AFMA Best R&D Paper for Technology/Information Generation October 29, 2009
Posted by whaldsz in : news, projects, research , add a commentOur 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
Harnessing defocus blur to recover high-resolution information in shape-from-focus technique September 23, 2008
Posted by whaldsz in : research , add a commentTraditional 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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Inferring Segmented Dense Motion Layers Using 5D Tensor Voting September 23, 2008
Posted by whaldsz in : research , add a commentWe 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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Statistical, DCT and vector quantisation-based video codec September 23, 2008
Posted by whaldsz in : research , add a commentThe 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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Discriminant analysis of the two-dimensional Gabor features for face recognition September 23, 2008
Posted by whaldsz in : research , add a commentA new technique called two-dimensional Gabor Fisher discriminant (2DGFD) is derived and implemented for image representation and recognition. In this approach, the Gabor wavelets are used to extract facial features. The principal component analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information from the image rows and a new direct two-dimensional Fisher linear discriminant (direct 2DFLD) method is derived in order to further remove redundant information and form a discriminant representation more suitable for face recognition. The conventional Gabor-based methods transform the Gabor images into a high-dimensional feature vector. However, these methods lead to high computational complexity and memory requirements. Furthermore, it is difficult to analyse such high-dimensional data accurately. The novel 2DGFD method was tested on face recognition using the ORL, Yale and extended Yale databases, where the images vary in illumination, expression, pose and scale. In particular, the 2DGFD method achieves 98.0% face recognition accuracy when using 20%3 feature matrices for each Gabor output on the ORL database and 97.6% recognition accuracy compared with 91.8% and 91.6% for the 2DPCA and 2DFLD method on the extended Yale database. The results show that the proposed 2DGFD method is computationally more efficient than the Gabor Fisher classifier method by approximately 8 times on the ORL, 135 times on the Yale and 1.2801%108 times on the extended Yale B data sets.
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