241,323 research outputs found
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Facial Expression Recognition Using SVM Classifier
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial  expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem
in computer vision and pattern recognition, and underpins a diverse set of
real-world applications. The task of FGIA targets analyzing visual objects from
subordinate categories, e.g., species of birds or models of cars. The small
inter-class and large intra-class variation inherent to fine-grained image
analysis makes it a challenging problem. Capitalizing on advances in deep
learning, in recent years we have witnessed remarkable progress in deep
learning powered FGIA. In this paper we present a systematic survey of these
advances, where we attempt to re-define and broaden the field of FGIA by
consolidating two fundamental fine-grained research areas -- fine-grained image
recognition and fine-grained image retrieval. In addition, we also review other
key issues of FGIA, such as publicly available benchmark datasets and related
domain-specific applications. We conclude by highlighting several research
directions and open problems which need further exploration from the community.Comment: Accepted by IEEE TPAM
Face Recognition: Issues, Methods and Alternative Applications
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition
Wndchrm – an open source utility for biological image analysis
<p>Abstract</p> <p>Background</p> <p>Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.</p> <p>Methods</p> <p><it>Wndchrm </it>is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement.</p> <p>Results</p> <p><it>Wndchrm </it>has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.</p> <p>Conclusion</p> <p>We suggest that <it>wndchrm </it>can be effectively used for a wide range of biological image analysis tasks. Using <it>wndchrm </it>can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.</p
Comparative study of human age estimation based on hand-crafted and deep face features
In the past few years, human facial age estimation has drawn a lot of attention
in the computer vision and pattern recognition communities because of its important
applications in age-based image retrieval, security control and surveillance, biomet-
rics, human-computer interaction (HCI) and social robotics. In connection with these
investigations, estimating the age of a person from the numerical analysis of his/her
face image is a relatively new topic. Also, in problems such as Image Classification
the Deep Neural Networks have given the best results in some areas including age
estimation.
In this work we use three hand-crafted features as well as five deep features
that can be obtained from pre-trained deep convolutional neural networks. We do a
comparative study of the obtained age estimation results with these features
An Analysis of Facial Expression Recognition Techniques
In present era of technology , we need applications which could be easy to use and are user-friendly , that even people with specific disabilities use them easily. Facial Expression Recognition has vital role and challenges in communities of computer vision, pattern recognition which provide much more attention due to potential application in many areas such as human machine interaction, surveillance , robotics , driver safety, non- verbal communication, entertainment, health- care and psychology study. Facial Expression Recognition has major importance ration in face recognition for significant image applications understanding and analysis. There are many algorithms have been implemented on different static (uniform background, identical poses, similar illuminations ) and dynamic (position variation, partial occlusion orientation, varying lighting )conditions. In general way face expression recognition consist of three main steps first is face detection then feature Extraction and at last classification. In this survey paper we discussed different types of facial expression recognition techniques and various methods which is used by them and their performance measures
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