A modified sparse distributed memory model for extracting clean patterns from noisy inputs (2009)
Meng, Hongying, Appiah, Kofi, Hunter, Andrew, Yue, Shigang, Hobden, Mervyn, Priestley, Nigel, ...
Abstract—The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human long-term memory, with a strong underlying mathematical theory. However, there are problematic...
Meng, Hongying, Yue, Shigang, Hunter, Andrew, Appiah, Kofi, Hobden, Mervyn, Priestley, Nigel, ...
The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the...
A binary self-organizing map and its FPGA implementation (2009)
Appiah, Kofi, Hunter, Andrew, Meng, Hongying, Yue, Shigang, Hobden, Mervyn, Priestley, Nigel, ...
A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state...
Descriptive temporal template features for visual motion recognition (2009)
In this paper, a human action recognition system is proposed. The system is based on new, descriptive `temporal template' features in order to achieve high-speed recognition in real-time, embedded...
Generic object recognition by combining distinct features in machine (2008)
Hongying Meng, David R. Hardoon, John Shawe-taylor, Or Szedmak
learning
Generic object recognition by combining distinct features in machine (2008)
Hongying Meng, David R. Hardoon, John Shawe-taylor, Or Szedmak
learning
Motion history histograms for human action recognition (2008)
Meng, Hongying, Pears, Nick, Freeman, Michael, Bailey, Chris
In this chapter, a compact human action recognition system is presented with a view to applications in security systems, human-computer interaction and intelligent environments. There are three main...
Motion feature combination for human action recognition in video (2008)
Meng, Hongying, Pears, Nick, Bailey, Chris
We study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can...
Meng, Hongying, Freeman, Micheal, Pears, Nick, Bailey, Chris
In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for...
The 2005 PASCAL Visual Object Classes Challenge (2006)
Everingham, Mark, Zisserman, Andrew, Williams, Christopher, Van Gool, Luc, Allan, Moray, Bishop, Chris, ...
The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not...
The 2005 pascal visual object classes challenge (2006)
Mark Everingham, Andrew Zisserman, Luc Van Gool, Moray Allan, Christopher M. Bishop, ...
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes...
The 2005 pascal visual object classes challenge (2006)
Mark Everingham, Andrew Zisserman, Luc Van Gool, Moray Allan, Christopher M. Bishop, ...
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes...
Two view learning: SVM-2K, theory and practice (2006)
Hongying Meng, Sandor Szedmak, David R. Hardoon, John Shawe-taylor
Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When...
Two view learning: SVM-2K, theory and practice (2006)
Hongying Meng, Sandor Szedmak, David R. Hardoon, John Shawe-taylor
Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When...
The 2005 pascal visual object classes challenge (2006)
Mark Everingham, Andrew Zisserman, Luc Van Gool, Moray Allan, Christopher M. Bishop, ...
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes...
Two view learning: SVM-2K, Theory and Practice (2005)
Farquhar, Jason, Hardoon, David, Meng, Hongying, Shawe-Taylor, John, Szedmak, Sandor
Kernel methods make it relatively easy to define complex high-dimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David, Shawe-Taylor, John, Szedmak, Sandor
In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Szedmak, Sandor, Shawe-Taylor, John
In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Shawe-Taylor, John, Szedmak, Sandor
In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Szedmak, Sandor, Shawe-Taylor, John
In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Shawe-Taylor, John, Szedmak, Sandor
In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Support Vector Machine to Synthesise Kernels (2005)
Meng, Hongying, Shawe-Taylor, John, Szedmak, Sandor, Farquhar, Jason
In this paper, we introduce a new method (SVM\_2K) which amalgamates the capabilities of the Support Vector Machine (SVM) and Kernel Canonical Correlation Analysis (KCCA) to give a more sophisticated...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Szedmak, Sandor, Shawe-Taylor, John
In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David R., Shawe-Taylor, John, Szedmak, Sandor
In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Support Vector Machine to synthesise kernels (2004)
Meng, Hongying, Shawe-Taylor, John, Szedmak, Sandor, Farquhar, Jason
Suppose we are given two sets of features from distinct sources about objects that need to be classified. The question we wish to answer is how to combine them into one classification rule, which can...