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Full-Text Articles in Physical Sciences and Mathematics
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
On The Combination Of Local Texture And Global Structure For Food Classification, Zhimin Zong, Duc Thanh Nguyen, Philip O. Ogunbona, Wanqing Li
Faculty of Informatics - Papers (Archive)
This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the …
A Human Gait Classification Method Based On Radar Doppler Spectrograms, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. Amin
A Human Gait Classification Method Based On Radar Doppler Spectrograms, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. Amin
Faculty of Informatics - Papers (Archive)
An image classification technique, which has recently been introduced for visual pattern recognition, is successfully applied for human gait classification based on radar Doppler signatures depicted in the time-frequency domain. The proposed method has three processing stages. The first two stages are designed to extract Doppler features that can effectively characterize humanmotion based on the nature of arm swings, and the third stage performs classification. Three types of arm motion are considered: free-arm swings, one-arm confined swings, and no-arm swings. The last two arm motions can be indicative of a human carrying objects or a person in stressed situations. The …
Automatic Classification Of Gpr Signals, W Shao, A Bouzerdoum, S L. Phung, L Su, B Indraratna, C Rujikiatkamjorn
Automatic Classification Of Gpr Signals, W Shao, A Bouzerdoum, S L. Phung, L Su, B Indraratna, C Rujikiatkamjorn
Faculty of Informatics - Papers (Archive)
Ground penetrating radar has been widely used in many areas. However, the processing and interpretation of acquired signals remains a challenging task since it requires experienced users to manage the whole operations. In this paper, we propose an automatic classification system to categorise GPR signals based on magnitude spectrum amplitudes and support vector machines. The system is tested on a real-world GPR data set. The experimental results show that our system can correctly distinguish ground penetrating radar signals reflected by different materials.
Automatic Human Motion Classification From Doppler Spectrograms, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. Amin
Automatic Human Motion Classification From Doppler Spectrograms, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Moeness G. Amin
Faculty of Informatics - Papers (Archive)
No abstract provided.