Correspondence Problem Research Articles

On injectivity of quantum finite automata

We consider notions of freeness and ambiguity for the acceptance probability of Moore-Crutchfield Measure Once Quantum Finite Automata (MO-QFA). We study the injectivity problem of determining if the acceptance probability function of a MO-QFA is injective over all input words, i.e., giving a distinct probability for each input word. We show that the injectivity problem is undecidable for 8 state MO-QFA, even when all unitary matrices and the projection matrix are rational and the initial state vector is real algebraic. We also show undecidability of this problem when the initial vector is rational, although with a huge increase in the number of states. We utilize properties of quaternions, free rotation groups, representations of tuples of rationals as linear sums of radicals and a reduction of the mixed modification of Post's correspondence problem, as well as a new result on rational polynomial packing functions which may be of independent interest.

Freeness Problem for Matrix Semigroups of Parikh Matrices

Since the undecidability of the mortality problem for 3 × 3 matrices over integers was proved using the Post Correspondence Problem, various studies on decision problems of matrix semigroups have emerged. The freeness problem in particular has received much attention but decidability remains open even for 2 × 2 upper triangular matrices over nonnegative integers. Parikh matrices are upper triangular matrices introduced as a generalization of Parikh vectors and have become useful tools in studying of subword occurrences. In this work, we focus on semigroups of Parikh matrices and study the freeness problem in this context.

The Construction Method of the Digital Operation Environment for Bridge Cranes

To realize real-time detection of the operating environment of bridge cranes, this paper presents a three-dimensional mapping method using a binocular camera and laser ranging sensor. First, the left and right images are obtained by using the binocular camera, and the block matching is used for stereo correspondence to obtain the disparity image. Then, according to the reprojection matrix and the disparity image, the depth value of each pixel is obtained, and the depth image can be transformed into a three-dimensional point cloud of the current frame. Relying on the camera position data obtained by the laser ranging sensor, the three-dimensional point cloud generated from different positions is registered to obtain the three-dimensional point cloud of the craneʼs global operating environment. Finally, an octree map is used to represent the operating environment. To simulate the construction process of the operating environment of the bridge crane, an experimental platform was built, and experiments were carried out using the method proposed in this paper. The experimental results verified the feasibility of the method proposed in this paper.

Elevation Angle Estimation in 2D Acoustic Images Using Pseudo Front View

A novel method to estimate the missing dimension in 2D acoustic images for 3D reconstruction is proposed in this paper. Acoustic cameras can acquire high resolution 2D images in underwater environment insusceptible to water turbidity and light condition. However, the formulation of acoustic images leads to the missing dimension problem. Estimating the unknown elevation angle dimension is a difficult task which has recently drawn the attention of researchers. The non-bijective characteristic between 3D points and 2D pixels increases the complexity of the problem. In this paper, a novel elevation angle estimation method is proposed. The method transfers the acoustic view to pseudo front view using a deep neural network. The proposed network can estimate the missing dimension and resolve the non-bijection problem of the 2D-3D correspondence. Because of the difficulty of acquiring depth information in underwater environments, the network is trained using simulated images. To mitigate the sim-real gap, a neural style transfer method is implemented to generate a realistic image dataset for training. Simulation experiments were carried out for evaluation and real data proved the feasibility of the proposed method.

The role of kinematic properties in multiple object tracking.

People commonly track objects moving in complex natural displays and their performance in the multiple object tracking paradigm has been used to study such visual attention for more than three decades. Given the theoretical and practical importance of object tracking, it is critical to understand how people solve the correspondence problem to track objects; however, it remains unclear what information people use to achieve this feat. In particular, although people can track multiple moving objects based on their positions, there is ambiguity about whether people can track objects via higher order kinematic information, such as velocity. We designed a paradigm in which position was rendered uninformative to directly examine whether people could use higher order kinematic information to track multiple objects. We find that people can track via velocity, but not acceleration, even though observers can reliably detect the acceleration cue that they cannot use for tracking. Furthermore, we show a capacity constraint on using higher order kinematic information—people perform worse when required to use velocity to resolve correspondence for multiple object pairs simultaneously. Together, our results suggest that, although people can use higher order kinematic information for object tracking, precise higher order kinematic information is not freely available from the early visual system.

Regularized multi-structural shape modeling of the knee complex based on deep functional maps.

The incorporation of a-priori knowledge on the shape of anatomical structures and their variation through Statistical Shape Models (SSMs) has shown to be very effective in guiding highly uncertain image segmentation problems. In this paper, we construct multiple-structure SSMs of purely geometric nature, that describe the relationship between adjacent anatomical components through Canonical Correlation Analysis. Shape inference is then conducted based on a regularization term on the shape likelihood providing more reliable structure representations. A fundamental prerequisite for performing statistical shape analysis on a set of objects is the identification of corresponding points on their associated surfaces. We address the correspondence problem using the recently proposed Functional Maps framework, which is a generalization of point-to-point correspondence to manifolds. Additionally, we show that, by incorporating techniques from the deep learning theory into this framework, we can further enhance the ability of SSMs to better capture the shape variation in a given dataset. The efficiency of our approach is illustrated through the creation of 3D models of the human knee complex in two application scenarios: incomplete or noisy shape reconstruction and missing structure estimation.

Latent Distribution-Based 3D Hand Pose Estimation From Monocular RGB Images

In this article, we propose a novel compressed latent distribution representation for 3D hand pose estimation from monocular RGB images to alleviate the channel correspondence problem. The channel correspondence problem occurs when the 2D and depth coordinates are estimated from independent feature maps, which means the 2D and depth channel sequences may not match during the cross-dataset inference. In contrast, we propose a compressed latent distribution representation that the 2D and depth feature maps for each joint are interconnected and inter-constrained more directly, effectively alleviating the channel correspondence problem and improving cross-dataset performance. Moreover, we design an efficient encoder-decoder network that can maintain the resolution of feature maps to enable better hand feature extraction from monocular RGB images. In this work, the overall pipeline contains two branches: one is the 2D hand pose estimation branch based on a latent heatmap representation (LHR); the other is the 3D hand pose estimation branch based on our proposed latent distribution representation (LDR). In this way, the 2D estimation branch serves as guidance for the 3D branch, which simplifies the optimization of the overall network and results in a more rapid convergence during training. The results on several benchmark datasets (including STB, RHD, and the most recently released InterHand2.6M) demonstrate that our proposed method achieves state-of-the-art (SOTA) performance.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses

Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.

Robust and ultrafast fiducial marker correspondence in electron tomography by a two-stage algorithm considering local constraints.

MotivationElectron tomography (ET) has become an indispensable tool for structural biology studies. In ET, the tilt series alignment and the projection parameter calibration are the key steps toward high-resolution ultrastructure analysis. Usually, fiducial markers are embedded in the sample to aid the alignment. Despite the advances in developing algorithms to find correspondence of fiducial markers from different tilted micrographs, the error rate of the existing methods is still high such that manual correction has to be conducted. In addition, existing algorithms do not work well when the number of fiducial markers is high.ResultsIn this article, we try to completely solve the fiducial marker correspondence problem. We propose to divide the workflow of fiducial marker correspondence into two stages: (i) initial transformation determination, and (ii) local correspondence refinement. In the first stage, we model the transform estimation as a correspondence pair inquiry and verification problem. The local geometric constraints and invariant features are used to reduce the complexity of the problem. In the second stage, we encode the geometric distribution of the fiducial markers by a weighted Gaussian mixture model and introduce drift parameters to correct the effects of beam-induced motion and sample deformation. Comprehensive experiments on real-world datasets demonstrate the robustness, efficiency and effectiveness of the proposed algorithm. Especially, the proposed two-stage algorithm is able to produce an accurate tracking within an average of ms per image, even for micrographs with hundreds of fiducial markers, which makes the real-time ET data processing possible.Availability and implementationThe code is available at https://github.com/icthrm/auto-tilt-pair. Additionally, the detailed original figures demonstrated in the experiments can be accessed at https://rb.gy/6adtk4.Supplementary information Supplementary data are available at Bioinformatics online.

Dense Depth Estimation from Stereo Endoscopy Videos Using Unsupervised Optical Flow Methods.

In the context of Minimally Invasive Surgery, estimating depth from stereo endoscopy plays a crucial role in three-dimensional (3D) reconstruction, surgical navigation, and augmentation reality (AR) visualization. However, the challenges associated with this task are three-fold: 1) feature-less surface representations, often polluted by artifacts, pose difficulty in identifying correspondence; 2) ground truth depth is difficult to estimate; and 3) an endoscopy image acquisition accompanied by accurately calibrated camera parameters is rare, as the camera is often adjusted during an intervention. To address these difficulties, we propose an unsupervised depth estimation framework (END-flow) based on an unsupervised optical flow network trained on un-rectified binocular videos without calibrated camera parameters. The proposed END-flow architecture is compared with traditional stereo matching, self-supervised depth estimation, unsupervised optical flow, and supervised methods implemented on the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) Challenge dataset. Experimental results show that our method outperforms several state-of-the-art techniques and achieves a close performance to that of supervised methods.

Professional psychological selection of military personnel: structure and main characteristics

The article examines the essential characteristics of professional psychological selection of military personnel through the analysis of professional selection and its structure. The problem of effective implementation of professional psychological selection is considered by the authors as a problem of mutual correspondence of the individual and the profession. It is noted that it is solvable only if a certain set of requirements is met: both the individual's requests for a certain type of professional activity, and the requirements of the activity itself for the subjective characteristics of the «person of labor». Professional selection of military personnel structurally includes: social selection; medical selection; professional psychological selection; educational and qualification selection; assessment of the level of functional development and general physical fitness. The scientific results of the study include the fact that the general characteristics of the candidate and the military specialist performing professional psychological selection, the conditions and means of its implementation are disclosed.

St. Gregory Palamas’s gnoseology in light of the dogma of the incarnation of Christ

This article is devoted to the problem of correspondence between the gnoseology of St. Gregory Palamas and the teachings of the Orthodox Church. His gnoseology contains two theses that caused a significant controversy in the 14th century in the Byzantine Empire. These are the uncreated nature of the Tabor Light and the possibility to know God by His uncreated energies, but not by His essence. The author turns directly to the Christological confessions and other dogmatic texts of the Ecumenical Councils to solve the problem. This method has not been largely used. As a general rule, the “palamites” and “antipalamites” used to refer to some particular father or plunged into a strictly philosophical discourse. At the same time, it was not fully taken into the account the fact that the Acts of the Ecumenical Councils contain information to adequately assess St. Gregory’s gnoseology. The article concisely presents the main theses of St. Gregory Palamas’s gnoseology, approved by the local Council of 1351 held in Constantinople. The theses are compared to the confession of the Council of Chalcedon. The texts of the subsequent Ecumenical Councils are considered to be as clarifying as the Chalcedonian confession. The views of St. Gregory’s main opponents are also analyzed in the article. Particular attention is paid to the meaning of key terms in the considered dogmatic texts. The article also takes into account the philosophical aspect of the problem, and expounds one of the arguments of St. Gregory in favor of the uncreated nature of the Tabor Light, which is based on the idea of the inability of human nature to emit light. As a result of the research, it was established that both theses of St. Gregory contradict the doctrine of the Ecumenical Councils about Christ. Instead of St. Gregory’s dubious gnoseology, the Ecumenical Councils offer to cognize the divinity of the Trinity inseparably from the flesh of Christ, and not only in energy, but also essence and hypostasis. As for the natural science argument of St. Gregory, it is refuted by the data of modern science, which proved the existence of biophotons. This discovery, according to the author, does not contradict, but only confirms the Christology of the Ecumenical Councils.

Robust Markerless Registration of Point Clouds for Terrestrial Laser Scanning-Based Measurement of Bulk Grains Stockpiled in Storehouses

HighlightsFully automated registration free from artificial markers for multi-scan point clouds aimed for TLS-based measurement of bulk grains in large storehouses.The geometric structure of the large grain storehouse is explored to derive geometrical features as the structurally semantic information for scene understanding.The geometrical features are modeled as a small ordered set and correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans.Significant improvements have been achieved in registration accuracy, computational efficiency, and robustness against scenes with symmetric structures as well as the immunity to noises and varying point density.Abstract. Point clouds collected by terrestrial laser scanning (TLS) in the application of bulk grain measurement and quantification contain a vast amount of data, relatively low-textured surfaces and highly symmetric structures. All of these challenges make it a difficult task to automatically register multiple scans from different viewpoints needed to fully cover a large-scale scene. To address the challenges, this article presents a robust automatic marker-free registration method dedicated for multi-scan TLS point cloud data captured in large grain storehouses. The framework of the dedicated method follows the common procedure to split the entire registration into coarse alignment and fine registration, and uses the iterative closest point (ICP) algorithm for the latter. The main contribution of the proposed dedicated method is an efficient way to find a global coarse alignment that is robust across individual scans in a TLS-based bulk grain measurement project. To tackle the correspondence problem, which is at the core of a registration task, the geometric information inherent in grain storehouses is explored in the stage of global coarse alignment. The derived semantic feature points are modeled as a small ordered set and reliable correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans. Experimental results show the dedicated method outperforms the existing generic markless registration approaches in terms of accuracy, robustness and computational efficiency. With robustness, efficiency and accuracy, the proposed markless point cloud registration method dedicated for bulk grain measurement can cover a gap between the TLS technology and various granary field applications. Especially, its applicability to the dominant storage structure in Chinese huge grain reserve system implies remarkable efficiency improvements and will facilitate the application of TLS-based measurement in the national grain inventory of China. Keywords: Bulk grain measurement, Feature extraction, Grain storehouse, Markerless registration, Point cloud, Terrestrial laser scanning.

On convergence of one class of corresponding two-dimensional branched continued fractions

The infinite branched continued fraction, associated with the correspondence problem between a formal double power series and a sequence of the rational approximations of a function of two variables, is considered. Using formulas for real and imaginary parts of tails of figured approximants and a multidimensional analogue of the Stieltjes–Vitali theorem, the figured uniform convergence of such a fraction in some domain is investigated and the estimation of the rate of its convergence is obtained. Cite as: T. M. Antonova, S. M. Vozna, “On convergence of one class of corresponding two-dimensional branched continued fractions,” Prykl. Probl. Mekh. Mat. , Issue 18, 25–33 (2020) (in Ukrainian), https://doi.org/10.15407/apmm2020.18.25-33

Self-Guided Stereo Correspondence Estimation Algorithm

This paper introduces an innovative algorithm, “Self-guided Stereo Correspondence” (SGSC), that is directed by photometric properties of the candidate pixels. As the photometric properties of reference image (left image) pixel and its neighbor’s pixel are similar in most cases, so the upcoming corresponding pixel exists in the surrounding of the previous matching pixel. Searching performance is greatly improved by utilizing this photometric property of the candidate pixels. The searching performance is further improved by applying the pioneering threshold technique. These two key techniques sufficiently reduced the computational cost with no degradation of accuracy. The achievements of the proposed method are testified on Middlebury standard Stereo Datasets of 2003 and 2006 and the Middlebury latest Optical Flow Dataset. Finally, the proposed method is compared with present state-of-the-art methods and our SGSC outdoes the latest methods in terms of speed, visualization of hidden ground truth, 3D reconstruction and accuracy.

Beyond Uniform Equivalence between Answer-set Programs

This article deals with advanced notions of equivalence between nonmonotonic logic programs under the answer-set semantics, a topic of considerable interest, because such notions form the basis for program verification and are useful for program optimisation, debugging, and modular programming. In fact, there is extensive research in answer-set programming (ASP) dealing with different notions of equivalence between programs. Prominent among these notions is uniform equivalence , which checks whether two programs have the same semantics when joined with an arbitrary set of facts. In this article, we study a family of more fine-grained versions of uniform equivalence, viz. relativised uniform equivalence with projection , which extends standard uniform equivalence in terms of two additional parameters: one for specifying the input alphabet and one for specifying the output alphabet for programs. In particular, the second parameter is used for projecting answer sets to a set of designated output atoms. Answer-set projection, in particular, allows to compare programs that make use of different auxiliary atoms, which is important for practical programming aspects. We introduce novel semantic characterisations for the program correspondence problems under consideration and analyse their computational complexity. In the general case, deciding these problems lies on the third level of the polynomial hierarchy. Therefore, this task cannot be efficiently reduced to propositional answer-set programs itself (under the usual complexity-theoretic assumptions). However, reductions to quantified Boolean formulas (QBFs) are feasible. Indeed, we provide efficient (in fact, linear-time constructible) reductions to QBFs and discuss simplifications for certain special cases. These QBF reductions yield the basis for a prototype implementation, the system cc ⊤, for deciding correspondence problems by using off-the-shelf QBF solvers. We discuss an application of cc ⊤ for verifying the correctness of solutions by students drawn from a laboratory course on logic programming and knowledge representation at the Technische Universität Wien, employing relativised uniform equivalence with projection as the underlying program correspondence notion.

Solving the α-helix correspondence problem at medium-resolution Cryo-EM maps through modeling and 3D matching

Cryo-electron microscopy (cryo-EM) has recently emerged as a prominent biophysical method for macromolecular structure determination. Many research efforts have been devoted to produce cryo-EM images, density maps, at near-atomic resolution. Despite many advances in technology, the resolution of the generated density maps may not be sufficiently adequate and informative to directly construct the atomic structure of proteins. At medium-resolution (∼4-10 Å), secondary structure elements (α-helices and β-sheets) are discernible, whereas finding the correspondence of secondary structure elements detected in the density map with those on the sequence remains a challenging problem. In this paper, an automatic framework is proposed to solve α-helix correspondence problem in three-dimensional space. Through modeling of the sequence with the aid of a novel strategy, the α-helix correspondence problem is initially transformed into a complete weighted bipartite graph matching problem. An innovative correlation-based scoring function based on a well-known and robust statistical method is proposed for weighting the graph. Moreover, two local optimization algorithms, which are Greedy and Improved Greedy algorithms, have been presented to find α-helix correspondence. A widely used data set including 16 reconstructed and 4 experimental cryo-EM maps were chosen to verify the accuracy and reliability of the proposed automatic method. The experimental results demonstrate that the automatic method is highly efficient (86.25% accuracy), robust (11.3% error rate), fast (∼1.4 s), and works independently from cryo-EM skeleton.

Напрямки драматургії у цирковому мистецтві 1990-х-2000-х рр. Проблеми видової релевантності

Стаття присвячена дослідженню генезису «Nouveau Cirque» або «нового цирку» як художнього явища. При цьому основна увага приділяється періоду з початку 1990-х рр. до наших днів, важливою характеристикою якого стала актуалізація питань релевантності циркового мистецтва з урахуванням його взаємозв’язків із театром, що помітно зміцніли в цей час. На основі аналізу сучасного контенту, а також тверджень вітчизняних і зарубіжних дослідників, ставиться проблема теоретичної відповідності театралізованих циркових шоу-програм класичним видовим канонам. Ключовим аспектом статті стало проведення чіткої демаркаційної лінії між театрально-цирковими виставами і театральним мистецтвом, феномен «Nouveau Cirque» був визначений автором як повноцінний етап у розвитку цирку.

A Spike-Based Neuromorphic Architecture of Stereo Vision.

The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems. As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem. Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences. However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored. Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the stereo matching performance with two datasets. Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.