2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)最新文献

Document Image Classification using SqueezeNet Convolutional Neural Network 基于SqueezeNet卷积神经网络的文档图像分类

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066032

M. Hassanpour, H. Malek

引用次数: 8

Finger Knuckle Surface Print Verification using Gabor Filter 使用Gabor滤波器的指关节表面指纹验证

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066108

Mahsa Arab, S. Rashidi

引用次数: 3

Content-based Image Retrieval Using Color Difference Histogram in Image Textures 基于图像纹理色差直方图的基于内容的图像检索

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066062

A. Ajam, M. Forghani, M. M. AlyanNezhadi, Hamed Qazanfari, Zahra Amiri

{"title":"Content-based Image Retrieval Using Color Difference Histogram in Image Textures","authors":"A. Ajam, M. Forghani, M. M. AlyanNezhadi, Hamed Qazanfari, Zahra Amiri","doi":"10.1109/ICSPIS48872.2019.9066062","DOIUrl":"https://doi.org/10.1109/ICSPIS48872.2019.9066062","url":null,"abstract":"The aim of content-based image retrieval system is finding similar images to the query image from a database based on its visual content. In this paper, a novel retrieval system based on human vision is proposed. A factor that has a high impact on the search process is a set of features which are used in. The recent studies emerged that the human eye system considers the image content, texture, and color properties more than other features. Therefore, to retrieve more precisely the images, features should be used that are close to the human eye system. In the current paper, at first, the texture is extracted from the images using the local binary patterns algorithm. After that, the color differences of two adjacent pixels with the same texture are calculated in the HSV color space. Afterward, the histogram is taken from the color difference values. The obtained features from the histogram can describe the visual content of the images in more detail. Finally, the effective features are selected based on their entropy value. The prominent advantage of the proposed method is the lack of implementation of segmentation, clustering, training, and any other method of machine learning, which requires a lot of processing and time. The method is evaluated on two standard Corel 10K and Corel 5K databases, and its retrieval rate is significantly improved compared to some recent methods.","PeriodicalId":371349,"journal":{"name":"2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"891 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116382921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A Novel Phase-Domain Speech Watermarking Method using Perceptual Orthogonal Matching Pursuit 一种基于感知正交匹配追踪的相域语音水印方法

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066046

Nasrin Montazeri, N. Faraji, Mohammadreza Hassannejad Bibalan

引用次数: 1

A Disturbance Activation Approach to Collision Avoidance Autonomous Driving 一种干扰激活的避碰自动驾驶方法

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066077

H. B. Jond, J. Platoš

引用次数: 0

Job-Shop Scheduling with Fuzzy Due Date by Multi-Objective Particle Swarm Optimization 模糊交货期作业车间调度的多目标粒子群算法

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066163

M. Abdolrazzagh-Nezhad, Saeed Sarbishegi

引用次数: 0

A Structured Gamification Approach for Improving Children's Performance in Online Learning Platforms 一种结构化的游戏化方法用于提高儿童在在线学习平台上的表现

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066006

Yazdan Takbiri, Amineh Amini, A. Bastanfard

引用次数: 3

Spatial-Spectral Feature Extraction of Hyperspectral Images using Attribute Profile With Partial Reconstruction and 3-D Gabor Filter Bank 基于部分重构属性轮廓和三维Gabor滤波器组的高光谱图像空间光谱特征提取

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066038

M. Dowlatshah, H. Ghassemian, M. Imani

{"title":"Spatial-Spectral Feature Extraction of Hyperspectral Images using Attribute Profile With Partial Reconstruction and 3-D Gabor Filter Bank","authors":"M. Dowlatshah, H. Ghassemian, M. Imani","doi":"10.1109/ICSPIS48872.2019.9066038","DOIUrl":"https://doi.org/10.1109/ICSPIS48872.2019.9066038","url":null,"abstract":"Simultaneously spatial-spectral feature extraction is preferred for classification of remotely sensed images. In this paper, attribute filters with partial reconstruction are applied for extraction of spatial characteristics of hyperspectral images. In addition, the 3-D Gabor filter bank is used for simultaneously extraction of spatial-spectral features. For implementation of attribute profiles with partial reconstruction, the first 3 principal components of data and 3 types of attributes (area, inertia, and standard deviation) are used for spatial features extraction. For extraction of 3-D Gabor features, firstly the 3-D Gabor filter bank is applied to the data. Then, the spatial and spectral properties in the first 20 principal components output are computed for classification. In the proposed method, the principal component analysis transform is used for dimensionality reduction and avoiding the complexity of the calculations. The support vector machine is used as classifier where its input is the spectral-spatial features extracted by attribute filters with partial reconstruction and 3-D Gabor filters.","PeriodicalId":371349,"journal":{"name":"2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114940798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Tracking Soccer Players in Shaded Playfield Video 改进跟踪足球运动员在阴影操场视频

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066103

A. Bastanfard, Sajjad Jafari, Dariush Amirkhani

{"title":"Improving Tracking Soccer Players in Shaded Playfield Video","authors":"A. Bastanfard, Sajjad Jafari, Dariush Amirkhani","doi":"10.1109/ICSPIS48872.2019.9066103","DOIUrl":"https://doi.org/10.1109/ICSPIS48872.2019.9066103","url":null,"abstract":"Soccer is the most popular sport in the world and the information extracted from this match has many uses. It can be used to extract players ‘paths, recognize the performance, and evaluate the players‘ statistics, evaluate the referee‘s decision, and so on. One of the main steps in analyzing soccer video is tracking players that seeks to locate players when playing video. Player tracking involves various processes, such as playfield detection, player detection, tracking of players, apparent modeling of players, and identification of players overlapping. One of the challenges in this field is the tracing of players in the shaded play field, which challenges the tracking of players due to light changes in the field. In this paper, using the proposed algorithm to identify and Tracking players in television shows with two shaded playfield and sun shades. The proposed method is identified the playfield by using the saliency map algorithm and shadow elimination, which will minimize the noise from the stadium area. Then by using the features of the color, brightness and edge we will recognize the players. Using the combination of Top-hat transformation and the morphological operation the lines of the playfield is detected. Finally, by using the results of the detection step we will track players. The tracker used in this study is an improved particle filter that uses a combination of color and edge features. The results of the proposed method demonstrate that the detection of play field has 93% accuracy. Also the proposed method tracks the detected players with 90% precision. Therefore tracking accuracy shows that light variations have very little effect on it.","PeriodicalId":371349,"journal":{"name":"2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124809263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Multi-View Human Action Recognition System in Limited Data Case using Multi-Stream CNN 基于多流CNN的有限数据情况下多视角人体动作识别系统

2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) Pub Date : 2019-12-01 DOI: 10.1109/ICSPIS48872.2019.9066079

Vahid Ashkani Chenarlogh, F. Razzazi, Najmeh Mohammadyahya

{"title":"A Multi-View Human Action Recognition System in Limited Data Case using Multi-Stream CNN","authors":"Vahid Ashkani Chenarlogh, F. Razzazi, Najmeh Mohammadyahya","doi":"10.1109/ICSPIS48872.2019.9066079","DOIUrl":"https://doi.org/10.1109/ICSPIS48872.2019.9066079","url":null,"abstract":"In recent years, Convolutional Neural Networks (CNNs) have been extensively used for human action recognition. However, training a convolutional neural network by limited data is a challenging problem. In this paper, a multi-stream 3DCNN structure is proposed for multi-view human action recognition. In this model, a four-stream 3D-CNN with handcrafted features, containing optical flow and gradients in horizontal and vertical directions, is proposed as a solution to improve the recognition performance in the case of limited data. This model combines multi-view four-stream 3D-CNNs from different views. The proposed multi-stream 3D-CNN is applied to IXMAS and NIXMAS multi-view datasets. The experiments illustrate superior results in comparison with state-of-the-art methods. The results show 3.58% improvement in comparison with single stream 3D-CNN architecture using raw video data in IXMAS dataset. However, with more limitations in number of training data in NIXMAS dataset, results show remarkable improvement in comparison with single stream 3D-CNN structure that is 12.6%.","PeriodicalId":371349,"journal":{"name":"2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129232896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19