7 invasive cribriform/ductal carcinoma. Each image was assessed for the presence of 9 histologic criteria ascribed to IDC. Thirty-nine respondents were asked to rate images as (1) benign/reactive, (2) HGPIN, (3) borderline between HGPIN and IDC, (4) IDC, or (5) invasive cribriform/ductal carcinoma. Intraclass correlation coefficient was 0.68. There was 70% overall agreement with HGPIN, 43% with IDC, and 73% with invasive carcinoma (P < .001, χ(2)). Respondents considered 19 (50%) of 38 cases as IDC candidates, of which 5 (26%) had a two-thirds consensus for IDC; two-thirds consensus for either borderline or IDC was reached in 9 (47%). Two-thirds consensus other than IDC was reached in the remaining 19 of 38 cases, with 15 supporting HGPIN and 4 supporting invasive carcinoma. Findings that differed across diagnostic categories were lumen-spanning neoplastic cells (P < .001), 2× benign duct diameters (P < .001), duct space contours (round, irregular, and branched) (P < .001), papillary growth (P = .048), dense cribriform or solid growth (both P = .023), and comedonecrosis (P = .015). When the 19 of 38 images that attained consensus for HGPIN or invasive carcinoma were removed from consideration, lack of IDC consensus was most often attributable to only loose cribriform growth (5/19), central nuclear maturation (5/19), or comedonecrosis (3/19). Of the 9 histologic criteria, only 1 retained significant correlation with a consensus diagnosis of IDC: the presence of solid areas (P = .038). One case that attained IDC consensus had less than 2× duct enlargement yet still had severe nuclear atypia and nucleomegaly. Six fold nuclear enlargement was not significant (P = .083), although no image had both 6× nuclei and papillary or loose cribriform growth: a combination postulated as sufficient criteria for IDC. Finally, 20.5% of respondents agreed that an isolated diagnosis of IDC on needle biopsy warrants definitive therapy, 20.5% disagreed, and 59.0% considered the decision to depend upon clinicopathologic variables. Although IDC diagnosis remains challenging, we propose these criteria: a lumen-spanning proliferation of neoplastic cells in preexisting ducts with a dense cribriform or partial solid growth pattern. Solid growth, in any part of the duct space, emerges as the most reproducible finding to rule in a diagnosis of IDC. Comedonecrosis is a rarer finding, but in most cases, it should rule in IDC. Duct space enlargement to greater than 2× the diameter of the largest, adjacent benign spaces is usually present in IDC, although there may be rare exceptions.

Proposição de um método de análise de movimentos de jogadores de tênis de campo a partir de vídeos televisivos = : Proposition of a method for tennis players motion analysis on broadcast videos

Universidade Estadual de Campinas . Faculdade de Educação Física

Concepção e implementação de um sistema para analise cinematica de movimentos humanos

Universidade Estadual de Campinas. Faculdade de Educação Física

Desenvolvimento de uma metodologia para reconstrução da superficie do pe humano por videogrametria

Universidade Estadual de Campinas . Faculdade de Educação Física

Influence of intracanal post on apical periodontitis identified by cone-beam computed tomography

The determination of the success of endodontic treatment has been often discussed based on outcome obtained by periapical radiography. The aim of this study was to verify the influence of intracanal post on apical periodontitis detected by cone-beam computed tomography (CBCT). A consecutive sample of 1020 images (periapical radiographs and CBCT scans) taken from 619 patients (245 men; mean age, 50.1 years) between February 2008 and September 2009 were used in this study. Presence and intracanal post length (short, medium and long) were associated with apical periodontitis (AP). Chi-square test was used for statistical analyses. Significance level was set at p<0.01. The kappa value was used to assess examiner variability. From a total of 591 intracanal posts, AP was observed in 15.06%, 18.78% and 7.95% using periapical radiographs, into the different lengths, short, medium and long, respectively (p=0.466). Considering the same posts length it was verified AP in 24.20%, 26.40% and 11.84% observed by CBCT scans, respectively (p=0.154). From a total of 1,020 teeth used in this study, AP was detected in 397 (38.92%) by periapical radiography and in 614 (60.19%) by CBCT scans (p<0.001). The distribution of intracanal posts in different dental groups showed higher prevalence in maxillary anterior teeth (54.79%). Intracanal posts lengths did not influenced AP. AP was detected more frequently when CBCT method was used.

Ex vivo analysis of root canal cleaning using Endo-PTC associated to NaOCl and different irrigant solutions

The aim of this study was to assess qualitatively, by means of SEM images, the cleaning of the dentin walls of root canals after chemical-surgical preparation using Endo-PTC cream with 0.5% and 1% sodium hypochlorite and different final irrigating solutions. Seventy-two single-rooted human teeth were divided into eight groups and prepared using Endo-PTC cream with sodium hypochlorite (NaOCl) at different concentrations, and irrigated with NaOCl at different concentrations. Final irrigation was performed with either EDTA-T or EDTA-C. The best results were obtained with Group 1, followed by Groups 5, 2, 7, 8, 3, 6 and 4. We can conclude that the use of 0.5% NaOCl during instrumentation and final flush of the root canals was more efficient in cleaning than was 1% sodium hypochlorite. EDTA-T was more efficient in removing smear layer than EDTA-C, and the cervical third presented better cleaning of the root canal walls than did the middle third, which showed cleaner dentin walls than the apical third.

Origins of the Xylella fastidiosa Prophage-Like Regions and Their Impact in Genome Differentiation

Xylella fastidiosa is a Gram negative plant pathogen causing many economically important diseases, and analyses of completely sequenced X. fastidiosa genome strains allowed the identification of many prophage-like elements and possibly phage remnants, accounting for up to 15% of the genome composition. To better evaluate the recent evolution of the X. fastidiosa chromosome backbone among distinct pathovars, the number and location of prophage-like regions on two finished genomes (9a5c and Temecula1), and in two candidate molecules (Ann1 and Dixon) were assessed. Based on comparative best bidirectional hit analyses, the majority (51%) of the predicted genes in the X. fastidiosa prophage-like regions are related to structural phage genes belonging to the Siphoviridae family. Electron micrograph reveals the existence of putative viral particles with similar morphology to lambda phages in the bacterial cell in planta. Moreover, analysis of microarray data indicates that 9a5c strain cultivated under stress conditions presents enhanced expression of phage anti-repressor genes, suggesting switches from lysogenic to lytic cycle of phages under stress-induced situations. Furthermore, virulence-associated proteins and toxins are found within these prophage-like elements, thus suggesting an important role in host adaptation. Finally, clustering analyses of phage integrase genes based on multiple alignment patterns reveal they group in five lineages, all possessing a tyrosine recombinase catalytic domain, and phylogenetically close to other integrases found in phages that are genetic mosaics and able to perform generalized and specialized transduction. Integration sites and tRNA association is also evidenced. In summary, we present comparative and experimental evidence supporting the association and contribution of phage activity on the differentiation of Xylella genomes.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.