DETC SLAM USING 3D RECONSTRUCTION VIA A VISUAL RGB & RGBD SENSORY INPUT


 Ashlynn Henderson
 1 years ago
 Views:
Transcription
1 Poceedings of the ASME 211 Intenational Design Engineeing Technical Confeences & Computes and Infomation in Engineeing Confeence IDETC/CIE 211 August 2831, 211, Washington, DC, USA DETC SAM USING 3D ECONSTUCTION VIA A VISUA GB & GBD SENSOY INPUT Helge A. Wudemann * PhD Candidate King s College ondon ondon, United Kingdom ei Cui Postdoctoal Fellow King s College ondon ondon, United Kingdom Evangelos Geogiou * PhD Candidate King s College ondon ondon, United Kingdom Jian S. Dai Pofesso of Mechanisms and obotics King s College ondon ondon, United Kingdom *joint fist authos with equal contibution to this pape ABSTACT This pape investigates simultaneous localization and mapping (SAM poblem by exploiting the Micosoft Kinect senso aay and an autonomous mobile obot capable of selflocalization. The combination of them coves the majo featues of SAM including mapping, sensing, locating, and modeling. The Kinect senso aay povides a dual camea output of GB, using a CMOS camea, and GBD, using a depth camea. The sensos will be mounted on the KCBOT, an autonomous nonholonomic two wheel maneuveable mobile obot. The mobile obot platfom has the ability to selflocalize and pefom navigation maneuves to tavese to set taget points using intelligent pocesses. The taget point fo this opeation is a fixed coodinate position, which will be the goal fo the mobile obot to each, taking into consideation the obstacles in the envionment which will be epesented in a 3D spatial model. Extacting the images fom the senso afte a calibation outine, a 3D econstuction of the tavesable envionment is poduced fo the mobile obot to navigate. Using the constucted 3D model the autonomous mobile obot follows a polynomialbased nonholonomic tajectoy with obstacle avoidance. The expeimental esults demonstate the cost effectiveness of this off the shelf senso aay. The esults show the effectiveness to poduce a 3D econstuction of an envionment and the feasibility of using the Micosoft Kinect senso fo mapping, sensing, locating, and modeling, that enables the implementation of SAM on this type of platfom. 1. INTODUCTION Ove the past 3 yeas, eseaches have been developing multiple solutions of visualbased mobile obots that ae able to navigate within an unknown indoo and outdoo envionment. Only duing the last decade, this wide aea has been focused on function diven navigation such as impoving the living standad and inceasing the independency of blind people. In this egad, concepts of obstacle avoidance and location as well as path planning using vision have been poposed by [1]. Anothe aea of application is secuity: In [2], the WITH mobile obot [3] is pesented fo detection of theat by evaluating unexpected objects and faces. The lagest field apat fom militay use is uban seach and escue obots [4] [5]. Mobile obot systems aimed at this secto should be obust and available at athe low costs. Futhemoe, tagets ae often not only identifiable via vision but via noise. Mobile obot navigation has been of majo inteest since the 198s. The development duing this peiod is summaized the development in [6]. This suvey concentates on indoo and outdoo navigation. These ae divided into thee goups: Mapbased systems depend on pedefined geometic models o topological maps of the envionment, wheeas mapless navigation ae systems that ecognize objects found in the space o tack those objects by geneating motions based on visual 1 Copyight 211 by ASME
2 obsevations. Mapbuildingbased navigation is an intemediate way whee sensos constuct thei own geometic o topological models of the envionment fo navigation. A simple, but obust and efficient algoithm fo a mobile obot path planning is discussed in [7]. Hee, a path is taught and eplayed in indoo and outdoo envionments. The system navigates by compaing featue coodinates qualitatively. Obstacle avoidance and global localization is pat of the autho s futue wok. Indoo navigation using 2dimensional vision systems can be anothe way to exploe the envionment. ie et al. [8] pesents a twostagestechnique: Duing the offline pat the suounding is constucted with the aoblackwellized paticle filte. A location ecognition algoithm then allocates featues to the pebuilt map in ode to move autonomously within the aea. A simila appoach is used in [9]: Images ae taken by a monocula camea, segmented, filteed by an edge algoithm, and modeled as a topological gaph, whee a cetain position of the mobile obot is equivalent to a node. Hwang and Shih [1] use two chagecoupledevice (CCD cameas contolled by two stepping motos each to navigate a calike wheeled obot. The cameas ae mounted ovehead and the obot is tagged with two landmaks. Duing indoo expeiments images ae locating the mobile obot using the landmaks and obstacles. Steeo cameas ae popula techniques fo mobile obot navigation, some vision cameas ae also expanded to an omnidiectional system [11] [12] [13]. In [14] a steeo visual system is mounted on an autonomous ai vehicle fo navigation afte having tested this technique on a gound obot. The main contibution of this pape is to selflocalize and estimate the change in position ove time. A futhe step [15] descibes an online steeo camea algoithm fo econstuction of uban envionments. The sequence is as follows: Using a point cloud a 3D model is econstucted, the envionment divided into tavesable gound egions, and a local safety map is built. This plot supplies infomation about safe and unsafe aeas that is essential fo the obotic system to navigate autonomously. In [16] a method is pesented fo obstacle avoidance and path planning in an indoo envionment. Using a steeo camea mounted on a humanoid obot, the system ecognizes the floo and detects obstacles via plane extaction without any a pioi infomation of the suounding space. The disadvantage of this method is that the envionment needs to contain enough textue. In [17], the eseaches also use steeo vision guidance fo a humanoid obot. The main goal is to make this obot walking up stais and cawling undeneath obstacles. This is achieved by using scanline gouping in ode to segment planes in the envionment. The key contibution of this pape is the extaction of height infomation that is used fo path planning and navigation. Howeve, it is mentioned that the success of steeo camea systems significantly depends on the level of textue since steeo vision elies on the hoizontal dispaity in ode to ceate 3D images. Anothe way of getting a 3D econstucted map of an envionment is apply a 3D lase senso with a hemispheical field [18] o use an I senso in combination with a single camea [19]. These last two sensos ae esponsible fo a diffeent pat of the mobile obot navigation system: The vision camea is used fo planning the closest path to the taget, wheeas the I sensos will help to avoid static and dynamic obstacles. The goal will be hit as the path is divided into intemediate steps. Figue 1. THE KINECT SENSO & THE KCBOT This pape integates the Micosoft Kinect to the simultaneous localization and mapping (SAM technique using not only 2D data (GB images but also depth infomation (GBD. Futhe, this system aims autonomously fo a pedefined audio signal. Without any a pioi knowledge about the suounding envionment, the mobile obot taveses its own planned path and navigates to the souce. Followed by a section that descibes the vision device and calibation, the pape intoduces the autonomous mobile obot KCBOT, as depicted in Fig. 1. Next, an algoithm fo polynomialbased nonholonomic path planning and obstacle avoidance is pesented. Expeimental esults pove the stability and obustness of this appoach. 2. GB & GBD VISUA IMAGE CAPTUE The GB and GBD captuing device was launched in the UK ealy Novembe 21. The vision devices, that ae located on a hoizontal line, ae connected to a small base with a motoized tilt mechanism. The Kinect TM consists of an GB camea, depth senso and multiaay micophone (Fig. 1. This chapte descibes the functionality and ability of the device as well as the calibation. 2.1 The Micosoft Kinect Senso The GB images obtained by the colo CMOS camea have 8bit esolution (64 48 pixels. An extacted GB image can be seen in Fig. 2(a. The CMOS senso that will eceive the I light fom the tansmitte povides input fo the depth map with 11bit esolution (32 24 pixels. Howeve in 2 Copyight 211 by ASME
3 this pape an 8bit esolution (64 48 pixels will be extacted (Fig. 2(b. The pinciple of the Kinect senso is as follows: Between the I tansmitte, sending out stuctued light, and eceive is a small angle. Also, the I senso should be povided with a bandpass filte in ode to captue the I light only. Using tiangulation the depth can be ecalculated d.91 GB (5.26 and fo the I senso: M I nt i nsi, cgb D (6 1 (a (b.2 d GB D.54 (7.48 Figue 2. (a GB AND (b BGD IMAGE CAPTUE Fig. 2 (a and (b pesent the GB and GBD images captued by the senso, espectively. 2.2 Senso Calibation The two CMOS cameas ae calibated using the widely known pinhole camea model. egading the extinsic paametes, the GB camea will be used as the wold coodinate fame, so that the depth senso needs to be tanslated by 25mm in ydiection. The intinsic matix M Intinsic is descibed by the focal length f x and f y and the pinciple point p x and p y, so that eveything adds up to the following camea matix: f p x x M f p In tin sic y y (1 1 In ode to conside nonlinea effects, the intinsic matix has to be multiplied with the adial distotion vecto d : d ( x y d ( x y d ( x y 1 x y z d ( x y d ( x y d ( x y 1 d x y z (2 1 x X / Z y Y / Z whee, X, Y and Z is a point in the camea efeence fame. Fo the GB camea, the intinsic paametes ae: ( M I nt i nsi, cgb (4 1 Figue 3. GB IMAGE WITH INTINSIC CAIBATION The GBD image shows a cetain GB colo sequence going fom close to deep. As z inceases, the ode is as follows: Magenta (1,, 1, Blue (,, 1, Cyan (, 1, 1, Geen (, 1,, Yellow (1, 1,, ed (1,,, whee, B,1. This can be witten in cylindicalcoodinate epesentations by calculating the hue, satuation and lightness value in the HSV colo space. The thee equations ae given by [2]:, if G B G B 6, if max (, max (, min (, H B 6 2, if max (, G max (, min (, G 6 4, if max (, B max (, min (, S, if G B max(, min(,, othewise max(, (8 (9 V max(, (1 3 Copyight 211 by ASME
4 (c Sample 3 5 (d Sample 4 Figue 5. FOU SAMPES FO DISTANCE CACUATION Table 1. GBD SAMPE DISTANCE ESTIMATION Figue 4. QUADATIC INTEPOATION BETWEEN DISTANCE AND HUE VAUE Fig. 4 plots the distance d = [7,17] in [cm] against the hue value H in [ ]. Unlike a linea appoximation, a quadatic equation descibes the atio between the distance and the hue value moe accuate: 2 d.3 2 H H (11 Having Equation (11 allows fo the computation of distance based on quadatic elationship to Hue. 2.3 HSVDistance esults Since the I depth senso is calibated fo a distance between 7cm and 17cm, tests ae taken within this inteval. Fou samples can be seen in Fig. 9. The obstacle in the middle of the image is located d=8, 95, 11 and 162cm fom the Kinect TM. Table 1 shows the tanslation fom the GB colo space to the HSV colo space. As mentioned befoe, the hue value is of special inteest because this is elated to the distance d by the quadatic Equation (11. Using this intepolation, the distance can be calculated. Compaed with the measued distance, these is an aveage eo of 1.1%. Sample G B Hue in [ ] Distance d in [cm] Implementing the calibated quadatic distance equation fo d, Equation (11, Table 1 and Fig. 5 (a, (b, (c, and (d pesent the expected distance valuation of the tacked obstacle D econstuction using a GB & GBD Senso Fom the 3D data gained fom the GD and GBD senso, it is possible to geneate a point cloud. The point cloud includes a desciption of the alignment of sufaces specified by a 3tuple in ode to econstuct a polygonal mesh. These points ae efeed to as vetices if they ae to be used as cones. Futhemoe, the data supplies infomation about the GB values fo each point. Fig. 6(a shows a view along the positive xaxis. It can be clealy distinguished between the backgound and obstacle. In Fig. 6(b this view has been pitched by 45. (a (b Figue 6. 3D ECONSTUCTION (a FONT VIEW AND (b OTATED BY 45 The pocessed 3D econstuction, Fig. 6 (a and (b, povides the mobile obot with an envionment map fo path planning. (a Sample 1 (b Sample 2 3 THE KCBOT: AN AUTONOMOUS MOBIE OBOT The KCBOT [21] is a nonholonomic two wheeled mobile obot. The mobile obot is built aound the specifications fo Micomouse obot and the obocup competition. These specifications contibute to the mobile obot s fom facto and size. This mobile obot holds a complex 4 Copyight 211 by ASME
5 electonic system to suppot online path planning, selflocalization, and even simultaneous localization and mapping (SAM, which is made possible by the onboad senso aay. Figue 7. THE KCBOT: A NONHOONOMIC MOBIE OBOT A suitable autonomous mobile obot is equied as a platfom fo the Micosoft Kinect senso. Fig. 7 pesents the KCBOT which is the platfom used to suppot the senso aay. 3.1 Mobile obot Configuation In the maneuveable classification of mobile obots [22], the vehicle is defined as being constained to move in the vehicle s fixed heading angle. Fo the vehicle to change maneuve configuation, it needs to otate about itself. As the vehicle taveses on a two dimensional plane both left and ight wheels follow a path that moves aound the instantaneous cente of cuvatue at the same angle, which can be defined as ω, and thus the angula velocity of the left and ight wheel otation is deduced as follows: θ ω(icc 2 (12 θ ω(icc 2 (13 Whee is the distance between the centes of the two otating wheels and the paamete icc is the distance between the midpoint of the otating wheels and the instantaneous cente of cuvatue. Using the velocities Equations (12 and (13 of the otating left and ights wheels, θ and θ espectively, the instantaneous cente of cuvatue, icc and the cuvatue angle, ω can deived as follows: (θ θ icc (14 2 (θ θ (θ θ ω (15 Using Equations (14 and (15, two singulaities can be identified. When θ θ, the adius of instantaneous cente of cuvatue, icc tends towads infinity and this is the condition when the mobile obot is moving in a staight line. When θ θ, the mobile obot is otating about its own cente and the adius of instantaneous cente of cuvatue, icc, is null. When the wheels on the mobile obot otate, the quadatue shaft encode etuns a counte tick value; the otation diection of the otating wheel is given by positive o negative value etuned by the encode. Using the numbes of tick counts etuned, the distance tavelled by the otating left and ight wheel can be deduced in the following way: ticks πd d (16 es ticks πd d (17 es Whee ticks and ticks depicts the numbe of encode pulses counted by left and ight wheel encodes, espectively, since the last sampling, and whee D is defined as the diamete of the wheels. With esolution of the left and ight shaft encodes es and es, espectively, it is possible to detemine the distance tavelled by the left and ight otating wheel, d and d. This calculation is epesented in Equations ( Selflocalization via a Dual Shaft Encode Configuation By using the quadatue shaft encodes that accumulate the distance tavelled by the wheels, a fom of position can be deduced by deiving the mobile obot s x, y Catesian position and the maneuveable vehicle s oientation, with espect to time. The deivation stats by defining and consideing s(t and (t to be function of time, which epesents the velocity and oientation of the mobile obot, espectively. The velocity and oientation ae deived fom diffeentiating the position fom as follows: dx s( t.cos( ( t (18 dt dy s( t.sin( ( t (19 dt The change in oientation with espect to time which was defined in Equation (15 and can be descibed as follows: d l (2 dt When Equation (2 is integated, the mobile obot s angle oientation value (t with espect to time is achieved. The mobile obot s initial angle of oientation ( is witten as and is epesented as follows: b 5 Copyight 211 by ASME
6 ( t l ( t (21 b The velocity of the mobile obot is equal to the aveage speed of the two wheels and this can be incopoated into Equations (18 and (19, which is depicted as follows: dx l cos( ( t (22 dt 2 dy l.sin( ( t (23 dt 2 The next step is to integate equations (22 and (23 to the initial position of the mobile obot, which is depicted as follows: ( ( t l l x( t x sin sin( 2( b l ( ( t l l y( t y cos cos( 2( b l (24 (25 Equations (24 and (25 descibe the mobile obot s position, whee x( x and y( y ae the mobile obot s initial positions. The next step is to epesent Equations (21, (24 and (25 in tems of the distances that the left and ight wheels have tavesed, which ae defined by d and d. This can be achieved by substituting θ and θ (in Equations (21, (24 l and (25 fo d and d, espectively, and also dopping the time constant t to achieve the following: d d (26 2 ( d d ( d d t x( t x sin sin( (27 2( d d ( d d ( d d t y( t y cos cos( 2( d d b b (28 By implementing Equations (26 to (28, they povide a solution to the elative position of a maneuveable mobile obot. This might offe a possible solution to the selflocalization poblem but is subject to accumulative dift of the position and oientation with no method of ealignment. The accuacy of this method is subject to the sampling ate of the data accumulation, such that if small position o oientation changes ae not ecoded then the position and oientation will be eoneous. 4. POYNOMIABASED NONHOONOMIC PATH PANNING AND OBSTACE AVOIDANCE This pat concentates on finding a path fo the KCBOT fom its initial configuation as descibed by (x, y, φ to a final one (x 1, y 1, φ 1. The nonholonomic constaint has to be satisfied and the thee dimensional final configuation space has to be eached with two contols only. The pape adopts a polynomial appoach to the path planning while obstacle avoidance is ealized by using the highe ode of the polynomials. The vetices and edges of the KCBOT as well as those of the obstacles ae enclosed in simple shapes such as cicles o squaes. To achieve the task of path planning detailed infomation about the tavesable space and location of potential obstacles is equied. Using the GBD image a localization map is poduced fo the path planning and obstacle avoidance. 4.1 GBD Image to 2D Envionment Mapping Befoe the autonomous mobile obot can complete any path planning o path following tasks, it equies sufficient infomation about the envionment that it will be tavesing. To povide the mobile obot with this infomation the detail fom the GBD camea is used to make plot of the teain, plotting the unobstucted space the mobile obot can utilize. Befoe the GBD image can be used, the noise esolved as black pixels in the ange of #E4 E1 Ch to #FF FF FFh needs to be emoved fom the image. This is achieved by conveting the GBD image to gay scale [23]. This pocess is caied out to potect natual colos in the #E4 E1 Ch to #FF FF FFh ange. In the GB colo model, a colo image can be epesented by the following intensity function: I GB ( F, FG, FB (29 Fom Equation (29, F is the intensity of the pixel (x,y in the ed channel, F is the intensity of pixel (x,y in the geen G channel, and F B is the intensity of pixel (x,y in the blue channel. Using only the bightness infomation the colo image can be tansfomed into a gay scale image [23]. I.333F.5 F. 1666F (3 GS Whee Equation (3 pesents the equation that convets a colo pixel to a gay scale pixel. (a G B (b Figue 8. GBD (a TO GAY SCAE (b CONVESION 6 Copyight 211 by ASME
7 Afte the image has been conveted to gay scale, as depicted in Fig. 8, the black pixels ae filteed out of the image. Figue 9. GAY SCAE FITEED IMAGE Once the image has been stipped fom the black noise pixels, as depicted in Fig. 9, the colo detail is equied fo mapping the tavesable envionment. the two diven wheels do not slip sideways. The velocity of any point on the wheel axis is nomal to this axis. This leads to following constaint equation: xsin( ycos( (31 Whee epesents the width of the obot. The above equation is a nonholonomic constaint involving velocities and, as is well known, it cannot be integated analytically to esult in a constaint between the configuation vaiables of the platfom, namely, x, y, and φ. Also, the configuation space of this system is theedimensional while the velocity space is twodimensional. The nonholonomic constaint can be witten in the fom of u xsin( y cos( v x cos( y sin( If we choose functions f and g as follows: f t, u g t, v du d (32 Figue 1. COO EMAPPING ON FITEED IMAGE The GBD depth colo infomation fom Fig. 8 (a is emapped onto the gay scale filteed image; the esult is pesented in Fig. 1. Using the HSV [24] cylindicalcoodinate epesentation of points in an GB colo model, the image is otated by 9, esulting in an image of a topological view of the tavesable space. (a Figue 11. EMAPPED GBD FITEED IMAGE OTATION The otation of the GBD image, Fig. 11 (a, esults in detailed localization mapping infomation, pesented in Fig. 11 (b, that the mobile obot can use fo path planning. 4.2 Obstacle Avoidance: A Polynomial Appoach Two independently diven wheels ae used to dive the mobile obot vehicle. It is assumed that the system moves at a low speed and the gound povides enough fiction foce. So (b and select the functions f and g to be fifth and thid ode time polynomials, we can obtain the tajectoy with obstacle avoidance. Details can be found in [25]. 6. CONCUSION & DISCUSSION This pape pesents the utilization of the Micosoft Kinect Senso to suppot the SAM methodology by exploiting the GB and GBD images fo mapping, sensing, locating, and modeling. Befoe any image pocessing is possible the image inputs ae calibated to acquie the image in a pinhole model with intinsic calibation. Using a HSV cylindicalcoodinate mapping space, a quadatic distance estimation model is pesented to esolve the estimation of a potential obstacles distance fom the senso. Using the GB and GBD images a 3D econstuction method is pesented fo envionment modeling. The KCBOT, an autonomous nonholonomic maneuveable mobile obot, is used as a platfom fo the senso to captue the expeimental images. The mobile obot is selflocalizing using the quadatue shaft encodes to esolve oientation and plana position. The mobile obot is povided with an envionment oveview map by the pesented GBD image otation method. This mapping infomation is applied to the polynomial based obstacle avoidance and path planning appoach. The expeimental images demonstate the cost effectiveness of this off the shelf senso aay. The esults show the effectiveness to poduce a 3D econstuction of an envionment and the feasibility of using the Micosoft Kinect senso fo mapping, sensing, locating, and modeling, that enables the implementation of SAM on this type of platfom. 7 Copyight 211 by ASME
8 EFEENCES [1] Amutha, B., Ponnavaikko, M., Novembe 29, "Mobile Assistant as a Navigational Aid fo Blind Childen to identify andm," Intenational Jounal of ecent Tends in Engineeing, 2(3, pp [2] Godon, S., Pang, S., Nishioka,.,Kasabov, N., and Yamakawa, T., 29, "Vision Based Mobile obot fo Indoo Envionmental Secuity," Poc. 15th Intenational Confeence on Neual Infomation Pocessing of the AsiaPacific Neual Netwok Assembly, SpingeVelag, Belin Heidelbeg, pp [3] Moi, K., Sato, M., Sonoda, T., and Ishii,K., "Towad ealization of swam intelligence," Poc. 7th PostechKyutech Joint Wokshop on Neuoinfomatics. [4] Scholtz, J., Young, J., Duy, J.., and Yanco, H.A., "Evaluation of Humanobot Inteaction Awaeness in Seach and escue," Poc. 24 IEEE Intenational Confeence on obotics and Automation, pp [5] Davids, A., 22, "Uban Seach and escue obots: Fom Tagedy to Technology," IEEE INTEIGENT SYSTEMSHistoies and Futues, pp [6] DeSouza, N. G., and Kak, A.C., 22, "Vision fo Mobile obot Navigation: A Suvey," IEEE Tansactions on Patten Analysis and Machine Intelligence, 24(2, pp [7] Chen, Z., and Bichfield, S.T., "Qualitative VisionBased Mobile obot Navigation," Poc. 26 IEEE Intenational Confeence on obotics and Automation, pp [8] i, M.H., Hong, B.., Cai, Z.S., Piao, S.H., and Huang, Q.C., 27, "Novel indoo mobile obot navigation using monocula vision," Engineeing Applications of Atificial Intelligence, 21, pp [9] Santosh, D., Acha, S., and Jawaha, C.V., "Autonomous Imagebased Exploation fo Mobile obot Navigation," Poc. 28 IEEE Intenational Confeence on obotics and Automation, pp [1] Hwang, C., and Shih, C., Mach 29, "A Distibuted ActiveVision NetwokSpace Appoach fo the Navigation of a Caike Wheeled obot," IEEE Tansactions on Industial Electonics, 56(3, pp [11] Gaspa, J., Wintes, N., and SantosVicto, J., 2, "VisionBased Navigation and Envionmental epesentations with an Omnidiectional Camea," IEEE Tansactions on obotics and Automation, 16(6, pp [12] Adoni, G., Modonini, M., Cagnoni, C., Sgobissa, A., "Omnidiectional steeo systems fo obot navigation," Poc. 23 Confeence on Compute Vision and Patten ecognition Wokshop, pp [13] ui, W.. D., and Javis,., 21, "EyeFull Towe: A GPUbased vaiable multibaseline omnidiectional steeovision system with automatic baseline selection fo outdoo mobile obot navigation," obotics and Autonomous Systems, 58, pp [14] Mejias,., Campoy, P., Mondagon, I., and Dohety, P., 35 Septembe 27, "Steeo VisionBased Navigation fo an Autonomous Helicopte," 6th IFAC Symposium on Intelligent Autonomous Vehicle. [15] Muaka, A., and Kuipes, B., "A Steeo Vision Based Mapping Algoithm fo Detecting Inclines, Dopoffs, and Obstacles fo Safe ocal Navigation," Poc. 29 IEEE/SJ Intenational Confeence on Intelligent obots and Systems, pp [16] Sabe, K., Fukuchi, M., Gutmann, J.S., Ohashi, T., Kawamoto, K., and Yoshigahaa, T., "Obstacle Avoidance and Path Planning fo Humanoid obots using Steeo Vision," Poc. 24 IEEE Intenational Confeence on obotics and Automation, pp [17] Gutmann, J.S., Fukuchi, M., and Fujita, M., 28, "3D Peception and Envionment Map Geneation fo Humanoid obot Navigation," The Intenational Jounal of obotics eseach, 27(1, pp [18] yde, J., and Hu, H., "3D ase ange Scanne with Hemispheical Field of View fo obot Navigation," Poc. 28 IEEE/ASME Intenational Confeence on Advanced Intelligent Mechatonics, pp [19] Singh, N. N., Chattejee, A., Chattejee, A., and akshit, A., 211, "A twolayeed subgoal based mobile obot navigation algoithm with vision system and I sensos," Measuement, in pess. [2] Smith, A.., "Colo gamut tansfom pais," Poc. 5th Annual Confeence on Compute Gaphics and Inteactive Techniques pp [21] Geogiou, E., 21, "The KCBOT Mobile obot," [22] Campion, G., Bastin, G., D AndeaNovel, B., 1996, "Stuctual Popeties and Classification of Kinematic and Dynamic Models of Wheeled Mobile obots," IEEE Tansactions on obotics and Automation, 12(2, pp [23] Kuma, T., and Vema, K., 21, "A Theoy Based on Convesion of GB image to Gay image," Intenational Jounal of Compute Applications, 7(2, pp [24] Joblove, G., and Geenbeg, D., "Colo spaces fo compute gaphics," Poc. 5th Annual Confeence on Compute Gaphics and Inteactive Techniques. [25] Papadopoulos, E., Poulakakis, I., and Papadimitiou, I., 22, "On Path Planning and Obstacle Avoidance fo Nonholonomic Platfoms with Manipulatos: A Polynomial Appoach," The Intenational Jounal of obotics eseach, 21(4, pp Copyight 211 by ASME
The KCLBOT: Exploiting RGBD Sensor Inputs for Navigation Environment Building and Mobile Robot Localization
The KCBOT: Exploiting GBD Senso Inputs fo Navigation Envionment Building and Mobile obot ocalization egula Pape Evangelos Geogiou 1,*, Jian Dai 1 and Michael uck 1 1 King s College ondon *Coesponding
More informationMassachusetts Institute of Technology Department of Mechanical Engineering
cm cm Poblem Massachusetts Institute of echnolog Depatment of Mechanical Engineeing. Intoduction to obotics Sample Poblems and Solutions fo the Midem Exam Figue shows a obotic vehicle having two poweed
More informationFifth Wheel Modelling and Testing
Fifth heel Modelling and Testing en Masoy Mechanical Engineeing Depatment Floida Atlantic Univesity Boca aton, FL 4 Lois Malaptias IFMA Institut Fancais De Mechanique Advancee ampus De lemont Feand Les
More informationJournal of World s Electrical Engineering and Technology J. World. Elect. Eng. Tech. 1(1): 1216, 2012
2011, Scienceline Publication www.scienceline.com Jounal of Wold s Electical Engineeing and Technology J. Wold. Elect. Eng. Tech. 1(1): 1216, 2012 JWEET An Efficient Algoithm fo Lip Segmentation in Colo
More informationControlled Information Maximization for SOM Knowledge Induced Learning
3 Int'l Conf. Atificial Intelligence ICAI'5 Contolled Infomation Maximization fo SOM Knowledge Induced Leaning Ryotao Kamimua IT Education Cente and Gaduate School of Science and Technology, Tokai Univeisity
More informationObstacle Avoidance of Autonomous Mobile Robot using Stereo Vision Sensor
Obstacle Avoidance of Autonomous Mobile Robot using Steeo Vision Senso Masako Kumano Akihisa Ohya Shin ichi Yuta Intelligent Robot Laboatoy Univesity of Tsukuba, Ibaaki, 358573 Japan Email: {masako,
More informationAugmented Reality. Integrating Computer Graphics with Computer Vision Mihran Tuceryan. August 16, 1998 ICPR 98 1
Augmented Reality Integating Compute Gaphics with Compute Vision Mihan Tuceyan August 6, 998 ICPR 98 Definition XCombines eal and vitual wolds and objects XIt is inteactive and ealtime XThe inteaction
More informationProf. Feng Liu. Fall /17/2016
Pof. Feng Liu Fall 26 http://www.cs.pdx.edu/~fliu/couses/cs447/ /7/26 Last time Compositing NPR 3D Gaphics Toolkits Tansfomations 2 Today 3D Tansfomations The Viewing Pipeline Midtem: in class, Nov. 2
More information(a, b) x y r. For this problem, is a point in the  coordinate plane and is a positive number.
Illustative GC Simila cicles Alignments to Content Standads: GC.A. Task (a, b) x y Fo this poblem, is a point in the  coodinate plane and is a positive numbe. a. Using a tanslation and a dilation, show
More informationIP Network Design by Modified Branch Exchange Method
Received: June 7, 207 98 IP Netwok Design by Modified Banch Method Kaiat Jaoenat Natchamol Sichumoenattana 2* Faculty of Engineeing at Kamphaeng Saen, Kasetsat Univesity, Thailand 2 Faculty of Management
More informationView Synthesis using Depth Map for 3D Video
View Synthesis using Depth Map fo 3D Video Cheon Lee and YoSung Ho Gwangju Institute of Science and Technology (GIST) 1 Oyongdong, Bukgu, Gwangju, 500712, Republic of Koea Email: {leecheon, hoyo}@gist.ac.k
More informationCoordinate Systems. Ioannis Rekleitis
Coodinate Systems Ioannis ekleitis Position epesentation Position epesentation is: P p p p x y z P CS417 Intoduction to obotics and Intelligent Systems Oientation epesentations Descibes the otation of
More informationMULTITEMPORAL AND MULTISENSOR IMAGE MATCHING BASED ON LOCAL FREQUENCY INFORMATION
Intenational Achives of the Photogammety Remote Sensing and Spatial Infomation Sciences Volume XXXIXB3 2012 XXII ISPRS Congess 25 August 01 Septembe 2012 Melboune Austalia MULTITEMPORAL AND MULTISENSOR
More informationTransmission Lines Modeling Based on Vector Fitting Algorithm and RLC Active/Passive Filter Design
Tansmission Lines Modeling Based on Vecto Fitting Algoithm and RLC Active/Passive Filte Design Ahmed Qasim Tuki a,*, Nashien Fazilah Mailah b, Mohammad Lutfi Othman c, Ahmad H. Saby d Cente fo Advanced
More informationDetection and tracking of ships using a stereo vision system
Scientific Reseach and Essays Vol. 8(7), pp. 288303, 18 Febuay, 2013 Available online at http://www.academicjounals.og/sre DOI: 10.5897/SRE12.318 ISSN 19922248 2013 Academic Jounals Full Length Reseach
More informationEgoMotion Estimation on Range Images using HighOrder Polynomial Expansion
EgoMotion Estimation on Range Images using HighOde Polynomial Expansion Bian Okon and Josh Haguess Space and Naval Wafae Systems Cente Pacific San Diego, CA, USA {bian.okon,joshua.haguess}@navy.mil Abstact
More information3D inspection system for manufactured machine parts
3D inspection system fo manufactued machine pats D. Gacía a*, J. M. Sebastián a*, F. M. Sánchez a*, L. M. Jiménez b*, J. M. González a* a Dept. of System Engineeing and Automatic Contol. Polytechnic Univesity
More informationTopic 3 Image Enhancement
Topic 3 Image Enhancement (Pat 1) DIP: Details Digital Image Pocessing Digital Image Chaacteistics Spatial Spectal Gaylevel Histogam DFT DCT PePocessing Enhancement Restoation Point Pocessing Masking
More informationHaptic Glove. ChanSu Lee. Abstract. This is a final report for the DIMACS grant of studentinitiated project. I implemented Boundary
Physically Accuate Haptic Rendeing of Elastic Object fo a Haptic Glove ChanSu Lee Abstact This is a final epot fo the DIMACS gant of studentinitiated poject. I implemented Bounday Element Method(BEM)
More informationCSE 165: 3D User Interaction
CSE 165: 3D Use Inteaction Lectue #6: Selection Instucto: Jugen Schulze, Ph.D. 2 Announcements Homewok Assignment #2 Due Fiday, Januay 23 d at 1:00pm 3 4 Selection and Manipulation 5 Why ae Selection and
More informationImage Enhancement in the Spatial Domain. Spatial Domain
8 Spatial Domain Image Enhancement in the Spatial Domain What is spatial domain The space whee all pixels fom an image In spatial domain we can epesent an image by f( whee x and y ae coodinates along
More informationMultiazimuth Prestack Time Migration for General Anisotropic, Weakly Heterogeneous Media  Field Data Examples
Multiazimuth Pestack Time Migation fo Geneal Anisotopic, Weakly Heteogeneous Media  Field Data Examples S. Beaumont* (EOST/PGS) & W. Söllne (PGS) SUMMARY Multiazimuth data acquisition has shown benefits
More informationAssessment of Track Sequence Optimization based on Recorded Field Operations
Assessment of Tack Sequence Optimization based on Recoded Field Opeations Matin A. F. Jensen 1,2,*, Claus G. Søensen 1, Dionysis Bochtis 1 1 Aahus Univesity, Faculty of Science and Technology, Depatment
More informationExtract Object Boundaries in Noisy Images using Level Set. Final Report
Extact Object Boundaies in Noisy Images using Level Set by: Quming Zhou Final Repot Submitted to Pofesso Bian Evans EE381K Multidimensional Digital Signal Pocessing May 10, 003 Abstact Finding object contous
More information= dv 3V (r + a 1) 3 r 3 f(r) = 1. = ( (r + r 2
Random Waypoint Model in ndimensional Space Esa Hyytiä and Joma Vitamo Netwoking Laboatoy, Helsinki Univesity of Technology, Finland Abstact The andom waypoint model (RWP) is one of the most widely used
More informationPROBABILITYBASED OPTIMAL PATH PLANNING FOR TWOWHEELED MOBILE ROBOTS
Poceedings of the ASME 215 Dynamic Systems and Contol Confeence DSCC215 Octobe 283, 215, Columbus, Ohio, USA DSCC215999 PROBABILITYBASED OPTIMAL PATH PLANNING FOR TWOWHEELED MOBILE ROBOTS Jaeyeon Lee
More informationA VISIONBASED UNMANNED AERIAL VEHICLE NAVIGATION METHOD
st Intenational Confeence on Innovative Reseach and Maitime Applications of Space Technology IRMAST 5 A VISIOBASED UMAED AERIAL VEHICLE AVIGATIO METHOD Paweł Budziakowski, Maek Pzyboski, Jakub Szulwic
More informationDense pointclouds from combined nadir and oblique imagery by objectbased semiglobal multiimage matching
Dense pointclouds fom combined nadi and oblique imagey by objectbased semiglobal multiimage matching Y X Thomas Luhmann, Folkma Bethmann & Heidi Hastedt Jade Univesity of Applied Sciences, Oldenbug,
More informationCellular Neural Network Based PTV
3th Int Symp on Applications of Lase Techniques to Fluid Mechanics Lisbon, Potugal, 69 June, 006 Cellula Neual Netwok Based PT Kazuo Ohmi, Achyut Sapkota : Depatment of Infomation Systems Engineeing,
More informationHand Tracking and Gesture Recognition for HumanComputer Interaction
Electonic Lettes on Compute Vision and Image Analysis 5(3):96104, 2005 Hand Tacking and Gestue Recognition fo HumanCompute Inteaction Cistina Manesa, Javie Vaona, Ramon Mas and Fancisco J. Peales Unidad
More informationOn Error Estimation in RungeKutta Methods
Leonado Jounal of Sciences ISSN 15830233 Issue 18, JanuayJune 2011 p. 110 On Eo Estimation in RungeKutta Methods Ochoche ABRAHAM 1,*, Gbolahan BOLARIN 2 1 Depatment of Infomation Technology, 2 Depatment
More informationXFVHDL: A Tool for the Synthesis of Fuzzy Logic Controllers
XFVHDL: A Tool fo the Synthesis of Fuzzy Logic Contolles E. Lago, C. J. Jiménez, D. R. López, S. SánchezSolano and A. Baiga Instituto de Micoelectónica de Sevilla. Cento Nacional de Micoelectónica, Edificio
More informationSYSTEM LEVEL REUSE METRICS FOR OBJECT ORIENTED SOFTWARE : AN ALTERNATIVE APPROACH
I J C A 7(), 202 pp. 4953 SYSTEM LEVEL REUSE METRICS FOR OBJECT ORIENTED SOFTWARE : AN ALTERNATIVE APPROACH Sushil Goel and 2 Rajesh Vema Associate Pofesso, Depatment of Compute Science, Dyal Singh College,
More informationANALYSIS TOOL AND COMPUTER SIMULATION OF A DOUBLE LOBED HYPERBOLIC OMNIDIRECTIONAL CATADIOPTRIC VISION SYSTEM
Copyight 04 y ABCM ANALYSIS TOOL AND COMPUTER SIMULATION OF A DOUBLE LOBED HYPERBOLIC OMNIDIRECTIONAL CATADIOPTRIC VISION SYSTEM Macello Mainho Rieio, macello@un. José Mauício S. T. da Motta, jmmotta@un.
More informationRealTime SpeechDriven Face Animation. Pengyu Hong, Zhen Wen, Tom Huang. Beckman Institute for Advanced Science and Technology
RealTime SpeechDiven Face Animation Pengyu Hong, Zhen Wen, Tom Huang Beckman Institute fo Advanced Science and Technology Univesity of Illinois at UbanaChampaign, Ubana, IL 61801, USA Abstact This chapte
More informationSeveral algorithms exist to extract edges from point. system. the line is computed using a least squares method.
Fast Mapping using the LogHough Tansfomation B. Giesle, R. Gaf, R. Dillmann Institute fo Pocess Contol and Robotics (IPR) Univesity of Kalsuhe D7618 Kalsuhe, Gemany fgieslejgafjdillmanng@ia.uka.de C.F.R.
More informationPointBiserial Correlation Analysis of Fuzzy Attributes
Appl Math Inf Sci 6 No S pp 439S444S (0 Applied Mathematics & Infomation Sciences An Intenational Jounal @ 0 NSP Natual Sciences Publishing o Pointiseial oelation Analysis of Fuzzy Attibutes HaoEn hueh
More informationLIDAR SYSTEM CALIBRATION USING OVERLAPPING STRIPS
LIDR SYSTEM CLIRTION USIN OVERLPPIN STRIPS Calibação do sistema LiDR utilizando faias sobepostas KI IN N 1 YMN F. HI 1 MURICIO MÜLLER 2 1 Dept. of eomatics Engineeing, Univesity of Calgay, 25 Univesity
More informationAccurate Diffraction Efficiency Control for Multiplexed Volume Holographic Gratings. Xuliang Han, Gicherl Kim, and Ray T. Chen
Accuate Diffaction Efficiency Contol fo Multiplexed Volume Hologaphic Gatings Xuliang Han, Gichel Kim, and Ray T. Chen Micoelectonic Reseach Cente Depatment of Electical and Compute Engineeing Univesity
More informationWearable inertial sensors for arm motion tracking in homebased rehabilitation
Book Title Book Editos IOS Pess, 005 Weaable inetial sensos fo am motion tacking in homebased ehabilitation Huiyu Zhou a,, Huosheng Hu a and Nigel Hais b a Univesity of Essex, Colcheste, CO4 3SQ, UK b
More informationSCR R&D and control development combining GTSUITE and TNO models. GTI userconference
SCR R&D and contol development combining GSUIE and NO models GI useconfeence Contents Intoduction: Bidging the gap fom R&D to ECU implementation Contol development at NO Implementation of NO models in
More informationHigh performance CUDA based CNN image processor
High pefomance UDA based NN image pocesso GEORGE VALENTIN STOIA, RADU DOGARU, ELENA RISTINA STOIA Depatment of Applied Electonics and Infomation Engineeing Univesity Politehnica of Buchaest 3, Iuliu Maniu
More informationRobust Object Detection at Regions of Interest with an Application in Ball Recognition
Robust Object Detection at Regions of Inteest with an Application in Ball Recognition Saa Miti, Simone Fintop, Kai Pevölz, Hatmut Sumann Faunhofe Institute fo Autonomous Intelligent Systems (AIS) Schloss
More informationSimulation and Performance Evaluation of Network on Chip Architectures and Algorithms using CINSIM
J. Basic. Appl. Sci. Res., 1(10)15941602, 2011 2011, TextRoad Publication ISSN 2090424X Jounal of Basic and Applied Scientific Reseach www.textoad.com Simulation and Pefomance Evaluation of Netwok on
More informationINFORMATION DISSEMINATION DELAY IN VEHICLETOVEHICLE COMMUNICATION NETWORKS IN A TRAFFIC STREAM
INFORMATION DISSEMINATION DELAY IN VEHICLETOVEHICLE COMMUNICATION NETWORKS IN A TRAFFIC STREAM LiLi Du Depatment of Civil, Achitectual, and Envionmental Engineeing Illinois Institute of Technology 3300
More informationAN ARTIFICIAL NEURAL NETWORK BASED ROTATION CORRECTION METHOD FOR 3DMEASUREMENT USING A SINGLE PERSPECTIVE VIEW
Ma 8, 9 and 30, 1997 Le Ceusot, Bougogne, FRANCE AN ARIFICIAL NEURAL NEWORK BASED ROAION CORRECION MEHOD FOR 3DMEASUREMEN USING A SINGLE PERSPECIVE VIEW Kauko Väinämö, Juha Röning Depatment of Electical
More informationINTERACTIVE RELATIVE ORIENTATION BETWEEN TERRESTRIAL IMAGES AND AIRBORNE LASER SCANNING DATA
INTERACTIVE RELATIVE ORIENTATION BETWEEN TERRESTRIAL IMAGES AND AIRBORNE LASER SCANNING DATA Peti Rönnholm *, Hannu Hyyppä, Pettei Pöntinen, Henik Haggén Institute of Photogammety and Remote Sensing, Helsinki
More informationIntroduction to Medical Imaging. ConeBeam CT. Introduction. Available conebeam reconstruction methods: Our discussion:
Intoduction Intoduction to Medical Imaging ConeBeam CT Klaus Muelle Available conebeam econstuction methods: exact appoximate Ou discussion: exact (now) appoximate (next) The Radon tansfom and its invese
More information3D Periodic Human Motion Reconstruction from 2D Motion Sequences
3D Peiodic Human Motion Reconstuction fom D Motion Sequences Zonghua Zhang and Nikolaus F. Toje BioMotionLab, Depatment of Psychology Queen s Univesity, Canada zhang, toje@psyc.queensu.ca Abstact In this
More informationEffects of Model Complexity on Generalization Performance of Convolutional Neural Networks
Effects of Model Complexity on Genealization Pefomance of Convolutional Neual Netwoks TaeJun Kim 1, Dongsu Zhang 2, and Joon Shik Kim 3 1 Seoul National Univesity, Seoul 151742, Koea, Email: tjkim@bi.snu.ac.k
More informationThreeDimensional Aerodynamic Design Optimization of a Turbine Blade by Using an Adjoint Method
Jiaqi Luo email: jiaqil@uci.edu Juntao Xiong Feng Liu email: fliu@uci.edu Depatment of Mechanical and Aeospace Engineeing, Univesity of Califonia, Ivine, Ivine, CA 926973975 Ivan McBean Alstom Powe
More informationApproximating Euclidean Distance Transform with Simple Operations in Cellular Processor Arrays
00 th Intenational Wokshop on Cellula Nanoscale Netwoks and thei Applications (CNNA) Appoximating Euclidean Distance Tansfom with Simple Opeations in Cellula Pocesso Aas Samad Razmjooei and Piot Dudek
More informationParametric Scattering Models for Bistatic Synthetic Aperture Radar
Paametic Scatteing Models fo Bistatic Synthetic Apetue Rada Julie Ann Jackson Student Membe, Bian D. Rigling Membe, Randolph L. Moses Senio Membe The Ohio State Univesity, Dept. of Electical and Compute
More informationA RealTime Foveated Senso with Ovelapping Receptive Fields Mac Bolduc and Matin D. Levine Cente fo Intelligent Machines McGill Univesity, 3480 Univesity St., Monteal, Quebec, Canada H3A 2A7 email: fbolduc,levineg@cim.mcgill.edu
More informationDEADLOCK AVOIDANCE IN BATCH PROCESSES. M. Tittus K. Åkesson
DEADLOCK AVOIDANCE IN BATCH PROCESSES M. Tittus K. Åkesson Univesity College Boås, Sweden, email: Michael.Tittus@hb.se Chalmes Univesity of Technology, Gothenbug, Sweden, email: ka@s2.chalmes.se Abstact:
More informationAny modern computer system will incorporate (at least) two levels of storage:
1 Any moden compute system will incopoate (at least) two levels of stoage: pimay stoage: andom access memoy (RAM) typical capacity 32MB to 1GB cost pe MB $3. typical access time 5ns to 6ns bust tansfe
More informationUCLA Papers. Title. Permalink. Authors. Publication Date. Localized Edge Detection in Sensor Fields. https://escholarship.org/uc/item/3fj6g58j
UCLA Papes Title Localized Edge Detection in Senso Fields Pemalink https://escholashipog/uc/item/3fj6g58j Authos K Chintalapudi Govindan Publication Date 3 Pee eviewed escholashipog Poweed by the Califonia
More informationGeneralized Grey Target Decision Method Based on Decision Makers Indifference Attribute Value Preferences
Ameican Jounal of ata ining and Knowledge iscovey 27; 2(4): 28 http://www.sciencepublishinggoup.com//admkd doi:.648/.admkd.2724.2 Genealized Gey Taget ecision ethod Based on ecision akes Indiffeence Attibute
More informationThe EigenRumor Algorithm for Ranking Blogs
he EigenRumo Algoithm fo Ranking Blogs Ko Fujimua N Cybe Solutions Laboatoies N Copoation akafumi Inoue N Cybe Solutions Laboatoies N Copoation Masayuki Sugisaki N Resonant Inc. ABSRAC he advent of easy
More informationA METHOD FOR INTERACTIVE ORIENTATION OF DIGITAL IMAGES USING BACKPROJECTION OF 3D DATA
The Photogammetic Jounal of Finland, Vol. 18, No. 2, 23 A METHOD FOR INTERACTIVE ORIENTATION OF DIGITAL IMAGES USING BACKPROJECTION OF 3D DATA Peti Rönnholm 1, Hannu Hyyppä 1, Pettei Pöntinen 1, Henik
More informationMultiview plus depth video coding with temporal prediction view synthesis
1 Multiview plus depth video coding with tempoal pediction view synthesis Andei I. Puica, Elie G. Moa, Beatice PesquetPopescu, Fellow, IEEE, Maco Cagnazzo, Senio Membe, IEEE and Bogdan Ionescu, Senio
More informationKenong Wu and Martin D. Levine. McGill University, Montreal, Quebec, Canada, H3A 2A7. The work reported in this paper is also boundarybased
3D PART EGMENTATION UING IMULATED ELECTRICAL CHARGE DITRIBUTION Kenong Wu and Matin D. Levine Cente fo Intelligent Machines & Dept. of Electical Engineeing McGill Univesity, Monteal, Quebec, Canada, H3A
More informationAN ANALYSIS OF COORDINATED AND NONCOORDINATED MEDIUM ACCESS CONTROL PROTOCOLS UNDER CHANNEL NOISE
AN ANALYSIS OF COORDINATED AND NONCOORDINATED MEDIUM ACCESS CONTROL PROTOCOLS UNDER CHANNEL NOISE Tolga Numanoglu, Bulent Tavli, and Wendi Heinzelman Depatment of Electical and Compute Engineeing Univesity
More informationPoint Similarity Measures Based on MRF Modeling of Difference Images for SplineBased 2D3D Rigid Registration of Xray Fluoroscopy to CT Images
Point Similaity Measues Based on MRF Modeling of Diffeence Images fo SplineBased DD Rigid Registation of Xay Fluooscopy to CT Images Guoyan Zheng, Xuan Zhang, Slavica Jonić,, Philippe Thévenaz, Michael
More informationFullPolarimetric Analysis of MERIC Air Targets Data
ABSTRACT FullPolaimetic Analysis of MERIC Ai Tagets Data C. TitinSchnaide, P. Bouad ONERA Chemin de la Hunièe et des Joncheettes 91120 Palaiseau Fance Cecile.TitinSchnaide@onea.f, Philippe.Bouad@onea.f
More informationA MULTIRESOLUTION AND OPTIMIZATIONBASED IMAGE MATCHING APPROACH: AN APPLICATION TO SURFACE RECONSTRUCTION FROM SPOT5HRS STEREO IMAGERY
A MULTIRESOLUTION AND OPTIMIZATIONBASED IMAGE MATCHING APPROACH: AN APPLICATION TO SURFACE RECONSTRUCTION FROM SPOT5HRS STEREO IMAGERY M. PieotDeseillign N. Papaoditis MATIS laboato Institut Géogaphique
More informationAn Optimised Density Based Clustering Algorithm
Intenational Jounal of Compute Applications (0975 8887) Volume 6 No.9, Septembe 010 An Optimised Density Based Clusteing Algoithm J. Hencil Pete Depatment of Compute Science St. Xavie s College, Palayamkottai,
More informationA Unified Approach to Moving Object Detection in 2D and 3D Scenes
IEEE RANSACIONS ON PAERN ANALYSIS AND MACINE INELLIGENCE, VOL. 0, NO. 6, JUNE 998 577 A Unified Appoach to Moving Object Detection in D and 3D Scenes Michal Iani and P. Anandan Abstact he detection of
More informationFINITE ELEMENT MODEL UPDATING OF AN EXPERIMENTAL VEHICLE MODEL USING MEASURED MODAL CHARACTERISTICS
COMPDYN 009 ECCOMAS Thematic Confeence on Computational Methods in Stuctual Dynamics and Eathquake Engineeing M. Papadakakis, N.D. Lagaos, M. Fagiadakis (eds.) Rhodes, Geece, 4 June 009 FINITE ELEMENT
More informationAddress for Correspondence 1 P.G. Student (Computer and Communication), 2 Associate Professor
Reseach Aticle BIOMETRIC AUTHENTICATION USING NEAR INFRARED IMAGES OF PALM DORSAL VEIN PATTERNS M.Rajalakshmi 1, R.Rengaaj 2 Addess fo Coespondence 1 P.G. Student (Compute and Communication), 2 Associate
More informationPrediction of Time Series Using RBF Neural Networks: A New Approach of Clustering
138 The Intenational Aab Jounal of Infomation Technology, Vol. 6,. 2, Apil 2009 Pediction of Time Seies Using RBF Neual Netwoks: A New Appoach of Clusteing Mohammed Awad 2, Hécto Pomaes 1, Ignacio Rojas
More informationEQUATIONS can at times be tedious to understand
DIGITAL IMAGE PROCESSING COURSE PROJECT, JUNE 2013 1 Augmented Reality Equation Plotte Salman Naqvi, Uzai Sikoa Digital Image Pocessing EE 368/ CS 232 Abstact Gaphical Visualization of an equation can
More informationThreat assessment for avoiding collisions with turning vehicles
Theat assessment fo avoiding collisions with tuning vehicles Mattias Bännstöm, Eik Coelingh and Jonas Sjöbeg Abstact This pape pesents a method fo estimating how the dive of a vehicle can use steeing,
More informationFinite element model
Loughboough Univesity Institutional Repositoy Finite element model updating of an expeimental vehicle model using measued modal chaacteistics This item was submitted to Loughboough Univesity's Institutional
More informationIn ancient western art, compositions
Featue Aticle Digital Route Panoamas Route panoama is a new image medium fo digitally achiving and visualizing scenes along a oute. It s suitable fo egistation, tansmission, and visualization of oute scenes.
More informationFree Viewpoint Action Recognition using Motion History Volumes
Fee Viewpoint Action Recognition using Motion Histoy Volumes Daniel Weinland 1, Remi Ronfad, Edmond Boye PeceptionGRAVIR, INRIA RhoneAlpes, 38334 Montbonnot Saint Matin, Fance. Abstact Action ecognition
More informationSignal integrity analysis and physically based circuit extraction of a mounted
emc design & softwae Signal integity analysis and physically based cicuit extaction of a mounted SMA connecto A poposed geneal appoach is given fo the definition of an equivalent cicuit with SMAs mounted
More informationExperimental and numerical simulation of the flow over a spillway
Euopean Wate 57: 253260, 2017. 2017 E.W. Publications Expeimental and numeical simulation of the flow ove a spillway A. Seafeim *, L. Avgeis, V. Hissanthou and K. Bellos Depatment of Civil Engineeing,
More informationAXON 2 A visual object recognition system for nonrigid objects
AXON 2 A visual object ecognition system fo nonigid objects PABLO ALVARADO, PEER DÖRFLER, JOCHEN WICKEL Depatment of echnical Compute Science RWH Aachen, Gemany alvaado doefle,wickel @techinfo.wthaachen.de
More informationRanking Visualizations of Correlation Using Weber s Law
Ranking Visualizations of Coelation Using Webe s Law Lane Haison, Fumeng Yang, Steven Fanconei, Remco Chang Abstact Despite yeas of eseach yielding systems and guidelines to aid visualization design, pactitiones
More informationConversion Functions for Symmetric Key Ciphers
Jounal of Infomation Assuance and Secuity 2 (2006) 41 50 Convesion Functions fo Symmetic Key Ciphes Deba L. Cook and Angelos D. Keomytis Depatment of Compute Science Columbia Univesity, mail code 0401
More informationIntelligent telerobotic assistance for enhancing manipulation capabilities of persons with disabilities
Univesity of South Floida Schola Commons Gaduate Theses and Dissetations Gaduate School 4 Intelligent teleobotic assistance fo enhancing manipulation capabilities of pesons with disabilities Wentao, Yu
More informationUsing Data Flow Diagrams for Supporting Task Models
in Companion Poc. of 5 th Euogaphics Wokshop on Design, Specification, Veification of Inteactive Systems DSVIS 98 (Abingdon, 35 June 1998), P. Makopoulos & P. Johnson (Eds.), SpingeVelag, Belin, 1998.
More informationVisual Servoing from Deep Neural Networks
Visual Sevoing fom Deep Neual Netwoks Quentin Bateux 1, Eic Machand 1, Jügen Leitne 2, Fançois Chaumette 3, Pete Coke 2 Abstact We pesent a deep neual netwokbased method to pefom highpecision, obust
More informationExtracting Articulation Models from CAD Models of Parts with Curved Surfaces
Extacting Aticulation Models fom CAD Models of Pats with Cuved Sufaces Rajaishi Sinha 1,*, Satyanda K. Gupta 2, Chistiaan J.J. Paedis 1, Padeep K. Khosla 1 1 Institute fo Complex Engineeed Systems, Canegie
More informationPrioritized Traffic Recovery over GMPLS Networks
Pioitized Taffic Recovey ove GMPLS Netwoks 2005 IEEE. Pesonal use of this mateial is pemitted. Pemission fom IEEE mu be obtained fo all othe uses in any cuent o futue media including epinting/epublishing
More informationErasureCoding Based Routing for Opportunistic Networks
EasueCoding Based Routing fo Oppotunistic Netwoks Yong Wang, Sushant Jain, Magaet Matonosi, Kevin Fall Pinceton Univesity, Univesity of Washington, Intel Reseach Bekeley ABSTRACT Routing in Delay Toleant
More informationCommunication vs Distributed Computation: an alternative tradeoff curve
Communication vs Distibuted Computation: an altenative tadeoff cuve Yahya H. Ezzeldin, Mohammed amoose, Chistina Fagouli Univesity of Califonia, Los Angeles, CA 90095, USA, Email: {yahya.ezzeldin, mkamoose,
More informationTwoDimensional Coding for Advanced Recording
TwoDimensional Coding fo Advanced Recoding N. Singla, J. A. O Sullivan, Y. Wu, and R. S. Indec Washington Univesity Saint Louis, Missoui s Motivation: Aeal Density Pefomance: match medium, senso, pocessing
More informationA MULTIRESOLUTION AND OPTIMIZATIONBASED IMAGE MATCHING APPROACH: AN APPLICATION TO SURFACE RECONSTRUCTION FROM SPOT5HRS STEREO IMAGERY
M. PieotDeseillign N. Papaoditis. A multiesolution and optimizationbased image matching appoach: An application to suface econstuction fom SPOT5HRS steeo imagey. In IAPRS vol XXXVI1/W41 in ISPRS Wokshop
More informationInterferenceAware Multicast for Wireless Multihop Networks
IntefeenceAwae Multicast fo Wieless Multihop Netwoks Daniel Letpatchya School of Electical and Compute Engineeing Geogia Institute of Technology Atlanta, Geogia 30332 0250 Douglas M. Blough School of
More informationUsing the PiP model for fast calculation of vibration from a railway tunnel in a multilayered halfspace
Using the PiP model fo fast calculation of vibation fom a ailway tunnel in a multilayeed halfspace M.F.M. Hussein a, H.E.M. Hunt b, L. Rikse c, S. Gupta c, G. Degande c, J.P. Talbot d, S. Fancois c,
More informationFuzzy Logic Resource Management and Coevolutionary Gamebased Optimization
Naval Reseach Laboatoy Washington, DC 203755320 NRL/FR/57410110001 Fuzzy Logic Resouce Management and Coevolutionay Gamebased Optimization JAMES F. SMITH III ROBERT D. RHYNE II Suface EW Systems Banch
More informationUsing SPEC SFS with the SNIA Emerald Program for EPA Energy Star Data Center Storage Program Vernon Miller IBM Nick Principe Dell EMC
Using SPEC SFS with the SNIA Emeald Pogam fo EPA Enegy Sta Data Cente Stoage Pogam Venon Mille IBM Nick Pincipe Dell EMC v6 Agenda Backgound on SNIA Emeald/Enegy Sta fo block Intoduce NAS/File test addition;
More informationHierarchical Region MeanBased Image Segmentation
Hieachical Region MeanBased Image Segmentation Slawo Wesolkowski and Paul Fieguth Systems Design Engineeing Univesity of Wateloo Wateloo, Ontaio, Canada, N2L3G1 s.wesolkowski@ieee.og, pfieguth@uwateloo.ca
More informationModeling Spatially Correlated Data in Sensor Networks
Modeling Spatially Coelated Data in Senso Netwoks Apoova Jindal and Konstantinos Psounis Univesity of Southen Califonia The physical phenomena monitoed by senso netwoks, e.g. foest tempeatue, wate contamination,
More informationEvaluation of Partial Path Queries on XML data
Evaluation of Patial Path Queies on XML data Stefanos Souldatos Dept of EE & CE, NTUA stef@dblab.ntua.g Theodoe Dalamagas Dept of EE & CE, NTUA dalamag@dblab.ntua.g Xiaoying Wu Dept. of CS, NJIT xw43@njit.edu
More informationTCBAC: An Access Control Model for Remote Calibration System
JOURNAL OF SOFTWARE, VOL. 8, NO., DECEMBER 03 339 TCBAC: An Access Contol Model fo Remote Calibation System Zhuokui Wu School of Mechanical & Automotive Engineeing, South China Univesity of Technology,
More informationAn EnergyEfficient Approach for Provenance Transmission in Wireless Sensor Networks
An EnegyEfficient Appoach fo Povenance Tansmission in Wieless Senso Netwoks S. M. Iftekhaul Alam Pudue Univesity alams@pudue.edu Sonia Fahmy Pudue Univesity fahmy@cs.pudue.edu Abstact Assessing the tustwothiness
More informationEstimation of the Knee FlexionExtension Angle During Dynamic Sport Motions Using Bodyworn Inertial Sensors
Estimation of the Knee FlexionExtension Angle Duing Dynamic Spot Motions Using Bodywon Inetial Sensos Caolin Jakob caolin.jakob@medtech.stud.unielangen.de Patick Kugle patick.kugle@cs.fau.de Felix Hebensteit,
More information