Um método para determinação da profundidade combinando visão estéreo e autocalibração para aplicação em robótica móvel

This work proposes a method to determine the depth of objects in a scene using a combination between stereo vision and self-calibration techniques. Determining the rel- ative distance between visualized objects and a robot, with a stereo head, it is possible to navigate in unknown environments. Stereo vision techniques supply a depth measure by the combination of two or more images from the same scene. To achieve a depth estimates of the in scene objects a reconstruction of this scene geometry is necessary. For such reconstruction the relationship between the three-dimensional world coordi- nates and the two-dimensional images coordinates is necessary. Through the achievement of the cameras intrinsic parameters it is possible to make this coordinates systems relationship. These parameters can be gotten through geometric camera calibration, which, generally is made by a correlation between image characteristics of a calibration pattern with know dimensions. The cameras self-calibration allows the achievement of their intrinsic parameters without using a known calibration pattern, being possible their calculation and alteration during the displacement of the robot in an unknown environment. In this work a self-calibration method based in the three-dimensional polar coordinates to represent image features is presented. This representation is determined by the relationship between images features and horizontal and vertical opening cameras angles. Using the polar coordinates it is possible to geometrically reconstruct the scene. Through the proposed techniques combination it is possible to calculate a scene objects depth estimate, allowing the robot navigation in an unknown environment

Evaluation of interface characterization and adhesion of glass ceramics to commercially pure titanium and gold alloy after thermal- and mechanical-loading

Objectives. This study evaluated the effect of thermal- and mechanical-cycling on the shear bond strength of three low-fusing glassy matrix dental ceramics to commercial pure titanium (cpTi) when compared to conventional feldspathic ceramic fused to gold alloy.Methods. Metallic frameworks (diameter: 5 min, thickness: 4 mm) (N = 96, n = 12 per group) were cast in cpTi and gold alloy, airborne particle abraded with 150 mu m aluminum oxide. Low-fusing glassy matrix ceramics and a conventional feldspathic ceramic were fired onto the alloys (thickness: 4mm). Four experimental groups were formed; Gr1 (control group): Vita Omega 900-Au-Pd alloy; Gr2: Ticeram-cpTi; Gr3: Super Porcelain Ti-22-cpTi and G4: Vita Titankeramik-cpTi. While half of the specimens from each ceramic-metal combination were randomly tested without aging (water storage at 37 C for 24h only), the other half were first thermocycled (6000 cycles, between 5 and 55 C, dwell time: 13 s) and then mechanically loaded (20,000 cycles under SON load, immersion in distilled water at 37 C). The ceramic-alloy interfaces were loaded under shear in a universal test machine (cross-head speed: 0.5 mm/min) until failure occur-red. Failure types were noted and the interfaces of the representative fractured specimens from each group were examined with stereo microscope and scanning electron microscope (SEM). in an additional study (N = 16, n = 2 per group), energy dispersive X-ray spectroscopy (EDS) analysis was performed from ceramic-alloy interfaces. Data were analyzed using ANOVA and Tukey's test.Results. Both ceramic-metal combinations (p < 0.001) and aging conditions (p < 0,001) significantly affected the mean bond strength values. Thermal- and mechanical-cycling decreased the bond strength (MPa) results significantly for Gr3 (33.4 +/- 4.2) and Gr4 (32.1 +/- 4.8) when compared to the non-aged groups (42.9 +/- 8.9, 42.4 +/- 5.2, respectively). Gr1 was not affected significantly from aging conditions (61.3 +/- 8.4 for control, 60.7 +/- 13.7 after aging) (p > 0.05). Stereomicroscope images showed exclusively adhesive failure types at the opaque ceramic-cpTi interfacial zone with no presence of ceramic on the substrate surface but with a visible dark titanium oxide layer in Groups 2-4 except Gr1 where remnants of bonder ceramic was visible. EDS analysis from the interfacial zone for cpTi-ceramic groups showed predominantly 34.5-85.1% O(2) followed by 1.1-36.7% Aland 0-36.3% Si except for Super Porcelain Ti-22 where a small quantity of Ba (1.4-8.3%), S (0.7%) and Sn (35.3%) was found. In the Au-Pd alloy-ceramic interface, 56.4-69.9% O(2) followed by 15.6-26.2% Si, 3.9-10.9% K, 2.8-6% Na, 4.4-9.6% Al and 0-0.04% Mg was observed.Significance. After thermal-cycling for 6000 times and mechanical-cycling for 20,000 times, Triceram-cpTi combination presented the least decrease among other ceramic-alloy combinations when compared to the mean bond strength results with Au-Pd alloy-Vita Omega 900 combination. (c) 2008 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

A unique intraorbital osseous structure in the large fruit-eating bat (Artibeus lituratus)

Objective: To describe the normal bony orbital structure of the large fruit-eating bat (Artibeus lituratus) with emphasis on a unique intraorbital bony structure previously not described in the literature. Procedures: The bony anatomy of the orbital cavity was studied on dissected skulls of large fruit-eating bats. The anatomic description of a unique intraorbital spine was made while studying the bony orbit of macerated skulls. Additional observations were made on dissected formalin-fixed whole heads. Both procedures were performed under a stereo dissecting microscope, using ×2-4-magnification. A histologic analysis of soft tissues surrounding this cylindrical bony structure was performed using cross and longitudinal oblique sections from decalcified whole heads, which had been fixed in formalin. Additionally, biometric measurements and a histomorphometric analysis were performed. Results and conclusions: An intraorbital cylindrical osseous structure measuring 3.96 ± 0.68 mm in length and 155.62 ± 14.03 μm in diameter was observed in the large fruit-eating bat (A. lituratus), creating a unique orbital structural design among mammals. We suggest the name optic spine of the alisphenoid bone. The anatomic, biometric and histologic characterization of this element might contribute to a further understanding of the dynamics of bat vision and the sort of factors that influenced evolution of the visual system of microbats. The authors hope that the documentation of this distinctive anatomic feature will also expand the debate about the phylogenetic analysis of the relationship among bat species in the near future. © 2007 American College of Veterinary Ophthalmologists.

Localização automática de pontos de controle em imagens aéreas baseada em cenas terrestres verticais

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

Efeito de protocolos de cimentação na carga de fratura para uma cerâmica de dissilicato de lítio

Pós-graduação em Odontologia Restauradora - ICT

Visão estéreo para auxílio à navegação autônoma em ambientes agrícolas

Pós-graduação em Ciência da Computação - IBILCE

An ice machine adapted into an autonomous photovoltaic system without batteries using a variable-speed drive

The adaptation of a commercially available ice machine for autonomous photovoltaic operation without batteries is presented. In this adaptation a 1040 W(p) photovoltaic array directly feeds a variable-speed drive and a 24 V(dc) source. The drive runs an induction motor coupled by belt-and-pulley to an open reciprocating compressor, while the dc source supplies a solenoid valve and the control electronics. Motor speed and refrigerant evaporation pressure are set aiming at continuously matching system power demand to photovoltaic power availability. The resulting system is a simple integration of robust, standard, readily available parts. It produces 27 kg of ice in a clear-sky day and has ice production costs around US$0.30/kg. Although a few machine features might be specific to Brazil, its technical and economical guidelines are applicable elsewhere. Copyright (C); 2010 John Wiley & Sons, Ltd.

Effects of age and optical blur on real depth stereoacuity

Stereoscopic depth perception utilizes the disparity cues between the images that fall on the retinae of the two eyes. The purpose of this study was to determine what role aging and optical blur play in stereoscopic disparity sensitivity for real depth stimuli. Forty-six volunteers were tested ranging in age from 15 to 60 years. Crossed and uncrossed disparity thresholds were measured using white light under conditions of best optical correction. The uncrossed disparity thresholds were also measured with optical blur (from +1.0D to +5.0D added to the best correction). Stereothresholds were measured using the Frisby Stereo Test, which utilizes a four-alternative forced-choice staircase procedure. The threshold disparities measured for young adults were frequently lower than 10 arcsec, a value considerably lower than the clinical estimates commonly obtained using Random Dot Stereograms (20 arcsec) or Titmus Fly Test (40 arcsec) tests. Contrary to previous reports, disparity thresholds increased between the ages of 31 and 45 years. This finding should be taken into account in clinical evaluation of visual function of older patients. Optical blur degrades visual acuity and stereoacuity similarly under white-light conditions, indicating that both functions are affected proportionally by optical defocus.

Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation

PURPOSE. We previously demonstrated that most eyes have regionally variable extensions of Bruch's membrane (BM) inside the clinically identified disc margin (DM) that are clinically and photographically invisible. We studied the impact of these findings on DM- and BM opening (BMO)-derived neuroretinal rim parameters. METHODS. Disc stereo-photography and spectral domain optical coherence tomography (SD-OCT, 24 radial B-scans centered on the optic nerve head) were performed on 30 glaucoma patients and 10 age-matched controls. Photographs were colocalized to SD-OCT data such that the DM and BMO could be visualized in each B-scan. Three parameters were computed: (1) DM-horizontal rim width (HRW), the distance between the DM and internal limiting membrane (ILM) along the DM reference plane; (2) BMO-HRW, the distance between BMO and ILM along the BMO reference plane; and (3) BMO-minimum rim width (MRW), the minimum distance between BMO and ILM. Rank-order correlations of sectors ranked by rim width and spatial concordance measured as angular distances between equivalently ranked sectors were derived. RESULTS. The average DM position was external to BMO in all quadrants, except inferotemporally. There were significant sectoral differences among all three rim parameters. DM- HRW and BMO-HRW sector ranks were better correlated (median rho = 0.84) than DM- HRW and BMO-MRW (median rho = 0.55), or BMO-HRW and BMO-MRW (median rho = 0.60) ranks. Sectors with the narrowest BMO-MRW were infrequently the same as those with the narrowest DM-HRW or BMO-HRW. CONCLUSIONS. BMO-MRW quantifies the neuroretinal rim from a true anatomical outer border and accounts for its variable trajectory at the point of measurement. (Invest Ophthalmol Vis Sci. 2012;53:1852-1860) DOI:10.1167/iovs.11-9309

Bilayer segmentation of live video in uncontrolled environments for background substitution: an overview and main challenges

Bilayer segmentation of live video in uncontrolled environments is an essential task for home applications in which the original background of the scene must be replaced, as in videochats or traditional videoconference. The main challenge in such conditions is overcome all difficulties in problem-situations (e. g., illumination change, distract events such as element moving in the background and camera shake) that may occur while the video is being captured. This paper presents a survey of segmentation methods for background substitution applications, describes the main concepts and identifies events that may cause errors. Our analysis shows that although robust methods rely on specific devices (multiple cameras or sensors to generate depth maps) which aid the process. In order to achieve the same results using conventional devices (monocular video cameras), most current research relies on energy minimization frameworks, in which temporal and spacial information are probabilistically combined with those of color and contrast.

A variational approach to 3D cylindrical geometry reconstruction from multiple views

[EN] In this paper we present a variational technique for the reconstruction of 3D cylindrical surfaces. Roughly speaking by a cylindrical surface we mean a surface that can be parameterized using the projection on a cylinder in terms of two coordinates, representing the displacement and angle in a cylindrical coordinate system respectively. The starting point for our method is a set of different views of a cylindrical surface, as well as a precomputed disparity map estimation between pair of images. The proposed variational technique is based on an energy minimization where we balance on the one hand the regularity of the cylindrical function given by the distance of the surface points to cylinder axis, and on the other hand, the distance between the projection of the surface points on the images and the expected location following the precomputed disparity map estimation between pair of images. One interesting advantage of this approach is that we regularize the 3D surface by means of a bi-dimensio al minimization problem. We show some experimental results for large stereo sequences.

Sobre algunos problemas reales en visión por ordenador que conducen a sistemas de ecuaciones algebraicos

[EN] In this paper we present some real problems which appear in computer vision which yields to nonlinear system of algebraic equations. We study the problem of camera calibration. Roughly speaking camera calibration consists in looking at the camera position in the 3- D world using as information the projection of a 3- D Scene in a 2-D plane (the photogram). The problem is quite different when we use a single view or several views (stereo vision) of the 3-D scene. We will show in this paper how these problems yields to nonlinear algebraic system of equations.

Segmentazione e categorizzazione di oggetti mediante immagini depth e deep learning

Questo lavoro è iniziato con uno studio teorico delle principali tecniche di classificazione di immagini note in letteratura, con particolare attenzione ai più diffusi modelli di rappresentazione dell’immagine, quali il modello Bag of Visual Words, e ai principali strumenti di Apprendimento Automatico (Machine Learning). In seguito si è focalizzata l’attenzione sulla analisi di ciò che costituisce lo stato dell’arte per la classificazione delle immagini, ovvero il Deep Learning. Per sperimentare i vantaggi dell’insieme di metodologie di Image Classification, si è fatto uso di Torch7, un framework di calcolo numerico, utilizzabile mediante il linguaggio di scripting Lua, open source, con ampio supporto alle metodologie allo stato dell’arte di Deep Learning. Tramite Torch7 è stata implementata la vera e propria classificazione di immagini poiché questo framework, grazie anche al lavoro di analisi portato avanti da alcuni miei colleghi in precedenza, è risultato essere molto efficace nel categorizzare oggetti in immagini. Le immagini su cui si sono basati i test sperimentali, appartengono a un dataset creato ad hoc per il sistema di visione 3D con la finalità di sperimentare il sistema per individui ipovedenti e non vedenti; in esso sono presenti alcuni tra i principali ostacoli che un ipovedente può incontrare nella propria quotidianità. In particolare il dataset si compone di potenziali ostacoli relativi a una ipotetica situazione di utilizzo all’aperto. Dopo avere stabilito dunque che Torch7 fosse il supporto da usare per la classificazione, l’attenzione si è concentrata sulla possibilità di sfruttare la Visione Stereo per aumentare l’accuratezza della classificazione stessa. Infatti, le immagini appartenenti al dataset sopra citato sono state acquisite mediante una Stereo Camera con elaborazione su FPGA sviluppata dal gruppo di ricerca presso il quale è stato svolto questo lavoro. Ciò ha permesso di utilizzare informazioni di tipo 3D, quali il livello di depth (profondità) di ogni oggetto appartenente all’immagine, per segmentare, attraverso un algoritmo realizzato in C++, gli oggetti di interesse, escludendo il resto della scena. L’ultima fase del lavoro è stata quella di testare Torch7 sul dataset di immagini, preventivamente segmentate attraverso l’algoritmo di segmentazione appena delineato, al fine di eseguire il riconoscimento della tipologia di ostacolo individuato dal sistema.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.