2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)最新文献

Image gray-level enhancement using Black Hole algorithm 利用黑洞算法增强图像灰度

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161633

Saber Yaghoobi, Saeed Hemayat, H. Mojallali

{"title":"Image gray-level enhancement using Black Hole algorithm","authors":"Saber Yaghoobi, Saeed Hemayat, H. Mojallali","doi":"10.1109/PRIA.2015.7161633","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161633","url":null,"abstract":"Image enhancement methods are known among the most important image processing techniques. Here, image enhancement is considered as an optimization problem and a new heuristic optimization algorithm namely the Black Hole is used to solve it. Image enhancement is a nonlinear optimization problem with its particular constraints and the enhancement process will be done by intensifying each pixel's content. In this paper, BH is employed to find the image's optimum parameters of the transfer function in order to get the best results. BH is used here for its simplicity, ease of implementation, and also its invincibility against the parameter tuning issues. Performance of the proposed enhancement algorithm is tested against some of the well-known enhancement techniques viz. GA, PSO, HE and CS, and the obtained results indicate the robustness and also the outperformance of the proposed algorithm among its other counterparts. Enhancement in opaque images consisting of immense dominant gray values can be listed as one of the proposed method's superiority to that of the other available in literature, which will turn the input image into an enhanced image, featuring embossed textures.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127353647","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: 18

Recognition of Farsi handwriting strokes using profile HMM 利用侧面HMM识别波斯语书写笔划

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161646

Ali Katanforoush, Z. Rezvani

{"title":"Recognition of Farsi handwriting strokes using profile HMM","authors":"Ali Katanforoush, Z. Rezvani","doi":"10.1109/PRIA.2015.7161646","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161646","url":null,"abstract":"This paper aims to stroke recognition, where the strokes are connected forms of cursive handwritten scripts, and in particular, we concern on recognition of Farsi handwriting strokes. In Farsi and some other writing systems, connected letters have special shapes that are often unrecognizable from their separated shapes. Despite that quite efficient algorithms have been developed for recognition of handwritten digits and disjoint letters, adapting these algorithms to stroke recognition is so arduous that development of a holistic approach is preferable. In this paper, we develop a method for Farsi handwriting recognition based on profile-HMM and study aspects of modeling the spatiotemporal features of handwriting strokes. The modular architecture of profile-HMMs provides a flexible framework for stroke modeling. Stroke shrinking and elongation are naturally modeled by the recurrent states and the silent states of profile-HMMs and make the model insensitive to writing speed and subtle slides. Our experimental results show that the profile-HMM is quite robust with respect to downsampling of the curve points, also is robust with respect to various settings in the training procedure. Our method correctly recognizes the main stroke of 90.8%, 98.5%, and 99.2% of handwriting samples, respectively in the top first, top five, and top ten hits.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116395581","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

The effect of the distance from the webcam in heart rate estimation from face video images 距离摄像头对人脸视频图像心率估计的影响

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161622

Atefeh Shagholi, M. Charmi, H. Rakhshan

引用次数: 7

Fast and adaptive license plate recognition algorithm for Persian plates 波斯车牌快速自适应车牌识别算法

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161638

Sina Moayed Baharlou, Saeed Hemayat, A. Saberkari, Saber Yaghoobi

{"title":"Fast and adaptive license plate recognition algorithm for Persian plates","authors":"Sina Moayed Baharlou, Saeed Hemayat, A. Saberkari, Saber Yaghoobi","doi":"10.1109/PRIA.2015.7161638","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161638","url":null,"abstract":"A new Persian license plate recognition algorithm is presented. These operations are highly susceptible to error, especially where the image consists of large amount of either vehicle's linked components or the other existing objects. Although the proposed character recognition procedure is highly optimized for Persian plates, the localization parts can be employed for all types of vehicles. Minimum rectangle bounding box is replaced the common bounding box methods, compensating normal bounding box's inherent flaws. License plate possibility ratio (LPPR) is a robust method proposed here to localize the plate. New method of finding plate's location out of so many rectangles, considering “Sensitive to angle” criterions for characters has also been presented. It should be noted that the process is regardless of the plate's location. Different approach on thresholding namely: “Dynamic Thresholding” is used to overcome the probable drawbacks caused by inappropriate lighting. From OCR point of view, a graph, consisting of two specifications will be formed and a set of rules will be defined to capture the character's label. An automated harassment section is added as the denoising filter, in order to omit the grinning ramifications. Presenting the best percent accuracy (95.33%) among relevant well-known algorithms in localization procedure with 25ms run time of the program, and also the outstanding results with over 97% of percent accuracy in character recognition of Persian plates with 30ms run time of the program on Linux and also average of 90ms on Android, can be listed as strong proofs of algorithm's efficiency.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125742096","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: 5

A binary-segmentation algorithm based on shearlet transform and eigenvectors 基于shearlet变换和特征向量的二值分割算法

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161618

Ladan Sharafyan Cigaroudy, N. Aghazadeh

引用次数: 4

Separation of multiplicative image components by Bayesian Independent Component Analysis 用贝叶斯独立分量分析分离相乘图像分量

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161648

Arash Mehrjou, Babak Nadjar Araabi, Reshad Hosseini

引用次数: 0

Investigation of error propagation and measurement error for 2D block method in Electrical Impedance Tomography 电阻抗层析二维块法误差传播及测量误差研究

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161642

Saeed Zaravi, R. Amirfattahi, B. Vahdat

引用次数: 1

Retinal vessel segmentation using system fuzzy and DBSCAN algorithm 采用系统模糊和DBSCAN算法分割视网膜血管

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161643

Negar Riazifar, Ehsan Saghapour

引用次数: 3

CGSR features: Toward RGB-D image matching using color gradient description of geometrically stable regions CGSR特征:利用几何稳定区域的颜色梯度描述对RGB-D图像进行匹配

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161627

A. Rahimi, A. Harati

{"title":"CGSR features: Toward RGB-D image matching using color gradient description of geometrically stable regions","authors":"A. Rahimi, A. Harati","doi":"10.1109/PRIA.2015.7161627","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161627","url":null,"abstract":"Image local feature extraction and description is one of the basic problems in computer vision and robotics. However it has still many challenges. On the other hand, in recent years, after the appearance of novel sensors like Kinect camera, RGB-D images are easily available. So it is necessary to extend feature extraction and description methods to be applicable on RGB-D images. In this paper we propose a new approach to feature extraction and description for RGB-D images: Color Gradient Description of Geometrically Stable Regions. The proposed method, first finds smooth regions with uniform changes in surface normal vectors. The process in this stage is inspired from MSER algorithm. Each region then is normalized to a fixed size circle and is rotated toward its dominant orientation to make description affine, scale, and rotation invariant. Finally, color gradients log-polar histogram of normalized regions is used for description. Experimental results show that CGSR features have good performance in illumination and viewpoint changes and outperform state of the art techniques such as SURF and BRAND in matching precision and robustness.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115137993","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

Facial expression recognition using high order directional derivative local binary patterns 基于高阶方向导数局部二值模式的面部表情识别

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2015-03-11 DOI: 10.1109/PRIA.2015.7161619

S. M. Tabatabaei, Abdollah Chalechale, Shekoofeh Moghimi

{"title":"Facial expression recognition using high order directional derivative local binary patterns","authors":"S. M. Tabatabaei, Abdollah Chalechale, Shekoofeh Moghimi","doi":"10.1109/PRIA.2015.7161619","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161619","url":null,"abstract":"The most expressive manner which human can reveal his emotional states is facial expression. Automatic facial expression recognition is an emerging field of study having extensive applications among which the human-computer interaction (HCI) has received lots of attentions in recent years. The features extracted from facial images, in order to recognize facial expressions, play an essential role in effectiveness of the facial image descriptors. Local binary pattern (LBP) texture descriptors have been known as simple, yet efficient descriptors which are noticeably used for extracting facial patterns from images. Recently, a generalized form of local binary pattern has been introduced which can offer a more precise image description than simple LBP descriptors. Consequently, it would be expected that taking the advantage of using these new LBP texture descriptors will produce more promising results in comparison with use of simple local binary pattern descriptors. In this paper, a novel method has been proposed for image feature extraction using these new image texture descriptors (generalized LBP); then, the obtained results have been compared to the results produced when applying simple LBP descriptors. Furthermore, K-NN and SVM have been used as classifiers in the proposed approach. Finally, a comparison between the proposed method and the existing local binary pattern algorithms for facial expression recognition concludes the superiority of the proposed algorithm over its existing counterparts.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115286126","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: 2