We present an approach to identify the plant species from the contour information from occluded leaf image using a database of full plant leaves. Although contour based 2D shape matching has been studied extensively in the last couple of decades, matching occluded leaves with full leaf databases is an open and little worked on problem. Classifying occluded plant leaves is even more challenging than full leaf matching because of large variations and complexity of leaf structures. Matching an occluded contour with all the full contours in a database is an NP-hard problem, so our algorithm is necessarily suboptimal. First, we represent the 2D contour points as a β-Spline curve. Then, we extract interest points on these curves via the Discrete Contour Evolution (DCE) algorithm. We use subgraph matching using the DCE points as graph nodes, which produces a number of open curves for each closed leaf contour. Next, we compute the similarity transformation parameters (translation, rotation, and uniform scaling) for each open curve. We then "overlay" each open curve with the inverse similarity transformed occluded curve and use the Fréchet distance metric to measure the quality of the match, retaining the best η matched curves. Since the Fréchet metric is cheap to compute but not perfectly correlated with the quality of the match, we formulate an energy functional that is well correlated with the quality of the match, but is considerably more expensive to compute. The functional uses local and global curvature, Shape Context descriptors, and String Cut features. We minimize this energy functional using a convex-concave relaxation framework. The curve among these best η curves, that has the minimum energy, is considered to be the best overall match with the occluded leaf. Experiments on three publicly available leaf image database shows that our method is both effective and efficient, outperforming other current state-of-the-art methods. Occlusion is measured as the percentage of the overall contour (and not leaf area) that is missing. We show that our algorithm can, even for leaves with a high amounts of occlusion (say 50 percent occlusion), still identify the best full leaf match from the databases.

Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer, and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana (L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.

Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.

We present a formal framework for robotic cooperation in which we use an extension to Petri nets, known as workflow nets, to establish a protocol among mobile agents based on the task coverage they maintain. Our choice is motivated by the fact that Petri nets handle concurrency and that goal reachability, or soundness, can be theoretically established. In particular, we define a mathematical cooperation operator which turns cooperation problems expressed as workflow nets into algebraic representations. While we do not address the problem of efficiency, we formally demonstrate that this framework guarantees soundness, or goal reachability, using workflow nets.

Preserving historic recording on gramophone records is an important task because the traditional record play back system damages/wears out records eventually. We present an optical flow based method to reproduce the sound signal from gramophone records using 3 D robust scene reconstruction of the surface orientation of the walls of the grooves. The imaging setup was modified to overcome a shallow depth of field by using a thin glass plate to obtain additional in-focus parts of the image at a second focal length. The sound signal was recovered from the surface orientation and processed further using the industry standard RIAA filter. The overall algorithm has been tested and found to be working correctly using both undamaged and damaged SP records. The algorithm is a "proof of concept" in that it shows sound can be recovered from time-varying 3 D orientation of groove walls.

We propose and evaluate a method to identify tornadoes automatically in Doppler radar imagery by detecting hook echoes, which are important signatures of tornadoes, in Doppler radar precipitation density data. Our method uses a skeleton to represent 2D storm shapes. To characterize hook echoes, we propose four shape features of skeletons: curvature, curve orientation, thickness variation, boundary proximity, and two shape properties of tornadoes: southwest localization and the ratio of storm size to model hook echo size. To evaluate the hook echo detection algorithm, the hook echoes detected in several radar datasets by the algorithm are compared to those proposed by an expert. The effectiveness of the algorithm is quantified using a critical success index (CSI) analysis.

The availability of sequences of 3D Doppler radial velocity datasets provides sufficient information to estimate the 3D velocity of Doppler storms. We present a regularization framework for computing the 3D velocity field of storms from the underlying 3D radial velocities via an intermediate least squares computation. We obtain very realistic Doppler velocities, which can be used to estimate and predict the motion of Doppler storms. Such information is fundamental in the tracking of Doppler storms over time.

The combined use of intensity and depth information greatly helps in the estimation of the local 3D movements (range flow) of moving surfaces. We demonstrate how the two can be combined in both: a local total least squares algorithm, and an iterative global variational technique. While the former assumes locally constant flow, the latter relies on a smoothly varying flow field. The improvement achieved through incorporating intensity is illustrated qualitatively and quantitatively on synthetic and real test data.

We present a theoretical investigation of the frequency structure of 1D occluding image signals. We show that image signal occlusion contains relevant information which is most easily extractable from its representation in the frequency domain. For instance, the occluding and occluded signal velocities may be identified as such and translucency phenomena may be understood in the terms of this theoretical investigation. In addition, it is found that the structure of occluding 1D signals is invariant under constant and linear models of signal velocity. This theoretical framework can be used to describe the exact frequency structure of non-Fourier motion and bridges the gap between such visual phenomena and their understanding in the frequency domain.

We describe an automatic storm tracking system to help with the forecasting of severe storms. The concepts fuzzy point, fuzzy vector, fuzzy length of a fuzzy vector, and the fuzzy angle between two non-zero fuzzy vectors are first examined. We use a region splitting algorithm with dynamic thresholding to determine storm masses in Doppler radar intensity images. We represent the center of an hypothesized storm using a fuzzy point. These fuzzy storm centers are tracked over time using an incremental relaxation algorithm. The algorithms are tested on actual radar images obtained from the Atmospheric Environment Service radar station at King City, Ontario, Canada. The algorithms are capable of producing storm tracks which closely match human perception.

We use the left and right monocular motion and structure parameters of two stereo image sequences (direction of translation, relative depth, observer rotation and rotational acceleration) to compute the absolute depth, absolute translation and absolute translational acceleration for each pair of left and right images. Individual translation parameters computed at each frame are integrated over time using a Kalman filter to provide more accuracy and a "best" estimate of the absolute translation at each time.

An automated tracking algorithm for Doppler radar storms is presented. Potential storms in Doppler radar images are hypothesized as regions of high water density (high intensity in the radar images) using a merge-and-split region growing algorithm. Potential storms are verified by a relaxation labelling scheme that attempts to finds the best tracks based on spatio-temporal storm consistency. Temporal consistency is ensured by requiring temporal coherence of storm properties, which include size, average intensity, radial velocity variance (computed from the Doppler radial velocity images), storm shape and orientation and neighbourhooding storm disparity. Spatial consistency requires neighbouring storm tracks with common storms to compete with a winner-take-all strategy. The property coherence framework is adaptive, allowing additional properties to be added or deleted as appropriate. The tracking algorithm allows storm merging and splitting via a construction called pseudo-storms. Several tracks for Doppler storm radar data supplied by AES are given as examples of the algorithms performance.>

The performance of six optical flow techniques is compared, emphasizing measurement accuracy. The most accurate methods are found to be the local differential approaches, where nu is computed explicitly in terms of a locally constant or linear model. Techniques using global smoothness constraints appear to produce visually attractive flow fields, but in general seem to be accurate enough for qualitative use only and insufficient as precursors to the computations of egomotion and 3D structures. It is found that some form of confidence measure/threshold is crucial for all techniques in order to separate the inaccurate from the accurate. Drawbacks of the six techniques are discussed.>

An automated edge detection algorithm, referred to as spatio-temporal edge focusing, is presented. It combines spatial edge focusing and temporal edge focusing. While spatial edge focusing and temporal edge focusing each have their own advantages and disadvantages, it is shown that the combination of these two techniques maintains their advantages while minimizing or removing most of their disadvantages. The final result is an automated edge detection algorithm that produces relatively noise-free edge maps with well-localized edges.>

We address the problem of interpreting image velocity fields generated by a moving monocular observer viewing a stationary environment under perspective projection to obtain 3-D information about the relative motion of the observer (egomotion) and the relative depth of environmental surface points (environmental layout). The algorithm presented in this paper involves computing motion and structure from a spatio-temporal distribution of image velocities that are hypothesized to belong to the same 3-D planar surface. However, the main result of this paper is not just another motion and structure algorithm that exhibits some novel features but rather an extensive error analysis of the algorithm’s preformance for various types of noise in the image velocities. Waxman and Ullman [83] have devised an algorithm for computing motion and structure using image velocity and its 1st and 2d order spatial derivatives at one image point. We generalize this result to include derivative information in time as well. Further, we show the approximate equivalence of reconstruction algorithms that use only image velocities and those that use one image velocity and its 1st and/or 2”d spatio-temporal derivatives at one image point. The main question addressed in this paper is: “How accurate do the input image velocities have to be?’ or equivalently, “How accurate does the input image velocity and its I~ and 2& order derivatives have to be?“. The answer to this question involves worst case error analysis. We end the paper by drawing some conclusions about the feasibility of motion and structure calculations in general. I.1 Introduction In this paper, we present a algorithm for computing the motion and strncture parameters that describe egomotion and environmental layout from image velocity fields generated by a moving monocular observer viewing a stationary environment. Egomotion is defined as the motion of the observer relative to his environment and can be described by 6 parameters; 3 dvth-scaled translational parameters, Z and 3 rotation parameters, o. Environmental layout refers to the 3-D shape and location of objects in the environment. For monocular image sequences, en$ronmental layout is described by the normalized surface gradient, a, at each image point. To determine these motion and structure parameters we derive nonlinear equations relating image velocity at some image int ?(?*,t ‘) to the underlying motion and structure parameters at (P,c). The computaP tion of egomotion and environmental layout from image velocity is sometimes called the reconstruction problem; we reconstruct the observer’s motion, and the layout of his environment, from (timevarying) image velocity. A lot of research has been devoted to

This paper describes the Computerized Gate Array Layout system (CGAL), a general purpose tool developed at Sperry Univac to provide efficient and reliable automated layouts for a variety of master slice arrays. The system contains algorithms for the construction of initial placement, placement improvement, global routing, channel routing and planar routing. The system also provides for user interaction through a design language and graphical output.