Matching Point Sets with Quantum Circuit Learning

Affine registration has a long and venerable history in computer vision literature, and in particular, extensive work has been done for affine registration in R(2) and R(3). This paper studies affine registration in R(m) with m typically ranging from 4 to 12. To justify breaking of this dimension barrier, the first part of the paper describes three novel matching problems that can be formulated and solved as affine point-set registration problems in dimensions greater than three: stereo correspondence under motion, image set matching, and covariant point-set matching, problems that are not only interesting in their own right but also have potential for important vision applications. Unfortunately, most of the existing affine registration algorithms do not generalize easily to higher dimensions due to their inefficiency. Therefore, the second part of this paper develops a novel algorithm for estimating the affine transform between two point sets in R(m). Specifically, the algorithm follows the common approach of iteratively solving the correspondences and transform. The initial correspondences are determined using the novel notion of local spectral features, features constructed from local distance matrices. Unlike many correspondence-based methods, the proposed algorithm is capable of registering point sets of different size, and the use of local features provides some degree of robustness against noise and outliers. The proposed algorithm is validated on a variety of synthetic point sets in different dimensions with varying degrees of deformation and noise, and the paper also shows experimentally that several instances of the aforementioned three matching problems can indeed be solved satisfactorily using the proposed affine registration algorithm.

A P-STABLE EIGHTEENTH-ORDER SIX-STEP METHOD FOR PERIODIC INITIAL VALUE PROBLEMS

Fingerprint matching is the most important part in the field of fingerprint recognition. In this paper, a novel fingerprint matching algorithm based on the probabilistic graphical model and 3-tree model is proposed. First, minutiae matching problems are considered as a special point-set matching. Fingerprint minutiae are viewed as random variables. Each minutia pairs have some probability to be matched. Second, an algorithm is proposed to generate the graphical model and choose "signal points", which dynamically have corresponding points in other point set. We choose three base minutiae pairs as signal pairs. Third, the model is converted into a Junction Tree. A 3-tree model is built and the potentials of other minutiae pairs are calculated through Junction Tree (J.T.) algorithm. Then we translate the matching problem into the best matching problem of a weighted bipartite graph. Finally, the number of common matching pairs can be got through maximum flow algorithm. The similarity of two fingerprints is evaluated using the number of common matching pairs and the maximal posteriori probability. In order to deal with part-matching problems, we use the smallest convex hull which contains all the matched minutiae. Experiments evaluated on FVC 2004 show both effectiveness and efficiency of our methods.

In this study, we present a new calibration method for the cooperation of wide-angle and pan-tilt-zoom (PTZ) cameras. A wide-angle camera enables the surveillance of a large area. However, it may be difficult to perceive the detail of an object. On the other hand, a PTZ camera enables the surveillance of the object detail. However, its view is limited when the camera zooms on a specific object. Therefore, the cooperation can complement the disadvantage of two types of cameras. For an object appears in the image of wide-angle camera, the desired pan and tilt angles of a PTZ camera for focusing on the object can be computed from its image coordinate. Previous methods can be classified into three categories: 3D world coordinate system, scene-dependent, or special technique. They are not simple and instinctive enough. The proposed method is simply based on the 2D image plane of wide-angle camera. A set of calibration points are selected directly on the image. Every point is a pair of pan/tilt angles (p, t) and 2D coordinate (x, y). Then, the calibration parameters are calculated from the set of points. An experiment was designed to evaluate the accuracy of the proposed method at fixed points. The results show that the proposed method is accurate enough for the cooperation of dual cameras. In advance, a prototype system for capturing close-up images of moving objects in an open area was implemented to demonstrate the feasibility of the proposed method.

This paper presents a theoretically simple, yet efficient approach for the problem of matching the 2-D point sets under rigid motion, where jitter is allowed. It is a fundamental problem in pattern recognition, having applications ranging from robotics to astronautics. Commonly, the previous methods tend to use symbolic arithmetic to solve this problem. However, they are always complex and limited by computational cost. In this paper, we present a visual analysis for the point set matching problem and develop a geometrical arithmetic which is proven to yield equivalent results to the symbolic arithmetic. This allows us to view the problem as one of shape matching and obtain an algorithm that firstly generates a probabilistic shape descriptor (PSD) to describe the local geometric property of each point in the point sets, then acquires the local correspondence from the Euclidean distance matrix of the descriptors, and validates the global transformation finally. Experimental results demonstrate that our simple algorithm with a lower computation complexity effectively improves accuracy over current method, particularly when matching equal-size patterns under varying jitter.

In this study, a visual similarity metric based on precision---recall graphs is presented as an alternative to the widely used Hausdorff distance (HD). Such metric, called maximum cardinality similarity metric, is computed between a reference shape and a test template, each one represented by a set of edge points. We address this problem using a bipartite graph representation of the relationship between the sets. The matching problem is solved using the Hopcroft---Karp algorithm, taking advantage of its low computational complexity. We present a comparison between our results and those obtained from applying the partial Hausdorff distance (PHD) to the same test sets. Similar results were found using both approaches for standard template-matching applications. Nevertheless, the proposed methodology is more accurate at determining the completeness of partial shapes under noise conditions. Furthermore, the processing time required by our methodology is lower than that required to compute the PHD, for a large set of points.

In the approximate Euclidean min-weighted perfect matching problem, a set of points in the plane and a real number are given. Usually, a solution of this problem is a partition of points of into pairs such that the sum of the distances between the paired points is at most times the optimal solution. In this paper, the authors give a randomized algorithm which follows a Monte-Carlo method. This algorithm is a randomized fully polynomial-time approximation scheme for the given problem. Fortunately, the suggested algorithm is a one tackled the matching problem in both Euclidean nonbipartite and bipartite cases. The presented algorithm outlines as follows: With repeating times, we choose a point from to build the suitable pair satisfying the suggested condition on the distance. If this condition is achieved, then remove the points of the constructed pair from and put this pair in (the output set of the solution). Then, choose a point and the nearest point of it from the remaining points in to construct a pair and put it in . Remove the two points of the constructed pair from and repeat this process until becomes an empty set. Obviously, this method is very simple. Furthermore, our algorithm can be applied without any modification on complete weighted graphs and complete weighted bipartite graphs , where and m is an even.

Under manufacturing process variation, a path through a fault site is called longest for delay test if there exists a process condition under which the path has the maximum delay among all paths through that fault site. There are often multiple longest paths for each fault site in the circuit, due to different process conditions. To detect the smallest delay fault, it is necessary to test all longest paths through the fault site. However, previous methods are either inefficient or their results include too many paths that are not longest. We present an efficient method to generate the longest path set for delay test under process variation. To capture both structural and systematic process correlation, we use linear delay functions to express path delays under process variation. A novel path-pruning technique is proposed to discard paths that are not longest, resulting in a significantly reduction in the number of paths compared with the previous best method. The new method can be applied to any process variation as long as its impact on delay is linear.

Cooperative driving behavior is essential for driv- ing in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also man- age these situations by behaving cooperatively and naturally. In this paper, we enhance our previous learning-based method to efficiently estimate other vehicles’ intentions and interact with them in ramp merging scenarios, without over-the-air commu- nication between vehicles. The proposed approach inherits our previous Probabilistic grahpical Model (PGM) and distance- keeping framework. Real driving trajectories are used to learn transition models in the PGM. Thus, besides the structure of the PGM, our method does not require human-designed reward or cost functions. The PGM-based intention estimation is followed by an off-the-shelf distance-keeping model to generate proper acceleration/deceleration controls. The PGM plays a plug-in role in our self-driving framework. The new model eliminates two assumptions in the previous model: 1) a fixed merging point for all merging agents, which is hard to determine before the merging vehicles make the merge; 2) Perfect velocity mea- surement, which requires sophisticated perception systems. We validate the performance of our method both on real merging data and using a designed merging strategy in simulation, and show significant improvements compared with previous methods. Parameter design is also discussed by experiments. The new method is computationally efficient, and exhibits better robustness against sensing uncertainty.

3D measurement system based on divergent multi-line structured light projection, its accuracy analysis

As part of a study aimed at gaining a clearer understanding of the pathogenesis of Naegleria fowleri, we have developed a simple and reliable technique for cloning trophozoites. Although cloning of N. fowleri has been reported using limiting dilution (Jonckheere, 1979, Path. Biol. 27: 453-458) and by micropipette technique (Wong et al., 1977, J. Parasit. 63: 872-878), the degree of success is difficult to assess since no data were provided on plating efficiency for either method. We have modified a previously described procedure for cloning Naegleria gruberi (Fulton, 1970, Meth. Cell Physiol. 4: 341-475) for use with N. fowleri which has proved to be highly reproducible and provides excellent efficiency of plating. Naegleria fowleri Lee (ATCC-30894) is routinely maintained in Nelson medium containing 2% calf serum as previously described (Weik and John, 1977, J. Parasit. 63: 868-871). Amebae used in cloning experiments were grown in tissue culture flasks (75 cm2, Falcon Plastics, Oxnard, California) containing 50 ml of Nelson medium supplemented with 2% calf serum. Flasks are inoculated with 104 amebae/ml and allowed to incubate for 72 hr at 37 C. The amebae were dislodged by placing the flasks on ice for 10 min and the resulting suspension of cells was vortexed to dissociate any remaining clumps of organisms. After quantitation by hemacytometer count and in some experiments by Coulter counter (Weik and John, 1977, J. Parasit. 63:150-151) the amebae were diluted to a final concentration of 1030 amebae/ml in Nelson medium without calf serum. The Escherichia coli (hospital isolate) used in cloning N. fowleri were first grown for 3 hr at 37 C with agitation in 50 ml of Nutrient Broth (Difco). The bacteria were diluted 1/10, 1/ 100, and 1/1,000 in soft nutrient agar (Difco, 0.5% agar w/v) and maintained at 55 C in a heated waterbath. The dilution was made in the following manner: 9.0 ml of bacteria in soft nutrient agar are placed in the heated waterbath and then 1.0 ml of Nelson medium containing the diluted amebae was added. The tube is then immediately vortexed and poured onto petri plates containing a non-nutrient agar base (1.5% agar, 10 ml per plate). Control plates containing no amebae were included with each experiment to assure that the observed plaques were not due to bacteriophage. The soft agar plates were allowed to harden at room temperature for 5 hr and then placed upright in a 37 C incubator stacked in sets of 3. After 24 hr incubation, parafilm was placed around the petri plates to prevent drying of the medium. The results from a representative cloning exp riment are shown in Table I. Plaques containing amebae are regularly observed only after 5 to 6 days of incubation with the presence of amebae being confirmed by phase-contrast microscopy. It is especially important to inspect plates carefully after this period since plaques will merge or disappear within the succeeding 24-48 hr. Amebae in the plaque can be observed growing in a concentric manner, a characteristic feature of Naegleria that has been previously described (Carter, 1970, J. Pathol. 100: 217-244). For amplification of clones, amebal plaques are aseptically cut out of the agar plates and placed in Falcon flasks containing Nelson medium supplemented with 2% calf serum and 100 ,tg/ml streptomycin. Two of the amebal clones tested so far have maintained their infectivity for mice. A notable feature of these experiments is the high efficiency of plating (100%) obtained when 3,000 bacteria/ml are initially diluted into the overlay whereas no plaques are observed when 300 or 30 bacteria/ml are used. We have had success using bacterial concentrations between 3,000 and 9,000 bacteria/ml in the initial overlay. The cloning method described in this report differs from previous methods in the use of the soft agar overlay. The observed high efficiency of plating may be due in part to the more effective separation of the individual amebae during the plating procedure. A previous cloning method for N. gruberi employed a glass spreading bar to

Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm.

Many computer vision applications can be formulated as labeling problems. However, multilabeling problems are usually very challenging to solve, especially when some ordering constraints are enforced. We solve in this paper a five-parts labeling problem proposed in [6, 7]. In this model, one wants to find an optimal labeling for an image with five possible parts: “left”, “right”, “top”, “bottom” and “center”. The geometric ordering constraints can be read naturally from the names. No previous method can solve the problem with globally optimal solutions in a linear space complexity. We propose an efficient dynamic programming based algorithm which guarantees the global optimal labeling for the five-parts model. The time complexity is O(N1.5) and the space complexity is O(N), with N being the number of pixels in the image. In practice, it runs faster than previous methods. Moreover, it works for both 4-neighborhood and 8-neighborhood settings, and can be easily parallelized for GPU.

Forest biomass-to-biofuel factory location problem with multiple objectives considering environmental uncertainties and social enterprises