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IMPROVED STATISTICAL FEATURES FOR CURSIVE CHARACTER recognition

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第9期7卷 5211-5224页

作者： Saba, Tanzila Rehman, Amjad Sulong, Ghazali Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Comp Graph & Multimedia Skudai Johor Malaysia

This paper presents an improved feature extraction technique for the cursive characters recognition. This technique can be applied in the perspective of handwritten word recognition system based on segmentation. The bases of fused statistical features extraction technique are improved projection profile and transition features. To extend this principal, a technique is integrated with the projection profile information to detect shifts of background and foreground pixels in the image of a character. A classifier based on neural network is used to test the improved fused features and comparison is done with the projection profile (PP) and transition feature (TF) extraction techniques. By using standard dataset, PP and TF techniques altogether show best performance with fused features having new enhancements and the best results in the literature are compared promisingly with this technique. The characters that are taken from the CEDAR dataset show 91.38% recognition accuracy.

关键词： OCR pattern recognition computer vision Machine learning Feature extraction

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Feature fusion based multilingual fingerspelling recognition

Feature fusion based multilingual fingerspelling recognition

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IEEE Signal Processing and Communications Applications (SIU)

作者： Ahmet Alp Kindiroğlu Hülya Yalçın Lale Akarun Bilgisayar Mühendisliği Bölümü Boğaziçi Üniversitesi Turkey

In this paper, we present a fingerspelling recognition module that has been designed to function in a smart system that is intended to act as a communication medium between people with hearing and visual disabilities. The method described is a computer vision based, close to real-time, automatic skin color based model hand gesture recognition module. We analyze and compare the recognition performance of appearance based hand descriptors on a self collected dataset. The dataset contains isolated videos of 88 different signs of the Czech, Turkish and Russian Sign Alphabets from 5 different signers with a total training and test length of 4 hours. On our test sets, we have achieved signer dependent and signer independent fingerspelling recognition rates of %82 and %42, respectively.

关键词： Conferences pattern recognition Videos Handicapped aids Visualization Signal processing computer vision

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Is there a connection between face symmetry and face recognition?

Is there a connection between face symmetry and face recogni...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Josh Harguess J. K. Aggarwal Computer & Vision Research Center / Department of ECE University of Texas Austin USA

Recent research in the area of automatic machine recognition of human faces has shown that there may be an advantage in utilizing face symmetry to improve recognition accuracy. While promising, this work has led to several open questions. What is a good feature description or score of the symmetry of the face? Is there a statistical significance between face symmetry and face recognition? We present new symmetry scores of the face and use the scores to compare the symmetry in several subgroups of a face database. A 3D face database is used to remove the effects of illumination which should improve the reliability of the symmetry score. We find a significant difference in face symmetry between the men and women subjects in the database. The database is then partitioned into most symmetric and least symmetric subjects based on the symmetry scores. The average-half-face is utilized in our face recognition experiments to take into account the symmetry of the face. Face recognition with eigenfaces using the average-half-face is significantly higher than using the full face in all subgroups regardless of symmetry score. However, face recognition using the full face does depend on the symmetry score and generally favors the least symmetric subjects.

关键词： Face Face recognition Databases Three dimensional displays Accuracy Histograms Statistical analysis

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Harmful Algal Bloom recognition based on microscopic images

Harmful Algal Bloom recognition based on microscopic images

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： Haiyong Zheng Guangrong Ji Bing Zheng Hao Zhang T. Aaron Gulliver Department of Electronic Engineering Ocean University of China Qingdao China Department of Electrical & Computer Engineering University of Victoria Victoria BC Canada

Using a microscope to recognize a Harmful Algal Bloom (HAB) requires specialized knowledge and is time-consuming. This method is, however, widely employed by HAB taxonomists. To provide a better solution, a HAB recognition system has been developed which is based on image analysis. This paper presents the architecture of this system, as well as sone performance results. The key component is the libiplhab library based on computer vision libraries (such as OpenCV and LIBSVM), which contain image processing and pattern recognition algorithms suitable for HAB microscope images.

关键词： Microscopy Libraries Feature extraction Graphical user interfaces pattern recognition Image recognition

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Low-Cost Hand Vein pattern recognition

Low-Cost Hand Vein Pattern Recognition

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2011 World Congress on Engineering and Technology(CET 2011)

作者： Marion Distler Sebastian Jensen Niels G.Myrtue Claire Petitimbert Kamal Nasrollahi Thomas B.Moeslund Laboratory of Computer Vision and Media Technology Aalborg UniversityAalborgDenmark

Identification based on hand vein pattern is an interesting branch of biometric recognition that is enjoying increasing attention among the researchers in the recent ***,despite the fact the most of the developed commercial systems for this purpose are expensive,their false acceptance ratios are not ***,using them for identification purposes can be crucial if they mistakenly accept an imposter (even with a very low probability).The current work proposes a very low-cost hand vein pattern recognition system using a simple modified *** system introduces a blob removal algorithm for enhancing the results of the segmentation and uses a modified version of Hausdorff distance for feature matching for the recognition *** results show that the system can achieve a zero false acceptance ratio while keeping the true acceptance rate at an acceptable level.

关键词： pattern recognition Hand Vein Biometrics Infrared Imaging

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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook

Scaling up biologically-inspired computer vision: A case stu...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Nicolas Pinto Zak Stone Todd Zickler David Cox McGovern Institute of Brain Research MIT Cambridge MA USA Rowland Institute at Harvard Harvard University Cambridge MA USA School of Engineering and Applied Sciences Harvard University Cambridge MA USA

Biological visual systems are currently unrivaled by artificial systems in their ability to recognize faces and objects in highly variable and cluttered real-world environments. Biologically-inspired computer vision systems seek to capture key aspects of the computational architecture of the brain, and such approaches have proven successful across a range of standard object and face recognition tasks (e.g. [23, 8, 9, 18]). Here, we explore the effectiveness of these algorithms on a large-scale unconstrained real-world face recognition problem based on images taken from the Face-book social networking website. In particular, we use a family of biologically-inspired models derived from a high-throughput feature search paradigm [19, 15] to tackle a face identification task with up to one hundred individuals (a number that approaches the reasonable size of real-world social networks). We show that these models yield high levels of face-identification performance even when large numbers of individuals are considered; this performance increases steadily as more examples are used, and the models outperform a state-of-the-art commercial face recognition system. Finally, we discuss current limitations and future opportunities associated with datasets such as these, and we argue that careful creation of large sets is an important future direction.

关键词： Face Facebook Face recognition Biological system modeling Brain models

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4D Photogeometric face recognition with time-of-flight sensors

4D Photogeometric face recognition with time-of-flight senso...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Sebastian Bauer Jakob Wasza Kerstin Müller Joachim Hornegger Pattern Recognition Lab Department of Computer Science University Erlangen-Nuremberg Germany

Methods for 2D/3D face recognition typically combine results obtained independently from the 2D and 3D data, respectively. There has not been much emphasis on data fusion at an early stage, even though it is at least potentially more powerful to exploit possible synergies between the two modalities. In this paper, we propose photogeometric features that interpret both the photometric texture and geometric shape information of 2D manifolds in a consistent manner. The 4D features encode the spatial distribution of gradients that are derived generically for any scalar field on arbitrary organized surface meshes. We apply the descriptor for biometric face recognition with a time-of-flight sensor. The method consists of three stages: (i) facial landmark localization with a HOG/SVM sliding window framework, (ii) extraction of photogeometric feature descriptors from time-of-flight data, using the inherent grayscale intensity information of the sensor as the 2D manifold's scalar field, (iii) probe matching against the gallery. recognition based on the photogeometric features achieved 97.5% rank-1 identification rate on a comprehensive time-of-flight dataset (26 subjects, 364 facial images).

关键词： Face recognition Three dimensional displays Sensors Face Gray-scale Support vector machines Probes

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Matching point pattern using a munkres genetic algorithm

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Journal of Convergence Information Technology 2011年第12期6卷 376-383页

作者： Xing, Chong Wei, Wei Wu, Jianan Zhang, Chulei Zhou, You College of Computer Science and Technology Jilin University China Guanghua College of Changchun University China College of Computer Science and Technology Changchun University China College of Humanities and Sciences Northeast Normal University Changchun China

Point pattern Matching is a very important and significant aspect of many fields in computer vision and pattern recognition. Using point pattern matching technique could find an optimal transformation for one point pattern to the other under a distance measurement. Genetic Algorithm combines with Munkres algorithm is proposed for solving Point pattern Matching problem. The proposed algorithm can be used to perform reliable matching between two different views of an object or scene. A new formula is proposed and used as a reasonable fitness function. The advantages of Munkres and Genetic algorithm are used and combined together in this paper. The experimental results show that the new algorithm is very effective.

关键词： Genetic algorithms

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Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion

Textured 3D face recognition using biological vision-based f...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Di Huang Wael Ben Soltana Mohsen Ardabilian Yunhong Wang Liming Chen Ecole Centrale Lyon LIRIS UMR 5205CNRS Université de Lyon Ecully France School of Computer Science and Engineering Beihang University Beijing China

This paper proposes a novel biological vision-based facial description, namely Perceived Facial Images (PFIs), aiming to highlight intra-class and inter-class variations of both facial range and texture images for textured 3D face recognition. These generated PFIs simulate the response of complex neurons to gradient information within a certain neighborhood and possess the properties of being highly distinctive and robust to affine illumination and geometric transformation. Based on such an intermediate facial representation, SIFT-based matching is further carried out to calculate similarity scores between a given probe face and the gallery ones. Because the facial description generates a PFI for each quantized gradient orientation of range and texture faces, we then propose a score level fusion strategy which optimizes the weights using a genetic algorithm in a learning step. Evaluated on the entire FRGC v2.0 database, the rank-one recognition rate using only 3D or 2D modality is 95.5% and 95.9%, respectively; while fusing both modalities, i.e. range and texture-based PFIs, the final accuracy is 98.0%, demonstrating the effectiveness of the proposed biological vision-based facial description and the optimized weighted sum fusion.

关键词： Face Three dimensional displays Face recognition Neurons Lighting Solid modeling

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Fast, recurrent, attentional modulation improves saliency representation and scene recognition

Fast, recurrent, attentional modulation improves saliency re...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xun Shi Neil D. B. Bruce John K. Tsotsos Department of Computer Science & Engineering and Centre of Vision Research York University Toronto ONT Canada

The human brain uses visual attention to facilitate object recognition. Traditional theories and models envision this attentional mechanism either in a pure feedforward fashion for selection of regions of interest or in a top-down task-priming fashion. To these well-known attentional mechanisms, we add here an additional novel one. The approach is inspired by studies of biological vision pertaining to the asynchronous timing of feedforward signals among different early visual areas and the role of recurrent connections from short latency areas to facilitate object recognition. It is suggested that recurrence elicited from these short latency dorsal areas improves the slower feedforward processing in the early ventral areas. We therefore propose a computational model that simulates this process. To test this model, we add such fast recurrent processes to a well-known model of feedforward saliency, AIM and show that those recurrent signals can modulate the output of AIM to improve its utility in recognition by later stages. We further add the proposed model to a back-propagation neural network for the task of scene recognition. Experimental results on standard video sequences show that the discriminating power of the modulated representation is significantly improved, and the implementation consistently outperforms existing work including a benchmark system that does not include recurrent refinement.

关键词： Computational modeling Visualization Feedforward neural networks Neurons Modulation Object recognition Spatiotemporal phenomena

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