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Journal articles on the topic 'Descriptor transformation'
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CHENG, D., S. Q. XIE, and E. HÄMMERLE. "LOCAL DESCRIPTORS BASED ON COLOR IMAGES." International Journal of Information Acquisition 06, no. 04 (December 2009): 281–301. http://dx.doi.org/10.1142/s0219878909002004.
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This paper investigates local descriptor methods utilizing color images instead of grayscale images to improve the uniqueness of the local descriptors. A color model was utilized in order to be invariant to illumination condition changes. Two local descriptor methods, namely color local descriptor and hybrid local descriptor methods, were developed. Results from the experiments conducted show that these methods are more robust against a variety of image transformation and illumination condition changes compared to existing methods.
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Liu, Cuiyin, Jishang Xu, and Feng Wang. "A Review of Keypoints’ Detection and Feature Description in Image Registration." Scientific Programming 2021 (December 1, 2021): 1–25. http://dx.doi.org/10.1155/2021/8509164.
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For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, TILDE, LIFT, multiscale detector, SuperPoint, and descriptors based on deep learning including pretrained descriptor, Siamese descriptor, LIFT, triplet network, and SuperPoint. Two group of comparison experiments are compared comprehensively and objectively on representative datasets. Finally, we concluded with insightful discussions and conclusions of descriptor and detector selection for specific application problem and hope this survey can be a reference for researchers and engineers in image registration and related fields.
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ELHADY, GAMAL F. "3D STRUCTURE FROM MOTION WITH FOURIER DESCRIPTOR TRANSFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (August 2013): 1355006. http://dx.doi.org/10.1142/s0218001413550069.
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The simultaneous recovery of three-dimensional (3D) structure from motion (SfM) for the sequences of images, is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more image. Motion analysis and 3D shape estimation based on the estimated motion is an important problem in computer vision. The correspondence problem is an important tool in SfM where in this paper a general 3D motion based on a simple rotation, tilt, roll and translation is proposed, and then is used for 3D shape estimation. The current work expands the 2D motion estimation (shift, rotation) to accommodate general 3D motion (shift, rotation, tilt and roll). The proposed work in this paper of motion estimation of moving object is based on Fourier Descriptor Transformation (FDT) analysis before and after motion. The FDT is also used to resolve the correspondence problem in a sequence of images. We test our method on several large-scale photo collections, show the efficacy of the introduced approach to improve reconstruction accuracy.
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Wang, Xu Guang, and Jie Su. "A Novel Descriptor for Line (Curve) Matching." Applied Mechanics and Materials 48-49 (February 2011): 92–97. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.92.
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This paper defines a new image feature called Harris feature vector, which is able to describe the image gradient distribution in an effective way. By computing the mean and the standard deviation of the Harris feature vector in a local image region, novel descriptors are constructed for line (curve) matching which are invariable to image rigid transformation and linear intensity change. Experimental evidence suggests that the novel descriptor for line (curve) matching performs well.
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Xu, Zheng Guang, Chen Chen, and Xu Hong Liu. "An Efficient View-Point Invariant Detector and Descriptor." Advanced Materials Research 659 (January 2013): 143–48. http://dx.doi.org/10.4028/www.scientific.net/amr.659.143.
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Many computer vision applications need keypoint correspondence between images under different view conditions. Generally speaking, traditional algorithms target applications with either good performance in invariance to affine transformation or speed of computation. Nowadays, the widely usage of computer vision algorithms on handle devices such as mobile phones and embedded devices with low memory and computation capability has proposed a target of making descriptors faster to computer and more compact while remaining robust to affine transformation and noise. To best address the whole process, this paper covers keypoint detection, description and matching. Binary descriptors are computed by comparing the intensities of two sampling points in image patches and they are matched by Hamming distance using an SSE 4.2 optimized popcount. In experiment results, we will show that our algorithm is fast to compute with lower memory usage and invariant to view-point change, blur change, brightness change, and JPEG compression.
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Hamdini, Rabah, Nacira Diffellah, and Abderrahmane Namane. "Color Based Object Categorization Using Histograms of Oriented Hue and Saturation." Traitement du Signal 38, no. 5 (October 31, 2021): 1293–307. http://dx.doi.org/10.18280/ts.380504.
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In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.
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Veinidis, Christos, Antonios Danelakis, Ioannis Pratikakis, and Theoharis Theoharis. "Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences." International Journal of Image and Graphics 19, no. 03 (July 2019): 1950018. http://dx.doi.org/10.1142/s0219467819500189.
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Two novel methods for fully unsupervised human action retrieval using 3D mesh sequences are presented. The first achieves high accuracy but is suitable for sequences consisting of clean meshes, such as artificial sequences or highly post-processed real sequences, while the second one is robust and suitable for noisy meshes, such as those that often result from unprocessed scanning or 3D surface reconstruction errors. The first method uses a spatio-temporal descriptor based on the trajectories of 6 salient points of the human body (i.e. the centroid, the top of the head and the ends of the two upper and two lower limbs) from which a set of kinematic features are extracted. The resulting features are transformed using the wavelet transformation in different scales and a set of statistics are used to obtain the descriptor. An important characteristic of this descriptor is that its length is constant independent of the number of frames in the sequence. The second descriptor consists of two complementary sub-descriptors, one based on the trajectory of the centroid of the human body across frames and the other based on the Hybrid static shape descriptor adapted for mesh sequences. The robustness of the second descriptor derives from the robustness involved in extracting the centroid and the Hybrid sub-descriptors. Performance figures on publicly available real and artificial datasets demonstrate our accuracy and robustness claims and in most cases the results outperform the state-of-the-art.
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Yan, Shen Hai, Xian Tong Huang, and Yang Liu. "A Novel Texture Spectrum Descriptor." Applied Mechanics and Materials 397-400 (September 2013): 1494–99. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1494.
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A concept of equivalence classes of texture pattern is put forward according to the visual consistency between the rotation texture and the flip texture. An improved texture spectrum descriptor (iTS) is proposed based on the equivalence classes. The iTS depicts the grayscale variation pattern of the pixels in the image neighbour domain and denotes the texture content of an image with a histogram of texture spectrum. Compared with the basic texture spectrum descriptor (TS), local binary pattern (LBP) and Shis local binary pattern (sLBP), iTS has best precision in the image retrieval experiments. The iTS has stronger ability to describle the texture and more adapt to the image rotation transformation.
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Martey, Ezekiel Mensah, Hang Lei, Xiaoyu Li, and Obed Appiah. "Image Representation Using Stacked Colour Histogram." Algorithms 14, no. 8 (July 30, 2021): 228. http://dx.doi.org/10.3390/a14080228.
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Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.
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Wei, Xiu-Shen, Chen-Lin Zhang, Jianxin Wu, Chunhua Shen, and Zhi-Hua Zhou. "Unsupervised object discovery and co-localization by deep descriptor transformation." Pattern Recognition 88 (April 2019): 113–26. http://dx.doi.org/10.1016/j.patcog.2018.10.022.
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Desai, Alok, and Dah-Jye Lee. "Visual Odometry Drift Reduction Using SYBA Descriptor and Feature Transformation." IEEE Transactions on Intelligent Transportation Systems 17, no. 7 (July 2016): 1839–51. http://dx.doi.org/10.1109/tits.2015.2511453.
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Liao, Fucheng, Zhihua Xue, and Jiang Wu. "Design of an Optimal Preview Controller for a Class of Linear Discrete-Time Descriptor Systems." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1414029.
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The preview control problem of a class of linear discrete-time descriptor systems is studied. Firstly, the descriptor system is decomposed into a normal system and an algebraic equation by the method of the constrained equivalent transformation. Secondly, by applying the first-order forward difference operator to the state equation, combined with the error equation, the error system is obtained. The tracking problem is transformed into the optimal preview control problem of the error system. Finally, the optimal controller of the error system is obtained by using the related results and the optimal preview controller of the original system is gained. In this paper, we propose a numerical simulation method for descriptor systems. The method does not depend on the restricted equivalent transformation.
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Tian, Ying, and De Bin Zhang. "Ear Recognition Based on Point Feature." Applied Mechanics and Materials 380-384 (August 2013): 3840–45. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3840.
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In order to improve recognition rate of human ear, a method based on point feature of image for ear recognition is proposed in this paper. Firstly force field transformation theory is applied to human ear image two times in our method. It can extract the structural feature points and contour feature points of ear respectively and compose feature point set. Then feature points described by the scale invariant feature transformation descriptor. At last nearest neighbor classifier is employed for ear recognition. Feature points extracted from ear image using force field transformation are stable, reliable and discriminative, and they are insensitive to variations in image resolution. Constructing descriptor can resolve the problems caused by lower recognition owing to illumination change, scaling transformation, rotation and minute alteration caused by pose transformation. The experimental results show that the proposed algorithm not only can effectively improve ear recognition rate but also has quite good robustness.
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Lin, Zhiyang, Jihua Zhu, Zutao Jiang, Yujie Li, Yaochen Li, and Zhongyu Li. "Merging Grid Maps in Diverse Resolutions by the Context-based Descriptor." ACM Transactions on Internet Technology 21, no. 4 (July 22, 2021): 1–21. http://dx.doi.org/10.1145/3403948.
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Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end, this article casts it into point set registration problem and proposes an effective map merging method based on the context-based descriptors and correspondence expansion. It first extracts interest points from grid maps by the Harris corner detector. By exploiting neighborhood information of interest points, it automatically calculates the maximum response radius as scale information to compute the context-based descriptor, which includes eigenvalues and normals computed from local structures of each interest point. Then, it effectively establishes origin matches with low precision by applying the nearest neighbor search on the context-based descriptor. Further, it designs a scale-based corresponding expansion strategy to expand each origin match into a set of feature matches, where one similarity transformation between two grid maps can be estimated by the Random Sample Consensus algorithm. Subsequently, a measure function formulated from the trimmed mean square error is utilized to confirm the best similarity transformation and accomplish the coarse map merging. Finally, it utilizes the scaling trimmed iterative closest point algorithm to refine initial similarity transformation so as to achieve accurate merging. As the proposed method considers scale information in the context-based descriptor, it is able to merge grid maps in diverse resolutions. Experimental results on real robot datasets demonstrate its superior performance over other related methods on accuracy and robustness.
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Qu, Xiujie, Fei Zhao, Mengzhe Zhou, and Haili Huo. "A Novel Fast and Robust Binary Affine Invariant Descriptor for Image Matching." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/129230.
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As the current binary descriptors have disadvantages of high computational complexity, no affine invariance, and the high false matching rate with viewpoint changes, a new binary affine invariant descriptor, called BAND, is proposed. Different from other descriptors, BAND has an irregular pattern, which is based on local affine invariant region surrounding a feature point, and it has five orientations, which are obtained by LBP effectively. Ultimately, a 256 bits binary string is computed by simple random sampling pattern. Experimental results demonstrate that BAND has a good matching result in the conditions of rotating, image zooming, noising, lighting, and small-scale perspective transformation. It has better matching performance compared with current mainstream descriptors, while it costs less time.
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DENTSORAS, A. J. "Information generation during design: Information importance and design effort." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 1 (February 2005): 19–32. http://dx.doi.org/10.1017/s089006040505002x.
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The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.
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Huang, Zeyi, Wenxiong Kang, Qiuxia Wu, and Xiaopeng Chen. "A new descriptor resistant to affine transformation and monotonic intensity change." Computer Vision and Image Understanding 120 (March 2014): 117–25. http://dx.doi.org/10.1016/j.cviu.2013.10.010.
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Fan, Hongbiao, Min Meng, and Jun-e. Feng. "Observers of Fuzzy Descriptor Systems with Time-Delays." Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/714518.
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For discrete fuzzy descriptor systems with time-delays, the problem of designing fuzzy observers is investigated in this paper. Based on an equivalent transformation, discrete fuzzy descriptor systems with time-delays are converted into standard discrete systems with time-delays. Then, via linear matrix inequality (LMI) approach, both delay-dependent and delay-independent conditions for the existence of fuzzy state observers are obtained. Finally, two numerical examples are provided to illustrate the proposed method.
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Zhan, Jiang Han, Jing Ma, Jin Hao, and Jian Ding. "Full-Order Filters and Smoothers for Descriptor Systems with Delayed Measurements." Advanced Materials Research 571 (September 2012): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amr.571.559.
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This paper is concerned with the filtering problem for descriptor system with measurement delays. Using full-order transformation, the original descriptor system with delayed measurements is transferred to the normal system with delayed state and delayed measurements. Based on the projection theory, the filter and smoother of the normal system are derived. Then, the full-order filter and smoother of the original descriptor system are given. The proposed estimators avoid the high-dimensional computation from state augmentation. Simulation research verifies the effectiveness of the proposed algorithm.
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Liu, Wei, and Zi Yun Lu. "The Asymptotical Stability Analysis for Switched Descriptor Systems." Applied Mechanics and Materials 29-32 (August 2010): 2150–56. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2150.
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This paper is concerned with the asymptotical stability analysis for a class of switched uncertain descriptor systems with time-delay. The robustly asymptotical stability of this system is proven by making use of the generalized Lyapunov Stability theory, linear matrix inequality (LMI) tools and multiple Lyapunov function techniques. The conservation of result is greatly reduced by means of introducing the optimal weight matrix and avoiding vector matrix inequality in deducing procedure, in which there is no need of transformation and hypothesis for descriptor systems. The designed control law could surely make switched Descriptor Systems quickly approach the balanceable point. Experimentally, one numerical simulation verifies the effectiveness of this method.
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Zheng, Chengyong, Hong Li, and Guokuan Li. "Distance context based PCB film image alignment." Circuit World 40, no. 3 (July 29, 2014): 110–18. http://dx.doi.org/10.1108/cw-03-2014-0006.
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Purpose – This paper presents a novel printed circuit board (PCB) film image alignment method based on distance context of image components, which can be directly used for PCB film inspection. PCB film inspection plays a very important role in PCB production. Design/methodology/approach – First, image components of reference film image and inspected film image are extracted. Then, local distance context (LDC) and global distance context (GDC) are computed for each image component. Using LDC and GDC, the similarity of each pair of components between the reference film image and the inspected film image are computed, the component correspondences can be established accordingly and the parameters for aligning these two images can be eventually estimated. Findings – LDC and GDC act as the local spatial distribution descriptor and the global relative position descriptor of the current component, and they are invariant to translation, rotating and scale. Experimental results on aligning real PCB film images against various rotations and scaling transformation show that the proposed algorithm is fast and accurate and is very suitable for PCB film inspection. Research limitations/implications – The proposed algorithm is suitable for aligning those images that have some isolated connected components, such as the PCB film images. It is not suitable for general image alignment. Originality/value – We put forward to use LDC and GDC as the local descriptor and global descriptor of an image component, and designed a PCB film image alignment algorithm that can overcome the shortcomings of that image alignment algorithm that was based on local feature descriptors such as Fourier descriptor.
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Miciak, Mirosław. "Invariant Radon-Moment Descriptor for Postal Applications." Image Processing & Communications 20, no. 4 (December 1, 2015): 13–21. http://dx.doi.org/10.1515/ipc-2015-0040.
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Abstract In this article a new solution of handwritten digits recognition system for postal applications is presented. Moreover, in this paper, a new approach of handwritten characters recognition was presented. The implemented algorithm is applied to recognition of postal items on the basis of postcode information. In connection with this article the research was carried with all digit characters used in authentic zip code of various mail pieces. Additionally, the paper contains some preliminary image processing for example normalization of the character. The main objective of this article is to use the Radon Transformation and other moments values to obtain an invariant set of character image features, on basis of which postal code will be classified. The reported experiments results prove the effectiveness of the proposed method. Furthermore, causes of errors as well as possible improvement of recognition results will be presented.
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Zhu, Zun Shang, Yue Qiang Zhang, Xiang Zhou, and Yang Shang. "An Affine SIFT Matching Algorithm Based on Local Patch Shape Estimation." Applied Mechanics and Materials 519-520 (February 2014): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.553.
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In this paper we present an affine SIFT matching method to achieve reliable correspondence points in stereo matching with large viewpoint changes. We extended the affine invariant of the conventional SIFT approach by estimating the shape of the local patch around the interest point. Since we can obtain the scale information by SIFT detector, a second moment matrix (SMM) descriptor was employed to describe the shape. Furthermore, by comparing the shapes of the potential matches, we can normalize the template of SIFT descriptor and obtain the initial affine transformation. At last, we applied the iterative based method to achieve a fine registration with the estimated initial transformation parameters. The experiment results show that the proposed method is more robust to viewpoint changes and the accuracy of registration is better than feature based methods.
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GENG, LICHUAN, SONGZHI SU, DONGLIN CAO, and SHAOZI LI. "PERSPECTIVE-INVARIANT IMAGE MATCHING FRAMEWORK WITH BINARY FEATURE DESCRIPTOR AND APSO." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (December 2014): 1455011. http://dx.doi.org/10.1142/s0218001414550118.
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A novel perspective invariant image matching framework is proposed in this paper, noted as Perspective-Invariant Binary Robust Independent Elementary Features (PBRIEF). First, we use the homographic transformation to simulate the distortion between two corresponding patches around the feature points. Then, binary descriptors are constructed by comparing the intensity of sample points surrounding the feature location. We transform the location of the sample points with simulated homographic matrices. This operation is to ensure that the intensities which we compared are the realistic corresponding pixels between two image patches. Since the exact perspective transform matrix is unknown, an Adaptive Particle Swarm Optimization (APSO) algorithm-based iterative procedure is proposed to estimate the real transformation angles. Experimental results obtained on five different datasets show that PBRIEF outperforms significantly the existing methods on images with large viewpoint difference. Moreover, the efficiency of our framework is also improved comparing with Affine-Scale Invariant Feature Transform (ASIFT).
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Cao, Mengjuan, and Fucheng Liao. "Design of an Optimal Preview Controller for Linear Discrete-Time Descriptor Noncausal Multirate Systems." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/965915.
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The linear discrete-time descriptor noncausal multirate system is considered for the presentation of a new design approach for optimal preview control. First, according to the characteristics of causal controllability and causal observability, the descriptor noncausal system is constructed into a descriptor causal closed-loop system. Second, by using the characteristics of the causal system and elementary transformation, the descriptor causal closed-loop system is transformed into a normal system. Then, taking advantage of the discrete lifting technique, the normal multirate system is converted to a single-rate system. By making use of the standard preview control method, we construct the descriptor augmented error system. The quadratic performance index for the multirate system is given, which can be changed into one for the single-rate system. In addition, a new single-rate system is obtained, the optimal control law of which is given. Returning to the original system, the optimal preview controller for linear discrete-time descriptor noncausal multirate systems is derived. The stabilizability and detectability of the lifted single-rate system are discussed in detail. The optimal preview control design techniques are illustrated by simulation results for a simple example.
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Wang, J. X., W. X. Wang, C. Y. Wang, H. Zhu, W. Y. He, and S. Y. Liu. "LINE SEGMENT MATCHING ALGORITHM BASED ON FEATURE GROUPING AND LBD DESCRIPTOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 103–9. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-103-2020.
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Abstract. This paper proposes a line-matching algorithm based on feature grouping and a line band descriptor (LBD) to address the insufficient reliability of individual line descriptors for line matching. First, the algorithm generates line-pairs according to geometrical relationships such as the distances and angles between line segments extracted from a single image. Subsequently, the algorithm employs the epipolar line of intersection between two lines in a reference line-pair to constrain candidate pairs corresponding to the reference line-pair. Thereafter, each line in the reference line-pair is considered individually, and its support region and the corresponding support region of each candidate line in the candidate pairs are established, following which an affine transformation is used for unifying the sizes of the reference support region and the candidate support region. Moreover, the LBD descriptor is then used for describing the reference and candidate lines. The Euclidean distances between the reference line and each candidate line descriptors are calculated, and the nearest neighbor distance ratio (NNDR) is used as a criterion for determining the final matching. Finally, the one-to-many and many-to-one line correspondences in matching results are transformed into one-to-one line correspondences by fitting multiple lines to the new line; simultaneously, incorrect matches are eliminated. The experimental results show that the proposed algorithm yields reliable line-matching performance for close-range images.
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Huang, R., W. Yao, Z. Ye, Y. Xu, and U. Stilla. "RIDF: A ROBUST ROTATION-INVARIANT DESCRIPTOR FOR 3D POINT CLOUD REGISTRATION IN THE FREQUENCY DOMAIN." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 235–42. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-235-2020.
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Abstract. Registration of point clouds is a fundamental problem in the community of photogrammetry and 3D computer vision. Generally, point cloud registration consists of two steps: the search of correspondences and the estimation of transformation parameters. However, to find correspondences from point clouds, generating robust and discriminative features is of necessity. In this paper, we address the problem of extracting robust rotation-invariant features for fast coarse registration of point clouds under the assumption that the pairwise point clouds are transformed with rigid transformation. With a Fourier-based descriptor, point clouds represented by volumetric images can be mapped from the image to feature space. It is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the spherical harmonics. The rotation-invariance is established based on the Fourier-based analysis, in which high-frequency signals can be filtered out. This makes the extracted features robust to noises and outliers. Then, with the extracted features, pairwise correspondence can be found by the fast search. Finally, the transformation parameters can be estimated by fitting the rigid transformation model using the corresponding points and RANSAC algorithm. Experiments are conducted to prove the effectiveness of our proposed method in the task of point cloud registration. Regarding the experimental results of the point cloud registration using two TLS benchmark point cloud datasets, featuring with limited overlaps and uneven point densities and covering different urban scenes, our proposed method can achieve a fast coarse registration with rotation errors of less than 1 degree and translation errors of less than 1m.
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Li, Yongfei, Shicheng Wang, Hao He, Deyu Meng, and Dongfang Yang. "Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature." Remote Sensing 13, no. 3 (January 30, 2021): 490. http://dx.doi.org/10.3390/rs13030490.
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We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.
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Kaczorek, Tadeusz. "Analysis of the descriptor Roesser model with the use of the Drazin inverse." International Journal of Applied Mathematics and Computer Science 25, no. 3 (September 1, 2015): 539–46. http://dx.doi.org/10.1515/amcs-2015-0040.
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AbstractA method of analysis for a class of descriptor 2D discrete-time linear systems described by the Roesser model with a regular pencil is proposed. The method is based on the transformation of the model to a special form with the use of elementary row and column operations and on the application of a Drazin inverse of matrices to handle the model. The method is illustrated with a numerical example
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Berenguer Fernández, Yerai. "Robot navigation in dynamic environments using global-appearance descriptors. State of the art." Revista Doctorado UMH 2, no. 1 (July 30, 2015): 2. http://dx.doi.org/10.21134/doctumh.v1i1.662.
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Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed.
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Jiang, Dan-chi, Wei-Yong Yan, and K. L. Teo. "Sensitivity Reduction of Constraint Forces and Position Control for Mechanical Descriptor Systems." Journal of Dynamic Systems, Measurement, and Control 119, no. 2 (June 1, 1997): 286–89. http://dx.doi.org/10.1115/1.2801247.
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This paper deals with the position and force control for mechanical systems with holonomic constraints. Our concern is the design of a feedback controller such that the closed-loop system has a satisfactory transient response and is less sensitive to various types of disturbances. Using an appropriate transformation, the constrained system is converted into an unconstrained system of lower order. Then, an H∞, control problem involving the reduced system is formulated. In the case of state feedback, a systematic design procedure for solving the problem is presented, where the key step is the solution of an algebraic Riccati equation. An example is given to illustrate the effectiveness of the proposed method.
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Jia, Chen, Fucheng Liao, and Jiamei Deng. "Impulse Elimination and Fault-Tolerant Preview Controller Design for a Class of Descriptor Systems." Mathematical Problems in Engineering 2019 (December 24, 2019): 1–13. http://dx.doi.org/10.1155/2019/3857275.
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In this paper, a fault-tolerant preview controller is designed for a class of impulse controllable continuous time descriptor systems with sensor faults. Firstly, the impulse is eliminated by introducing state prefeedback; then an algebraic equation and a normal control system are obtained by restricted equivalent transformation for the descriptor system after impulse elimination. Next, the model following problem in fault-tolerant control is transformed into the optimal regulation problem of the augmented system which is constructed by a general method. And the final augmented system and its corresponding performance index function are obtained by state feedback for the augmented system constructed above. The controller with preview effect for the final augmented system is attained based on the existing conclusions of optimal preview control; then, the fault-tolerant preview controller for the original system is obtained through integral and backstepping. The relationships between the stabilisability and detectability of the final augmented system and the corresponding characteristics of the original descriptor system are also strictly discussed. The effectiveness of the proposed method is verified by numerical simulation.
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Royal, Virginie, Jarcy Zee, Qian Liu, Carmen Avila-Casado, Abigail R. Smith, Gang Liu, Laura H. Mariani, et al. "Ultrastructural Characterization of Proteinuric Patients Predicts Clinical Outcomes." Journal of the American Society of Nephrology 31, no. 4 (February 21, 2020): 841–54. http://dx.doi.org/10.1681/asn.2019080825.
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BackgroundThe analysis and reporting of glomerular features ascertained by electron microscopy are limited to few parameters with minimal predictive value, despite some contributions to disease diagnoses.MethodsWe investigated the prognostic value of 12 electron microscopy histologic and ultrastructural changes (descriptors) from the Nephrotic Syndrome Study Network (NEPTUNE) Digital Pathology Scoring System. Study pathologists scored 12 descriptors in NEPTUNE renal biopsies from 242 patients with minimal change disease or FSGS, with duplicate readings to evaluate reproducibility. We performed consensus clustering of patients to identify unique electron microscopy profiles. For both individual descriptors and clusters, we used Cox regression models to assess associations with time from biopsy to proteinuria remission and time to a composite progression outcome (≥40% decline in eGFR, with eGFR<60 ml/min per 1.73 m2, or ESKD), and linear mixed models for longitudinal eGFR measures.ResultsIntrarater and interrater reproducibility was >0.60 for 12 out of 12 and seven out of 12 descriptors, respectively. Individual podocyte descriptors such as effacement and microvillous transformation were associated with complete remission, whereas endothelial cell and glomerular basement membrane abnormalities were associated with progression. We identified six descriptor-based clusters with distinct electron microscopy profiles and clinical outcomes. Patients in a cluster with more prominent foot process effacement and microvillous transformation had the highest rates of complete proteinuria remission, whereas patients in clusters with extensive loss of primary processes and endothelial cell damage had the highest rates of the composite progression outcome.ConclusionsSystematic analysis of electron microscopic findings reveals clusters of findings associated with either proteinuria remission or disease progression.
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Wang, Yan Wei, Si Qing Zhang, Bing Lin, Hong Liang, and Yan Ming Pan. "Feature Point Extraction Method of X-Ray Image Based on Scale Invariant." Applied Mechanics and Materials 274 (January 2013): 667–70. http://dx.doi.org/10.4028/www.scientific.net/amm.274.667.
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Feature Point Extraction Method of X-ray Image Based on Scale Invariant is proposed in this paper for industrial X-ray image with low contrast and some artifacts. First of all, the scale transformation of original image is adopted by the Gaussian kernel to building the DOG multi-scale pyramid. Then, the location and scale of the key points is fixed by the three-dimensional quadratic function. Finally, the Simply SIFT descriptor illustrates the key points. Experimental results show that the algorithm has good stability in translation, rotation and affine transformation, especially with 10 percent normalized Gaussian noise, this algorithm can still be detected feature points accuracy.
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Nitta, Tohru. "Learning Transformations with Complex-Valued Neurocomputing." International Journal of Organizational and Collective Intelligence 3, no. 2 (April 2012): 81–116. http://dx.doi.org/10.4018/joci.2012040103.
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The ability of the 1-n-1 complex-valued neural network to learn 2D affine transformations has been applied to the estimation of optical flows and the generation of fractal images. The complex-valued neural network has the adaptability and the generalization ability as inherent nature. This is the most different point between the ability of the 1-n-1 complex-valued neural network to learn 2D affine transformations and the standard techniques for 2D affine transformations such as the Fourier descriptor. It is important to clarify the properties of complex-valued neural networks in order to accelerate its practical applications more and more. In this paper, first, the generalization ability of the 1-n-1 complex-valued neural network which has learned complicated rotations on a 2D plane is examined experimentally and analytically. Next, the behavior of the 1-n-1 complex-valued neural network that has learned a transformation on the Steiner circles is demonstrated, and the relationship the values of the complex-valued weights after training and a linear transformation related to the Steiner circles is clarified via computer simulations. Furthermore, the relationship the weight values of the 1-n-1 complex-valued neural network learned 2D affine transformations and the learning patterns used is elucidated. These research results make it possible to solve complicated problems more simply and efficiently with 1-n-1 complex-valued neural networks. As a matter of fact, an application of the 1-n-1 type complex-valued neural network to an associative memory is presented.
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Chen, Fang, and Yun Xi Xu. "High-Speed and Robust Scene Matching Algorithm Based on ORB for SAR/INS Integrated Navigation System." Applied Mechanics and Materials 241-244 (December 2012): 439–43. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.439.
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It is important that scene matching algorithm should satisfy the requirements of real-time, robustness and high-precision for inertial integrated navigation system. And considering the serious distortion and speckle noises of SAR images, we proposed a new scene matching algorithm for the SAR/INS integrated navigation system with high-speed and robustness based on Oriented FAST and Rotated BRIEF (ORB). We started by detecting scale-space FAST-based features in combination with an efficiently computed orientation in the image. Then, we calculated feature point's Rotation-Aware BRIEF descriptor which performs well with rotation and match features by computing Hamming distance between descriptors. Finally, we adopted GroupSAC which are proposed recently to remove the false matching points and the least square algorithm for getting the distortion transformation parameters that are the aircraft position errors and rotation transform parameters between real image and reference image. Experimental results on real SAR images indicate that our algorithm is invariant to various image transformations due to rotation and scale, and also robust to speckle noise and extremely efficient to compute, better than SIFT in many situations. Therefore, our algorithm can meet the high performance needs for matching navigation in the SAR/INS integrated navigation system.
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Peng, Yaxin, Naiwu Wen, Chaomin Shen, Xiaohuang Zhu, and Shihui Ying. "Parallel calibration based on modified trim strategy." Assembly Automation 40, no. 2 (October 10, 2019): 249–56. http://dx.doi.org/10.1108/aa-06-2019-0104.
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Purpose Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy. Design/methodology/approach First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency. Findings The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process. Research limitations/implications This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation. Practical implications In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation. Social implications Point set calibration is a building block in the field of intelligent manufacturing. Originality/value The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.
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Ji, Huang, Bo Yucheng, and Wang Huiyuan. "Robust control of delay-dependent T-S fuzzy system based on method of descriptor model transformation." Artificial Intelligence Review 34, no. 3 (June 5, 2010): 205–20. http://dx.doi.org/10.1007/s10462-010-9170-1.
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Pinjai, Sirada, and Kanit Mukdasai. "New Robust Exponential Stability Criterion for Uncertain Neutral Systems with Discrete and Distributed Time-Varying Delays and Nonlinear Perturbations." Abstract and Applied Analysis 2011 (2011): 1–16. http://dx.doi.org/10.1155/2011/463603.
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We investigate the problem of robust exponential stability for uncertain neutral systems with discrete and distributed time-varying delays and nonlinear perturbations. Based on the combination of descriptor model transformation, decomposition technique of coefficient matrix, and utilization of zero equation and new Lyapunov functional, sufficient conditions for robust exponential stability are obtained and formulated in terms of linear matrix inequalities (LMIs). The new stability conditions are less conservative and more general than some existing results.
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Gao, Zhiyun, Huaguang Zhang, Jie Duan, and Yuliang Cai. "Consensus conditions for higher-order descriptor multi-agent systems with communication time-delays." Transactions of the Institute of Measurement and Control 42, no. 11 (March 6, 2020): 2127–36. http://dx.doi.org/10.1177/0142331220906634.
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In this paper, the consensus problem for higher-order descriptor multi-agent systems (DMASs) with time-delays is investigated. Firstly, a distributed consensus protocol based on output feedback is proposed. Secondly, the state transformation is presented to convert the consensus problem into the stability problem. Based on the Lyapunov theory and the linear matrix inequality (LMI) method, sufficient conditions in terms of LMIs are derived to ensure that consensus of DMASs can be achieved. Moreover, the consensus problem for higher-order DMASs with delays is solved under two cases where interaction topologies are undirected and directed, which is more complicated and challenging. Finally, numerical simulations are provided to demonstrate the effectiveness of our results.
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HUSSAIN, MUHAMMAD, IHSAN ULLAH, HATIM A. ABOALSAMH, GHULAM MUHAMMAD, GEORGE BEBIS, and ANWAR MAJID MIRZA. "GENDER RECOGNITION FROM FACE IMAGES WITH DYADIC WAVELET TRANSFORM AND LOCAL BINARY PATTERN." International Journal on Artificial Intelligence Tools 22, no. 06 (December 2013): 1360018. http://dx.doi.org/10.1142/s021821301360018x.
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Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant wavelet transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline dyadic wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively.
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Tahir, Mehwish, Nadia Kanwal, and Fatima Anjum. "FAB: Fast Angular Binary Descriptor for Matching Corner Points in Video Imagery." Journal of Robotics 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3458207.
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Image matching is a fundamental step in several computer vision applications where the requirement is fast, accurate, and robust matching of images in the presence of different transformations. Detection and more importantly description of low-level image features proved to be a more appropriate choice for this purpose, such as edges, corners, or blobs. Modern descriptors use binary values to store neighbourhood information of feature points for matching because binary descriptors are fast to compute and match. This paper proposes a descriptor called Fast Angular Binary (FAB) descriptor that illustrates the neighbourhood of a corner point using a binary vector. It is different from conventional descriptors because of selecting only the useful neighbourhood of corner point instead of the whole circular area of specific radius. The descriptor uses the angle of corner points to reduce the search space and increase the probability of finding an accurate match using binary descriptor. Experiments show that FAB descriptor’s performance is good, but the calculation and matching time is significantly less than BRIEF, the best known binary descriptor, and AMIE, a descriptor that uses entropy and average intensities of informative part of a corner point for the description.
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Bohush, Rykhard, Sergey Ablameyko, Tatiana Kalganova, and Pavel Yarashevich. "Extraction of image parking spaces in intelligent video surveillance systems." Machine Graphics and Vision 27, no. 1/4 (December 1, 2019): 47–62. http://dx.doi.org/10.22630/mgv.2018.27.1.3.
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This paper discusses the algorithmic framework for image parking lot localization and classification for the video intelligent parking system. Perspective transformation, adaptive Otsu's binarization, mathematical morphology operations, representation of horizontal lines as vectors, creating and filtering vertical lines, and parking space coordinates determination are used for the localization of parking spaces in a~video frame. The algorithm for classification of parking spaces is based on the Histogram of Oriented Descriptors (HOG) and the Support Vector Machine (SVM) classifier. Parking lot descriptors are extracted based on HOG. The overall algorithmic framework consists of the following steps: vertical and horizontal gradient calculation for the image of the parking lot, gradient module vector and orientation calculation, power gradient accumulation in accordance with cell orientations, blocking of cells, second norm calculations, and normalization of cell orientation in blocks. The parameters of the descriptor have been optimized experimentally. The results demonstrate the improved classification accuracy over the class of similar algorithms and the proposed framework performs the best among the algorithms proposed earlier to solve the parking recognition problem.
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NEWMAN, JANET, CAROLINE GLENDINNING, and MICHAEL HUGHES. "Beyond Modernisation? Social Care and the Transformation of Welfare Governance." Journal of Social Policy 37, no. 4 (October 2008): 531–57. http://dx.doi.org/10.1017/s0047279408002201.
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AbstractThis article reflects on the process and outcomes of modernisation in adult social care in England and Wales, drawing particularly on the recently completed Modernising Adult Social Care (MASC) research programme commissioned by the Department of Health. We begin by exploring the contested status of ‘modernisation’ as a descriptor of reform. We then outline some of the distinctive features of adult social care services and suggest that these features introduce dynamics likely to shape both the experiences and outcomes of policy ambitions for modernisation. We then reflect on the evidence emerging from the MASC studies and develop a model for illuminating some of the dynamics of welfare governance. Finally, we highlight the emerging focus on individualisation and on user-directed and controlled services. We argue that the current focus of modernisation involves a reduced emphasis on structural and institutional approaches to change and an increased emphasis on changes in the behaviours and roles of adult social care service users. This focus has implications for both the future dynamics of welfare governance and for conceptions of citizenship.
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Polewski, P., A. Erickson, W. Yao, N. Coops, P. Krzystek, and U. Stilla. "OBJECT-BASED COREGISTRATION OF TERRESTRIAL PHOTOGRAMMETRIC AND ALS POINT CLOUDS IN FORESTED AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 347–54. http://dx.doi.org/10.5194/isprsannals-iii-3-347-2016.
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Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76 x 121 m<sup>2</sup>) and a photogrammetric point cloud (33 x 35 m<sup>2</sup>) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66 cm.
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Polewski, P., A. Erickson, W. Yao, N. Coops, P. Krzystek, and U. Stilla. "OBJECT-BASED COREGISTRATION OF TERRESTRIAL PHOTOGRAMMETRIC AND ALS POINT CLOUDS IN FORESTED AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 347–54. http://dx.doi.org/10.5194/isprs-annals-iii-3-347-2016.
Full textAbstract:
Airborne Laser Scanning (ALS) and terrestrial photogrammetry are methods applicable for mapping forested environments. While ground-based techniques provide valuable information about the forest understory, the measured point clouds are normally expressed in a local coordinate system, whose transformation into a georeferenced system requires additional effort. In contrast, ALS point clouds are usually georeferenced, yet the point density near the ground may be poor under dense overstory conditions. In this work, we propose to combine the strengths of the two data sources by co-registering the respective point clouds, thus enriching the georeferenced ALS point cloud with detailed understory information in a fully automatic manner. Due to markedly different sensor characteristics, coregistration methods which expect a high geometric similarity between keypoints are not suitable in this setting. Instead, our method focuses on the object (tree stem) level. We first calculate approximate stem positions in the terrestrial and ALS point clouds and construct, for each stem, a descriptor which quantifies the 2D and vertical distances to other stem centers (at ground height). Then, the similarities between all descriptor pairs from the two point clouds are calculated, and standard graph maximum matching techniques are employed to compute corresponding stem pairs (tiepoints). Finally, the tiepoint subset yielding the optimal rigid transformation between the terrestrial and ALS coordinate systems is determined. We test our method on simulated tree positions and a plot situated in the northern interior of the Coast Range in western Oregon, USA, using ALS data (76 x 121 m<sup>2</sup>) and a photogrammetric point cloud (33 x 35 m<sup>2</sup>) derived from terrestrial photographs taken with a handheld camera. Results on both simulated and real data show that the proposed stem descriptors are discriminative enough to derive good correspondences. Specifically, for the real plot data, 24 corresponding stems were coregistered with an average 2D position deviation of 66 cm.
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Sun, Bo, Yadan Zeng, Houde Dai, Junhao Xiao, and Jianwei Zhang. "A novel scan registration method based on the feature-less global descriptor – spherical entropy image." Industrial Robot: An International Journal 44, no. 4 (June 19, 2017): 552–63. http://dx.doi.org/10.1108/ir-11-2016-0329.
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Purpose This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering. Design/methodology/approach The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering. Findings No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans. Originality/value A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.
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Yan, Maode, Aryan Saadat Mehr, and Yang Shi. "Discrete-Time Sliding-Mode Control of Uncertain Systems with Time-Varying Delays via Descriptor Approach." Journal of Control Science and Engineering 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/489124.
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This paper considers the problem of robust discrete-time sliding-mode control (DT-SMC) design for a class of uncertain linear systems with time-varying delays. By applying a descriptor model transformation and Moon's inequality for bounding cross terms, a delay-dependent sufficient condition for the existence of stable sliding surface is given in terms of linear matrix inequalities (LMIs). Based on this existence condition, the synthesized sliding mode controller can guarantee the sliding-mode reaching condition of the specified discrete-time sliding surface for all admissible uncertainties and time-varying delays. An illustrative example verifies the effectiveness of the proposed method.
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Yang, Feng, Mingyue Ding, and Xuming Zhang. "Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor." Sensors 19, no. 21 (October 28, 2019): 4675. http://dx.doi.org/10.3390/s19214675.
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The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.
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Mohanraj, V., V. Vaidehi, S. Vasuhi, and Ranajit Kumar. "A Novel Approach for Face Recognition under Varying Illumination Conditions." International Journal of Intelligent Information Technologies 14, no. 2 (April 2018): 22–42. http://dx.doi.org/10.4018/ijiit.2018040102.
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Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better recognition rate than existing methods, overcoming the challenges like varying illumination and noise