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pattern encoding of Robust M-Array Driven by Texture Constraints

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Zhou, Xiao Zhou, Cong Kang, Yu Zhang, Tingting Mou, Xingang Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China Baidu Informat Technol Co Ltd Beijing 100085 Peoples R China

M-Array pattern encoding is widely used in structured light for 3-D reconstruction due to its single-projection and single-imaging characteristics. The robustness of M-Array in 3-D reconstruction can be assessed by the accuracy of the global positioning of the code word and the density of the decodable feature points. This article proposes a pattern encoding method of robust M-Array based on texture constraints. M-Arrays are formed by matching and splicing candidate sub-windows, which are composed of symbols obtained from an M-Sequence one by one. To construct an M-Array with more texture information, both the number of edges and the maximum rectangular area within the sub-window are constrained. A novel three-level backtracking mechanism reduces the time complexity. Four indicators are introduced to evaluate the robustness of the M-Array and used to compare the proposed method with existing methods. The results show that the proposed method has low similarity between adjacent sub-windows and high uniformity of symbol distribution globally, indicating better accuracy of sub-window decoding. A 3-D reconstruction experiment in the actual scenes proves that the point cloud density and accuracy obtained by solving the M-Array pattern constructed by the method proposed are better.

关键词： 3-D reconstruction binary pattern M-Array pattern encoding pseudorandom pattern structured light

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A lossy/lossless compression method for printed typeset bi-level text images based on improved pattern matching

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2009年第4期11卷 159-182页

作者： Grailu, Hadi Lotfizad, Mojtaba Sadoghi-Yazdi, Hadi Tarbiat Modares Univ Dept Engn Tehran Iran Tarbiat Moallem Univ Sabzevar Dept Engn Sabzavar Iran Ferdowsi Univ Mashad Dept Comp Mashhad Iran

pattern matching is the most widely used technique for the compression of printed bi-level text images. In some printed scripts, letters normally attach to each other, or some letters have a simple relation to each other, or there may be undesired touching characters. Detecting such situations and exploiting them to reduce the library size, has a rather great effect on the compression ratio. In this paper, a lossy/lossless compression method for printed typeset bi-level text images is proposed for archiving purposes. For this, three techniques are proposed. First, the number of library prototypes is reduced by detecting and exploiting the mentioned situations. Second, a new effective encoding scheme is proposed for patterns and numbers. Third, three levels are proposed for lossy compression. Experimental results show that the proposed method works better, as high as 1.4-3.3 times in lossy case and 1.2-2.7 times in lossless case at 300 dpi, than the best existing compression methods or standards.

关键词： pattern encoding Library size reduction Chain coding Attaching characters pattern matching Binary text image compression

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Encoded pattern classification using constructive learning algorithms based on learning vector quantization

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NEURAL NETWORKS 1998年第2期11卷 315-322页

作者： Murthy, CNSG Venkatesh, YV Indian Inst Sci Dept Elect Engn Comp Vis & Artificial Intelligence Lab Bangalore 560012 Karnataka India

A novel encoding technique is proposed for the recognition of patterns using four different techniques for training artificial neural networks (ANNs) of the Kohonen type. Each template or model pattern is overlaid on a radial grid of appropriate size, and converted to a two-dimensional feature array which then acts as the training input to the ANN. The first technique employs Kohonen's self-organizing network, each neuron of which is assigned, after training, the label of the model pattern. It is found that a graphical plot of the labels of the neurons exhibits clusters (which means in effect that the feature array pertaining to distorted versions of the same pattern belongs to a specific cluster), thereby justifying the coding strategy used in this paper. When the new, unknown pattern is input to the network, it is classified to have the same label of the neuron whose corresponding model pattern is closest to the given pattern. In an attempt to reduce the computational time and the size of the network, and simultaneously improve accuracy in recognition, Kohonen's learning vector quantization (LVQ) algorithm is used to train the ANN. To further improve the network's performance and to realize a network of minimum size, two constructive learning algorithms, both based on LVQ, are proposed: (1) multi-step learning vector quantization (MLVQ), and (2) thermal multi-step learning vector quantization (TLVQ). When the proposed algorithms are applied to the classification of noiseless and noisy (and distorted) patterns, the results demonstrate that the pattern encoding strategy and the suggested training techniques for ANNs are efficient and robust. For lack of space, only the most essential results are presented here. For details, see Ganesh Murthy and Venkatesh (1996b). (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： Kohonen network learning learning vector quantization pattern classification pattern encoding self-organization

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Density codes and shape spaces

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NEURAL NETWORKS 2006年第4期19卷 429-445页

作者： Courrieu, Pierre Univ Aix Marseille 1 UMR 6146 CNRS Lab Psychol CognitCtr St Charles F-13331 Marseille 1 France

This paper presents an algorithm that allows for encoding probability density functions associated to samples of points of R-n. The resulting code is a sequence of points of R-n whose density function approximates that of the set of data points. However, contrarily to sampled data points, code points associated to two different density functions can be matched, which allows to efficiently compare such functions. Moreover, the comparison of two codes can be made invariant to a wide variety of geometrical transformations of the support coordinates, provided that the Jacobian matrix of the transformation be everywhere triangular, with a strictly positive diagonal. Such invariances are commonly encountered in visual shape recognition, for example. Thus, using this tool, one can build spaces of shapes that are suitable input spaces for pattern recognition and pattern analysis neural networks. Moreover, a parallel neural implementation of the encoding algorithm is available for 2D image data. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： sets of points density functions pattern encoding geometrical invariants shape similarity

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A Novel Technique for Fast Content-Based Image Retrieval using Dual-Cross patterns. 3

A Novel Technique for Fast Content-Based Image Retrieval usi...

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3rd International Conference for Convergence in Technology (I2CT)

作者： Kakde, Bhavana Okade, Manish Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India

ISBN: (纸本)9781538642733

This proposed work introduces a novel technique for Fast Content-Based Image Retrieval (CBIR) using Dual-Cross patterns (DCP). DCP encodes second order information in the vertical, horizontal and diagonal direction, by performing the encoding of sample points in the local surrounding region of every center pixel in an image. The local binary pattern (LBP), an efficient visual texture descriptor, performs a comparison between the center pixel and its neighboring pixel. Local tetra patterns (LTrP) is a method which acquires more detailed information by using four possible directions of every center pixel in an image, and is calculated from first order derivatives in horizontal and vertical directions. To analyze its effectiveness, the proposed technique, LBP, and LTrP are compared. Our extensive simulation on Corel database shows that the proposed technique has better time complexity compared to these methods.

关键词： CBIR query image local sampling pattern recognition dual-cross patterns(DCP) pattern encoding local tetra patterns (LTrP)

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Validation of Candlestick patterns as a Technical Indicator for Commodity Traders, Proposal of Improved Candlestick encoding, and Data Mining Improved Candlestick patterns 23

Validation of Candlestick Patterns as a Technical Indicator ...

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13th International Conference on Advances in Information Technology, IAIT 2023

作者： Lu, Jinchun Setthawong, Rachsuda Setthawong, Pisal Assumption University Department of Computer Science United Kingdom Assumption University Department of Digital Business Management United Kingdom

ISBN: (纸本)9798400708497

Traders in financial markets utilize many different technical indicators. One of the most widely used technical indicators in trading is the candlestick pattern, which is the graphical representation of the price movement of a pre-determined time period. The candlestick patterns are widely used by traders to help identify trends, reversals, and signals for entry and exit for creating profitable trading strategies in financial markets. Though candlestick patterns are popular in financial circles, this study aims to use data mining approaches to further scrutinize candlestick patterns with real-world trading data. The study would first examine the effectiveness of some existing well-known candlestick patterns by checking whether the price movements would match the predicted state after the patterns occur. If a pattern is declared as a bullish pattern (e.g., HAMMER), the ratio between increase and decrease should exceed a threshold value to confirm the declaration. With the results of the existing patterns as the benchmark, the study aims to utilize data-mining techniques to search for new candlestick patterns that may perform better than the popular patterns that had been proposed. Data-mining approaches were used in which an encoding method was applied to allow more detailed candlestick patterns to be defined, and to facilitate the pattern-finding process. To validate the newly proposed candlestick patterns, the proposed candlesticks were benchmarked against the original patterns with real trading data. © 2023 ACM.

关键词： candlestick patterns financial market pattern encoding

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Three-Dimensional encoding Approach for Wearable Tactile Communication Devices

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SENSORS 2022年第24期22卷 9568-9568页

作者： Lin, Yan-Ni Li, Yan-Cheng Ge, Song Xu, Jing-Jing Li, Lian-Lin Xu, Sheng-Yong Peking Univ Sch Elect Key Lab Phys & Chem Nanodevices Beijing 100871 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Shandong Univ Sch Microelect Jinan 250100 Peoples R China

In this work, we presented a novel encoding method for tactile communication. This approach was based on several tactile sensory characteristics of human skin at different body parts, such as the head and neck, where location coordinates in the three-dimensional (3D) space were clearly mapped in the brain cortex, and gentle stimulations of vibrational touching with varied strengths were received instantly and precisely. For certain applications, such as playing cards or navigating walk paths for blinded people, we demonstrated specifically designed code lists with different patterns of tactile points in varied temporal sequences. By optimizing these codes, we achieved excellent efficiency and accuracy in our test experiments. As this method matched well with the natural habits of tactile sensory, it was easy to learn in a short training period. The results of the present work have offered a silent, efficient and accurate communication solution for visually impaired people or other users.

关键词： tactile sensory pattern encoding temporal sequence silent communication visually impaired users

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