File Name: advances in computer vision and pattern recognition .zip
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The special issue will focus on the recent advance in learning to solve the combinatorial optimization problem, especially for problems related to pattern recognition. The capability of efficiently solving the challenging combinatorial optimization tasks, which are often NP-hard, is key to success of many business areas, ranging from transportation, aerospace industry, to industrial engineering etc. However, the traditional solvers are often based on rules and specific design based on human knowledge and experience, and the computing is often iterative and serialized on CPU, suffering limitation in scalability, adaptation ability, speed and accuracy.
The outbreak of novel coronavirus COVID has spurred the urgent development of biometric recognition technologies that are able to analyze and identify subjects wearing facial masks, especially in high security applications. Indeed, in this challenging context, the problem of face recognition is often equivalent to periocular recognition involving the iris, pupil, sclera, upper and lower eyelids, eye folds, eye corners, skin texture, fine wrinkles, skin color, skin pores, etc.
Notwithstanding the enormous potential of the ocular traits in both controlled and uncontrolled environments, their possible fusion still needs to be investigated to develop robust face recognition techniques for different types of images acquired using mobile phones, near-infrared and thermal cameras, surveillance cameras, etc. Currently the COVID pandemic poses not only an immense threat to human life across the entire globe, with mounting numbers of fatalities, but it has also had an unprecedented impact on our day-day-today lives and the global economy.
The whole gamut of AI techniques has attracted significant attention as well as a massive investment of time and effort among researchers seeking solutions to the problems posed by COVID For example, machine learning and deep learning have been successfully applied to the detection of COVID using medical imaging and this has become an important tool in fight against the disease.
This issue will be devoted to conformal prediction, a novel machine learning technique that complements predictions of ML algorithms with reliable measures of confidence.
Machine learning and pattern recognition techniques have had a significant impact on the analysis of large-scale datasets in the financial domain. However, to date most of the analysis techniques used have focused on the use of standard vectorial methods and time series data. Recently though, interest has turned to the use of relational and similarity-based representations of financial data. This is largely due to improvements in the maturity of the available methods, including graph embedding, graph kernels and deep graph convolutional networks.
The special issue will focus on the recent advance in modeling and learning to solve the matching problem in pattern recognition. Deep neural networks DNNs have recently achieved outstanding predictive performance, and become an indispensable tool in a wide range of pattern recognition applications, including image classification, object detection, video understanding, document analysis, etc.
While DNN methods give impressively high predictive accuracy, they are often perceived as black-boxes with deep, computationally expensive layers, and have been recently found vulnerable to spoofing with well-designed input samples in many safety critical applications. Video analysis is an important research area in pattern recognition and computer vision. The past decades have witnessed the rapid expansion of the video data generated every day including video surveillance, personal mobile device capture, and webs upload.
It is quite needed for understanding such a large amount of video data. This cognitive skill makes interaction with the environment extremely effortless and provides an evolutionary advantage to humans as a species. In our daily routines, we, humans, not only learn and apply knowledge for visual recognition, we also have intrinsic abilities of transferring knowledge between related visual tasks, i.
In developing machine learning based automated visual recognition algorithms, it is desired to utilize these capabilities to make the algorithms adaptable. We seek to include in the special issue recent successful studies on pattern recognition incorporating ideas and paradigms from the field of neuroscience. We also seek contributions from where neuroscience-inspired algorithms for pattern recognition still fall behind the state-of-the-art in terms of speed and accuracy.
We also cover areas where deeper connections are likely to be fruitful. For example, we would like to highlight how neuroscience driven simulations either hardware or software based suggest new directions, which offer real advances for pattern recognition. Note that we are not interested in papers that focus on the details of such hardware or software, but on how they simulate pattern recognition, based on biological and neuro-scientific principles. Representation learning has always been an important research area in pattern recognition.
A good representation of practical data is critical to achieving satisfactory recognition performance. Intra-data representation focuses on extracting or refining the raw feature of data point itself. Representative methods range from the early-staged hand-crafted feature design e.
Inter-data representation characterizes the relationship between different data points or the structure carried out by the dataset. For example, metric learning, kernel learning and causality reasoning investigate the spatial or temporal relationship among different examples, while subspace learning, manifold learning and clustering discover the underlying structural property inherited by the dataset.
Above analyses reflect that representation learning covers a wide range of research topics related to pattern recognition. On one hand, many new algorithms on representation learning are put forward every year to cater for the needs of processing and understanding various practical data.
On the other hand, massive problems regarding representation learning still remain unsolved, especially for the big data and noisy data. Thereby, the objective of this special issue is to provide a stage for researchers all over the world to publish their latest and original results on representation learning.
Examples include robotic platforms, networked systems that combine computing, sensing, communication, and actuation, amongst others. They exhibit a high-level of awareness beyond primitive actions, in support of persistent and long-term autonomy. They employ a variety of representation and reasoning mechanisms, such as semantic or probabilistic reasoning, decision-making in uncertainties, and intention inference of other entities in their vicinity.
Computer aided cancer detection and diagnosis CAD has made significant strides in the past 10 years, with the result that many successful CAD systems have been developed. However, the accuracy of these systems still requires significant improvement, so that the can meet the needs of real world diagnostic situations..
Recent progress in machine learning offers new prospects for computer aided cancer detection and diagnosis. A major recent development is the massive success resulting from the use of deep learning techniques, which has attracted attention from both the academic research and commercial application communities. Deep learning is the fastest-growing field in machine learning and is widespread uses in cancer detection and diagnosis.
Recent research has demonstrated that deep learning can increase cancer detection accuracy significantly. Thus, deep learning techniques offer the promise not only of more accurate CAD systems for cancer detection and diagnosis, but may also revolutionize their design.
This SI invites contributions which make novel developments to the theory and application of pattern recognition and machine learning to the analysis of human motion and deformable objects. Articulated motion and deformable objects AMDO research focuses on the automatic analysis of complex objects, such as the human body.
The subject is important to different fields, including pattern recognition, computer vision, computer graphics, multimedia applications, and multimodal interfaces. Advances in the automatic analysis of this kind of objects will promote the generation of new technologies and applications in many sectors, including leisure industry gaming, intelligent retrieval of video data, augmented reality, Human Computer Interaction, etc.
Machine learning techniques have played a central role in pattern recognition, and a variety of machine learning methods have been developed for various pattern recognition applications over the past decade. Among these learning methods, distance metric learning has achieved many state-of-the-arts in many pattern recognition applications, which aims to learn an appropriate distance function given some constrains between samples.
To better discover the geometric property of high-dimensional feature spaces and exploit the complementary information of different feature spaces, manifold learning and multi-view learning strategies have also been integrated into distance metric learning to further improve the performance of various distance metric learning methods.
While these methods are helpful to learn the similarity of data such as images, videos, texts, radars, and voices, how to develop task-specific distance metric learning algorithms for different pattern recognition tasks still remains unsolved, especially for big data which are captured in the wild. Moreover, how to develop transferable and nonlinear distance metric learning methods for large-scale pattern recognition systems still requires many efforts.
We are living in a world where we are surrounded by so many intelligent video-capturing devices. These devices capture data about how we live and what we do. For example, thanks to surveillance and action cameras, as well as smart phones and even old-fashioned camcorders, we are able to record videos at an unprecedented scale and pace. There is exceedingly rich information and knowledge embedded in all those videos. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world.
Due to the remarkable successes of deep learning techniques, we are now able to boost video analysis performance significantly and initiate new research directions to analyze video content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. Deep video analytics, or video analytics with deep learning, is becoming an emerging research area in the field of pattern recognition.
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View More on Journal Insights. Call for Papers. Conformal and Probabilistic Prediction with Applications. Aim and Scope Video analysis is an important research area in pattern recognition and computer vision.
Special Issue on Advances in Representation Learning. Pattern Recognition. Advertisers Media Information.
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. The special issue will focus on the recent advance in learning to solve the combinatorial optimization problem, especially for problems related to pattern recognition. The capability of efficiently solving the challenging combinatorial optimization tasks, which are often NP-hard, is key to success of many business areas, ranging from transportation, aerospace industry, to industrial engineering etc. However, the traditional solvers are often based on rules and specific design based on human knowledge and experience, and the computing is often iterative and serialized on CPU, suffering limitation in scalability, adaptation ability, speed and accuracy.
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering , it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring , processing , analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning device.
I am a deputy engeering director of Baidu, managing the company's multimedia department. My team innovates search technologies and products everyday, by making better use of speech, images, videos, and musics. Before April , I led the media analytics department of NEC Labs in northen California, developing intelligent systems involving machine learning, image recognition, multimedia search, data mining, and human-computer interface. Object-centric Spatial Pooling for Image Classification [ pdf ]. Multi-Component Models for Object Detection [ pdf ].
The nature of handwriting in our society has significantly altered over the ages due to the introduction of new technologies such as computers and the World Wide Web. With increases in the amount of signature verification needs, state of the art internet and paper-based automated recognition methods are necessary. Pattern Recognition Technologies and Applications: Recent Advances provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification. This book describes theoretical and applied research work in areas such as handwriting recognition, signature verification, speech recognition, human detection, gender classification, support vector machines for biomedical data and unified support vector machines.
Zhu, K. Sapra, F. Reda, K. Shih, S. Newsam, A.
Available Formats: Hardcover eBook.
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Deep learning is a powerful machine learning framework that has shown outstanding performance in many fields.