Magnetic Resonance (MR) images can be considered as multispectral images so that MR imaging can be processed by
multispectral imaging techniques such as maximum likelihood classification. Unfortunately, most multispectral imaging
techniques are not particularly designed for target detection. On the other hand, hyperspectral imaging is primarily
developed to address subpixel detection, mixed pixel classification for which multispectral imaging is generally not
effective. This paper takes advantages of hyperspectral imaging techniques to develop target detection algorithms to find
lesions in MR brain images. Since MR images are collected by only three image sequences, T1, T2 and PD, if a
hyperspectral imaging technique is used to process MR images it suffers from the issue of insufficient dimensionality.
To address this issue, two approaches to nonlinear dimensionality expansion are proposed, nonlinear correlation
expansion and nonlinear band ratio expansion. Once dimensionality is expanded hyperspectral imaging algorithms are
readily applied. The hyperspectral detection algorithm to be investigated for lesion detection in MR brain is the well-known
subpixel target detection algorithm, called Constrained Energy Minimization (CEM). In order to demonstrate the
effectiveness of proposed CEM in lesion detection, synthetic images provided by BrainWeb are used for experiments.
Pesticide residue detection in agriculture crops is a challenging issue and is even more difficult to quantify pesticide residue resident in agriculture produces and fruits. This paper conducts a series of base-line experiments which are particularly designed for three specific pesticides commonly used in Taiwan. The materials used for experiments are single leaves of vegetable produces which are being contaminated by various amount of concentration of pesticides. Two sensors are used to collected data. One is Fourier Transform Infrared (FTIR) spectroscopy. The other is a hyperspectral sensor, called Geophysical and Environmental Research (GER) 2600 spectroradiometer which is a batteryoperated field portable spectroradiometer with full real-time data acquisition from 350 nm to 2500 nm. In order to quantify data with different levels of pesticide residue concentration, several measures for spectral discrimination are developed. Mores specifically, new measures for calculating relative power between two sensors are particularly designed to be able to evaluate effectiveness of each of sensors in quantifying the used pesticide residues. The experimental results show that the GER is a better sensor than FTIR in the sense of pesticide residue quantification.
Block-matching motion estimation plays an important role in real-time video compression and thus has significant impact on searching speed and quality of performance. In order to address these issues, we introduce a highly efficient block motion estimation algorithm, referred to as a predictive cross-hexagon search (PCHS) algorithm, that can considerably reduce the complexity of the Joint Video Team (JVT) encoder. In contrast to many classical fast motion estimation algorithms, PCHS has three desirable features: (1) prediction of a search center, (2) usage of search patterns with different sizes, and (3) early algorithm termination that makes it adaptive and effective. We set four predictor candidates for initial search point options and then increase the accuracy of the predictor. The different-size search patterns, including small cross search patterns, hexagon search patterns, and cross-hexagon search patterns, used in the searching process can better suit more motion types. Due to the high accuracy of the predictor, the proposed algorithm adapts early termination; as the predictor is good enough, the search stops early. Therefore, the PCHS algorithm is suitable for real-time video encoding, as it can speed up the encoder without sacrificing performance compared with other fast algorithms.
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