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Abstrak :
This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based.

Abstrak :
This work proposes a complete sensor-independent visual system that provides robust target motion detection. First, the way sensors obtain images, in terms of resolution distribution and pixel neighbourhood, is studied. This allows a spatial analysis of motion to be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. Two different situations are considered, a fixed camera observing a constant background where objects are moving, and a still camera observing objects in movement within a dynamic background. This distinction lies on developing a surveillance mechanism without the constraint of observing a scene free of foreground elements for several seconds when a reliable initial background model is obtained, as that situation cannot be guaranteed when a robotic system works in an unknown environment. Other problems are also addressed to successfully deal with changes in illumination, and the distinction between foreground and background elements.

Abstrak :
This book constitutes the refereed proceedings of the Chinese Conference on Pattern Recognition, CCPR 2012, held in Beijing, China, in September 2012. The 82 revised full papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on pattern recognition theory, computer vision, biometric recognition, medical imaging, image and video analysis, document analysis, speech processing, and natural language processing and information retrieval.

Abstrak :
This special issue contains five selected papers that were presented at the Workshop on Pattern Recognition for IT Security, held in Darmstadt, Germany, in September 2010, in conjunction with the 32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010. It demonstrates the broad range of security-related topics that utilize graphical data. The contributions explore the security and reliability of biometric data, the power of machine learning methods to differentiate forged images from originals, the effectiveness of modern watermark embedding schemes and the use of information fusion in steganalysis.

Abstrak :
The two-volume set LNCS 7324/7325 constitutes the refereed proceedings of the 9th International Conference on Image and Recognition, ICIAR 2012, held in Aveiro, Portugal, in June 2012. The 107 revised full papers presented were carefully reviewed and selected from 207 submissions. The papers are organized in topical sections on clustering and classification, image processing, image analysis, motion analysis and tracking, shape representation, 3D imaging, applications, biometrics and face recognition, human activity recognition, biomedical image analysis, retinal image analysis, and call detection and modeling.

Abstrak :
The two-volume set LNCS 7324/7325 constitutes the refereed proceedings of the 9th International Conference on Image and Recognition, ICIAR 2012, held in Aveiro, Portugal, in June 2012. The 107 revised full papers presented were carefully reviewed and selected from 207 submissions. The papers are organized in topical sections on clustering and classification, image processing, image analysis, motion analysis and tracking, shape representation, 3D imaging, applications, biometrics and face recognition, human activity recognition, biomedical image analysis, retinal image analysis, and call detection and modeling.

Abstrak :
Conventional video surveillance, where people sit in front of banks of TV monitors may soon become obsolete as key enabling technologies develop. This book details recent developments in machine vision algorithms capable of handling complex visual data acquired by camera systems. It also explores advances in distributed computing and distributed intelligence systems, capable of handling numerous devices and adapting to the evolution of the complex communication networks, thereby inferring a better interpretation of the dynamics of people and objects.

Abstrak :
This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms

Abstrak :
Sistem pengenal objek 3 dimensi secara otomatis sedang banyak diteliti, berkaitan dengan kegunaannya dalam masalah keamanan dan perkantoran degnan tingkat kerahasisaan yang tinggi. Aplikasi dalam sistem multi media juga memerlukan sistem pengenalan 3 dimensi ini. Akan tetapi hingga sekarang, tingkat pengenalan yang tinggi disertai dengan waktu dan biaya komputasi rendah masih sangat sulit untuk dapat dilakukan. Dalam makalah ini, penulis akan menggunakan jaringan percentron lapis jamak yang telah dimodifikasi pada lapis tersembunyi untuk diterapka ndalam sistem pengenal 3 dimensi ini. Modifikasi ini dilakukan dengan menggantikan setiap neuron pada jaringan perceptron lapis jamak dengan sbuat (atau beberapa bua) lingkaran neuron-neuron sehingga membentuk lapis tersembunyi berbentuk silinder. Makalah ini juga akan membahas penggunaan beberapa fungsi kesalahan dan pengaruhnya terhadap tingkat pengenalan sistem. Dengan menggunakan jaringan syarat buatan dengan lapis tersembunyi berbentuk silinder ini, kemampuan sistem ini dapat ditingkatkan hingga mencapai tingkat pengenalan sebesar 97.2%