102,741 research outputs found
Labelled Dataset of Retinal Images for Glaucoma detection
No full textFundus photography is a viable option for glaucoma population screening. In order to facilitate the development of computer-aided glaucoma detection systems, we publish this annotation dataset that contains manual annotations of glaucoma features for seven public fundus image data sets. All manual annotations are made by a specialised ophthalmologist. For each of the fundus images in the seven fundus datasets, the upper, the bottom, the left and the right boundary coordinates of the optic disc and the cup are stored in a .mat file with the corresponding fundus image name.
The seven public fundus image data sets are:
CHASEDB (https://blogs.kingston.ac.uk/retinal/chasedb1/),
Diaretdb1_v_1_1 (https://www.it.lut.fi/project/imageret/diaretdb1/),
DRINSHTI (http://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php),
DRIONS-DB (http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html),
DRIVE (https://www.isi.uu.nl/Research/Databases/DRIVE/),
HRF (https://www5.cs.fau.de/research/data/fundus-images/), and
Messidor (http://www.adcis.net/en/Download-Third-Party/Messidor.html).
Researchers are encouraged to use this set to train or validate their systems for automatic glaucoma detection. When you use this set, please cite our published paper:
J. Guo, G. Azzopardi, C. Shi, N. M. Jansonius and N. Petkov, "Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening," in IEEE Access, vol. 7, pp. 8527-8541, 2019, doi: 10.1109/ACCESS.2018.2890544.
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Lill-mużika ta’ Chopin
No full textĠabra ta’ poeżiji u proża li tinkludi: Talba lil Ġesù Bambin ta’ Ivo Muscat Azzopardi – Il-kewkba tal-Milied ta’ Ġużè Galea – Il-Milied it-tajjeb! ta’ G. Borg Pantalleresco – Lill-mużika ta’ Chopin ta’ John Sciberras.N/
Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models
No full textThe remarkable abilities of the primate visual cortex have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 -> V4 -> TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. A S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms
Using the quantum probability ranking principle to rank interdependent documents
No full textA known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements
Taħbit u ferħ ta’ ħajja
No full textĠabra ta’ poeżiji u proża li tinkludi: Sbejħa Marija ta’ A. G. – In-Nemusa u l-Barri ta’ R. M. B. – Xewqat tal-Milied ta’ Dun Karm – Ħasibha Tard ta’ Ivo Muscat Azzopardi – Il-poeżija tal-ħajja ta’ Ġużè Chetcuti – Taħbit u ferħ ta’ ħajja ta’ Ġużi Chetcuti.N/
Recognition of architectural and electrical symbols by COSFIRE filters with inhibition
No full textThe automatic recognition of symbols can be used to automatically convert scanned drawings into digital representations compatible with computer aided design software. We propose a novel approach to automatically recognize architectural and electrical symbols. The proposed method extends the existing trainable COSFIRE approach by adding an inhibition mechanism that is inspired by shape-selective TEO neurons in visual cortex. A COSFIRE filter with inhibition takes as input excitatory and inhibitory responses from line and edge detectors. The type (excitatory or inhibitory) and the spatial arrangement of low level features are determined in an automatic configuration step that analyzes two types of prototype pattern called positive and negative. Excitatory features are extracted from a positive pattern and inhibitory features are extracted from one or more negative patterns. In our experiments we use four subsets of images with different noise levels from the Graphics Recognition data set (GREC 2011) and demonstrate that the inhibition mechanism that we introduce improves the effectiveness of recognition substantially
Information Scent, Searching and Stopping: Modelling SERP Level Stopping Behaviour
No full textCurrent models and measures of the Interactive Information Retrieval (IIR) process typically assume that a searcher will always examine the first snippet in a given Search Engine Results Page (SERP), and then with some probability or cutoff, he or she will stop examining snippets and/or documents in the ranked list (snippet level stopping). Prior work has however shown that searchers will form an initial impression of the SERP, and will often abandon a page without clicking on or inspecting in detail any snippets or documents. That is, the information scent affects their decision to continue. In this work, we examine whether considering the information scent of a page leads to better predictions of stopping behaviour. In a simulated analysis, grounded with data from a prior user study, we show that introducing a SERP level stopping strategy can improve the performance attained by simulated users, resulting in an increase in gain across most snippet level stopping strategies. When compared to actual search and stopping behaviour, incorporating SERP level stopping offers a closer approximation than without. These findings show that models and measures that naïvely assume snippets and documents in a ranked list are actually examined in detail are less accurate, and that modelling SERP level stopping is required to create more realistic models of the search process
Quantitative assessment of the carbocation/carbene character of the gold-carbene bond
No full textThe geometric perturbation of the cyclopropyl ring in [LAu(S)]n+ (S = cyclopropyl(methoxy)carbene) complexes has been recently proposed as an indirect experimental probe of the [LAu]n+ electron-donating power, but experimental data are available only for a phosphine ligand [Brooner et al., Chem. Commun., 2014, 50, 2420, L = P(t-Bu)2(o-biphenyl)]. We broaden the study through DFT geometry optimization of a large number of systems, including anionic, neutral and cationic ligands. We combine these results with the accurate calculation, through charge displacement analysis, of the Dewar-Chatt-Duncanson components of the Au-carbene bond. The results demonstrate a linear correlation between the distortion of the cyclopropyl ring (Δd) and the Au → C π back-donation, which enables us to confidently estimate back-donation from a simple geometry optimization or, when available, from experimental data such as X-ray crystal structures. Consequently, Δd can be reliably used to quantitatively determine the position of each system in the continuum between the carbocationic and carbene extremes and the percentage of back-donation that S is able to accept (Pback). In particular, Pback results to be vanishing with cationic ligands, between 18 and 27% with neutral phosphines and carbenes and around 50% with anionic ligands. Finally, we study the effect of the heteroatom on the substrate, showing that the absolute value of the back-donation is enhanced by around 25% when the methoxy is substituted by a methyl group. Despite this, since the absence of the heteroatom also enhances the maximum capacity of the carbene to accept back-donation, the position of the systems in the continuum moves only slightly toward the carbene end. © The Royal Society of Chemistry 2015
Cutting Edge Localisation in an Edge Profile Milling Head
No full textWear evaluation of cutting tools is a key issue for prolonging their lifetime and ensuring high quality of products. In this paper, we present a method for the effective localisation of cutting edges of inserts in digital images of an edge profile milling head. We introduce a new image data set of 144 images of an edge milling head that contains 30 inserts. We use a circular Hough transform to detect the screws that fasten the inserts. In a cropped area around a detected screw, we use Canny’s edge detection algorithm and Standard Hough Transform to localise line segments that characterise insert edges. We use this information and the geometry of the insert to identify which of these line segments is the cutting edge. The output of our algorithm is a set of quadrilateral regions around the identified cutting edges. These regions can then be used as input to other algorithms for the quality assessment of the cutting edges. Our results show that the proposed method is very effective for the localisation of the cutting edges of inserts in an edge profile milling machine
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