Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Effect of optical aberrations on the colour appearance of small lights

Purpose: Small red lights (one minute of arc or less) change colour appearance with positive defocus. We investigated the influence of longitudinal chromatic aberration and monochromatic aberrations on the colour appearance of small narrow band lights. Methods: Seven cyclopleged, trichromatic observers viewed a small light (one minute of arc, λmax = 510, 532, 550, 589, 620, 628 nm, approximately 19 per cent Weber contrast) centred within a black annulus (4.5 minutes of arc) and surrounded by a uniform white field (2,170 cd/m2). Pupil size was four millimetres. An optical trombone varied focus. Longitudinal chromatic aberration was controlled with a two component Powell achromatising lens that neutralises the eye’s chromatic aberration; a doublet that doubles and a triplet that reverses the eye’s chromatic aberration. Astigmatism and higher order monochromatic aberrations were corrected using adaptive optics. Results: Observers reported a change in appearance of the small red light (628 nm) without the Powell lens at +0.49 ± 0.21 D defocus and with the doublet at +0.62 ± 0.16 D. Appearance did not alter with the Powell lens, and five of seven observers reported the phenomenon with the triplet for negative defocus (-0.80 ± 0.47 D). Correction of aberrations did not significantly affect the magnitude at which the appearance of the red light changed (+0.44 ± 0.18 D without correction; +0.46 ± 0.16 D with correction). The change in colour appearance with defocus extended to other wavelengths (λmax = 510 to 620 nm), with directions of effects being reversed for short wavelengths relative to long wavelengths. Conclusions: Longitudinal chromatic aberrations but not monochromatic aberrations are involved in changing the appearance of small lights with defocus.

Optical absorption, infrared, Raman and EPR spectral studies on natural Iowaite mineral

Natural iowaite, magnesium–ferric oxychloride mineral having light green color originating from Australia has been characterized by EPR, optical, IR, and Raman spectroscopy. The optical spectrum exhibits a number of electronic bands due to both Fe(III) and Mn(II) ions in iowaite. From EPR studies, the g values are calculated for Fe(III) and g and A values for Mn(II). EPR and optical absorption studies confirm that Fe(III) and Mn(II) are in distorted octahedral geometry. The bands that appear both in NIR and Raman spectra are due to the overtones and combinations of water and carbonate molecules. Thus EPR, optical, and Raman spectroscopy have proven most useful for the study of the chemistry of natural iowaite and chemical changes in the mineral.

Obstacle detection using optical flow

Motion has been examined in biology to be a critical component for obstacle avoidance and navigation. In particular, optical flow is a powerful motion cue that has been exploited in many biological systems for survival. In this paper, we investigate an obstacle detection system that uses optical flow to obtain range information to objects. Our experimental results demonstrate that optical flow is capable of providing good obstacle information but has obvious failure modes. We acknowledge that our optical flow system has certain disadvantages and cannot be solely used for navigation. Instead, we believe that optical flow is a critical visual subsystem used when moving at reason- able speeds. When combined with other visual subsystems, considerable synergy can result.

Learning to avoid indoor obstacles from optical flow

Optical flow (OF) is a powerful motion cue that captures the fusion of two important properties for the task of obstacle avoidance − 3D self-motion and 3D environmental surroundings. The problem of extracting such information for obstacle avoidance is commonly addressed through quantitative techniques such as time-to-contact and divergence, which are highly sensitive to noise in the OF image. This paper presents a new strategy towards obstacle avoidance in an indoor setting, using the combination of quantitative and structural properties of the OF field, coupled with the flexibility and efficiency of a machine learning system.The resulting system is able to effectively control the robot in real-time, avoiding obstacles in familiar and unfamiliar indoor environments, under given motion constraints. Furthermore, through the examination of the networks internal weights, we show how OF properties are being used toward the detection of these indoor obstacles.

Optical absorption near infrared and EPR studies of mottramite

Mottramite mineral originated from Tsumeb Corporation Mine, Tsumeb, Otavi, Namibia, is used in the present work. The mineral contains of vanadium and copper to the extent of 22.73% and 16.84% by weight respectively as V2O5 and CuO. An EPR study of sample confirms the presence of Cu(II) with g = 2.2. Optical absorption spectrum of mottramite indicates that Cu(II) is present in rhombic environment. NIR results are due to water fundamentals.

EPR and optical absorption spectral studies on voglite mineral

A voglite mineral sample of Volrite Canyon #1 mine, Frey Point, White Canyon Mine District, San Juan County, Utah, USA is used in the present study. An EPR study on powdered sample confirms the presence of Mn(II) and Cu(II). Optical absorption spectral results are due to Cu(II) which is in distorted octahedron. NIR results are indicating the presence of water fundamentals.

The strength and failure of silica optical fibers

The mechanical strength and failure behavior of conventional and microstructured silica optical fibers was investigated using a tensile test and fracture mechanics and numerical analyses. The effect of polymer coating on failure behavior was also studied. The results indicate that all these fibers fail in a brittle manner and failure normally starts from fiber surfaces. The failure loads observed in coated fibers are higher than those in bare fibers. The introduction of air holes reduces fiber strength and their geometrical arrangements have a remarkable effect on stress distribution in the longitudinal direction. These results are potentially useful for the design, fabrication and evaluation of optical fibers for a wide range of applications.

Virtual force feedback teleoperation of the InsectBot using optical flow

This paper proposes the use of optical flow from a moving robot to provide force feedback to an operator’s joystick to facilitate collision free teleoperation. Optical flow is measured by a pair of wide angle cameras on board the vehicle and used to generate a virtual environmental force that is reflected to the user through the joystick, as well as feeding back into the control of the vehicle. We show that the proposed control is dissipative and prevents the vehicle colliding with the environment as well as providing the operator with a natural feel for the remote environment. Experimental results are provided on the InsectBot holonomic vehicle platform.

A new framework for force feedback teleoperation of robotic vehicles based on optical flow

This paper proposes the use of optical flow from a moving robot to provide force feedback to an operator's joystick to facilitate collision free teleoperation. Optic flow is measured by wide angle cameras on board the vehicle and used to generate a virtual environmental force that is reflected to the user through the joystick, as well as feeding back into the control of the vehicle. The coupling between optical flow (velocity) and force is modelled as an impedance - in this case an optical impedance. We show that the proposed control is dissipative and prevents the vehicle colliding with the environment as well as providing the operator with a natural feel for the remote environment. The paper focuses on applications to aerial robotics vehicles, however, the ideas apply directly to other force actuated vehicles such as submersibles or space vehicles, and the authors believe the approach has potential for control of terrestrial vehicles and even teleoperation of manipulators. Experimental results are provided for a simulated aerial robot in a virtual environment controlled by a haptic joystick.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.