Aiming at the problem that the current defocus blurred region detection methods are easy to misjudge homogeneous-sharp regions, and can′t locate the edge accurately enough, a defocus blurred region detection method based on LBP and saliency is proposed. Firstly, LBP feature and SLIC algorithm are used to obtain SLBP blur map, DRFI saliency detection algorithm is used to obtain DRFI saliency map. Secondly, the trimap is obtained using SLBP blur map and DRFI saliency map, and then KNN matting algorithm is used to obtain a blur map. Finally, the blur map is refined with the help of morphological operations and smoothing filter. The experimental results on the public blur dataset show that the method can effectively detect homogeneous-sharp regions, retain the edge details of an image, and perform well in detection accuracy and recall.

论述了电子信息类专业中"数字信号处理"课程的重要性,详细分析了"数字信号处理"课程教学中存在的问题,探讨了案例式教学法在"数字信号处理"课程中的设计思路,并针对课程中的主要教学内容给出了具体的教学实施方案,最后选取课程中比较典型的两个工程案例,详细阐述了案例教学法的具体实施过程.教学实践证明将案例式教学法引入教学中,通过实际工程应用案例的讨论与实践,能充分调动学生学习的主观能动性,提高学生对数字信号处理的综合应用能力.

Dual-channel contrast prior (Dual-CP) simulates contrast using the difference between the bright channel and the dark channel of an image, and it achieves good results in the blind restoration of blurred images. However, in practical applications, the values of the bright channel and the dark channel of an image are not distributed on 1 and 0 as theoretically researched. This paper proposes a blind image restoration algorithm that combines Dual-CP, L-0 regularization strength, and gradient prior, wherein an effective optimization algorithm is derived using semi-quadratic splitting method to solve the nonconvex L-0 minimization problem. Experiments demonstrate that the proposed method has better intuitive description recovery capabilities, and on the benchmark dataset presented by Levin et al., Kohler et al., and Lai et al., the average peak signal-to-noise ratio increased by 2. 1051 dB, 1. 1273 dB, and 0. 4491 dB, respectively, and the average structural similarity increased by 0. 1302, 0. 0599, and 0. 0158, respectively.

针对面部动力谱(FDM)特征易受光照影响、对运动信息描述不准确的缺陷,提出基于FDM特征与时空局部二值模式积分投影(STLBP-IP)特征相结合的微表情识别方法.将FDM特征与STLBP-IP特征相结合,在弥补FDM对运动信息描述不足的同时对人脸面部信息进行补充描述以提升识别率.使用支持向量机进行分类,在SMIC和CASMEⅡ微表情数据库上进行实验.实验结果表明,该算法识别率有所提高,分别达到57.14％和64.59％.

城市交通流具有非线性、相关性、周期性等特点,其建模和准确预测难度较大.为提高预测准确率,在雨天、非雨天和工作日、非工作日四种影响因素下搭建基于长短期记忆(LSTM)网络模型的双向长短期记忆(BiLSTM)网络模型用于提取不同因素下的数据周期特性,利用实测数据进行交通流预测分析实验.结果表明,与不考虑影响因素即随机环境相比,在考虑影响因素下BiLSTM模型预测的效果稍好;同时,在四个影响因素下,与传统RNN模型、LSTM模型进行比较,BiLSTM模型预测效果均稍好.

复制粘贴篡改是最常见的图像篡改方式之一,常用检测算法中,SIFT算法是基于点特征的检测方法,对小的篡改区域检测性能较差.针对这一问题,本文探讨通过增加MB-LBP纹理特征提取更多的特征匹配对和提升检测定位能力的算法.实验结果表明,小区域的篡改检测定位是可行的.

文章论述了在电子信息类专业中开展"数字信号处理"课程双语教学的重要性和必要性,详细分析了"数字信号处理"课程双语教学中存在的问题,通过在教材选用、教学内容、教学模式及考核方式四个方面的改革及实践,使得教学效果得到有效提升,并总结和分析了教学改革的成效.

The paper presents a recognition system of online overlaid handwritten Japanese text patterns on a smart phone or baby-face tablet. The proposed system oversegments a sequence of strokes into primitive segments at candidate off-strokes between strokes using a SVM model. One or more consecutive primitive segments form a candidate character pattern, which is recognized into a list of candidate categories. Then, a segmentation and recognition candidate lattice is constructed to represent all candidate character patterns and their corresponding character classes. Finally, the optimal path is effectively found by the Viterbi search in the lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. This system incorporates feature reduction and non-character pruning to decrease the time cost per character, and semi-incremental recognition to decrease waiting time. The recognition rates on generated and collected overlaid handwritten text are 92.16% and 93.04%, respectively. The average time cost per character is not more than 0.6s, and the average waiting time is less than 0.875s even on an Intel Atom 1.33GHz CPU: a low power consumption CPU for small tablets and embedded devices. Therefore, we confirm that our system recognizes online overlaid handwritten text composed of thousands of Japanese character classes with the high recognition rate without excessive waiting time.

This paper presents a candidate lattice refinement method for online handwritten Japanese text recognition. In the integrated segmentation-recognition framework, we first over-segment a character string pattern into primitive segments at least at their true boundaries so that each primitive segment may compose a single character or a part of a character. Then a candidate lattice is constructed based on the primitive segments. We search within the candidate lattice to obtain the optimal path as recognition result. In striving for high recognition accuracy, however, the approach must generate many candidate lattice nodes, which ultimately increase the recognition time. To solve this problem, we refine the candidate lattice to eliminate unnecessary nodes before path search and text recognition. For the refinement, we evaluate all segmentation hypotheses by combining the probability of a character verifier using noncharacter samples, the class-independent unary and binary geometric context, as well as character segmentation. We retain N-best paths by beam search to reduce the complexity of the candidate lattice. Experiments on horizontal text lines extracted from the Kondate database show that the proposed method keeps recognition accuracy while reducing recognition time to half.

The paper presents a recognition method of characterposition-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

The paper presents a recognition method of character-position-free (CPF) on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we prepared large sets of CPF handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by the Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, and verify that it produces competing recognition rates as the latest recognizer for normally handwritten horizontal Japanese text without the serious problem in speed for practical applications.

研究了一种有效的词典驱动的联机手写日文病名识别方法.病名词典以树结构存储,包舍21713个病名短语.在切分中,手写病名字符串通过分析相邻笔划之间的空间信息等特征被切分为原始的片段序列.连续的片段动态地合并为候选字符模式,不同的合并方式产生不同的候选字符序列,这样可构成一个切分候选网格.在识别过程中,结合病名词典匹配来限制候选字符模式的类别扩展,采用集束搜索策略来寻找到一条最优路径作为识别结果.用5...