- Property Tax Assessment System (PTAS?) January 27, 2012
- Business Permit and Licensing System January 22, 2012
- Insect Identification December 18, 2011
- Legislative Information Management and Tracking System September 6, 2011
- People Counting - Patent finally approved! July 20, 2011
research
July 2, 2011 | 
Author During the 32nd In-house Research and Development Review last May10-11, 2011 held at Munoz City, Philippines. Our on-going research on Milled Rice Computer Vision garnered an award.
For more information, follow this link - http://www.philmech.gov.ph/?page=news&action=details&code01=NP11050002
Posted in projects, research, technology | 
Tags: Milled Rice, philmech, project, research | 
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Our study, “Development of a Computer Vision System for Milled Rice Quality Analysis” won the AFMA Best R&D Paper for Technology/Information Generation – Agriculture category.
The event was held during the 21st National Research Symposium last October 9, 2009 at the Fernando Lopez Hall of the Bureau of Soils and Water Management (BSWM) Bldg., Visayas Avenue, Diliman, Quezon City.
Check the full article @ http://www.bar.gov.ph/news/21stnrswinners.asp
Traditional shape-from-focus (SFF) uses focus as the singular cue to derive the shape profile of a 3D object from a sequence of images. However, the stack of low-resolution (LR) observations is space-variantly blurred because of the finite depth of field of the camera. The authors propose to exploit the defocus information in the stack of LR images to obtain a super-resolved image as well as a high-resolution (HR) depth map of the underlying 3D object. Appropriate observation models are used to describe the image formation process in SFF. Local spatial dependencies of the intensities of pixels and their depth values are accounted for by modelling the HR image and the HR structure as independent Markov random fields. Taking as input the LR images from the stack and the LR depth map, the authors first obtain the super-resolved image of the 3D specimen and use it subsequently to reconstruct a HR depth profile of the object.
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We present a novel local spatiotemporal approach to produce motion segmentation and dense temporal trajectories from an image sequence. A common representation of image sequences is a 3D spatiotemporal volume, (x,y,t), and its corresponding mathematical formalism is the fiber bundle. However, directly enforcing the spatiotemporal smoothness constraint is difficult in the fiber bundle representation. Thus, we convert the representation into a new 5D space (x,y,t,vx,vy) with an additional velocity domain, where each moving object produces a separate 3D smooth layer. The smoothness constraint is now enforced by extracting 3D layers using the tensor voting framework in a single step that solves both correspondence and segmentation simultaneously. Motion segmentation is achieved by identifying those layers, and the dense temporal trajectories are obtained by converting the layers back into the fiber bundle representation. We proceed to address three applications (tracking, mosaic, and 3D reconstruction) that are hard to solve from the video stream directly because of the segmentation and dense matching steps, but become straightforward with our framework. The approach does not make restrictive assumptions about the observed scene or camera motion and is therefore generally applicable. We present results on a number of data sets.
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The authors present a novel hybrid statistical, DCT and vector quantisation-based video-coding technique. In intra mode of operation, an input frame is divided into a number of non-overlapping pixel blocks. A discrete cosine transform then converts the coefficients in each block into the frequency domain. Coefficients with the same frequency index at different blocks are put together generating a number of matrices, where each matrix contains the coefficients of a particular frequency index. The matrix, which contains the DC coefficients, is losslessly coded. Matrices containing high frequency coefficients are coded using a novel statistical encoder. In inter mode of operation, overlapped block motion estimation / compensation is employed to exploit temporal redundancy between successive frames and generates a displaced frame difference (DFD) for each inter-frame. A wavelet transform then decomposes the DFD-frame into its frequency subbands. Coefficients in the detail subbands are vector quantised while coefficients in the baseband are losslessly coded. To evaluate the performance of the codec, the proposed codec and the adaptive subband vector quantisation (ASVQ) video codec, which has been shown to outperform H.263 at all bitrates, were applied to a number of test sequences. Results indicate that the proposed codec outperforms the ASVQ video codec subjectively and objectively at all bitrates.
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