Thank you very much for downloading principles of fluorescence spectroscopy. As you may know, people have look hundreds times for their favorite novels like this principles of fluorescence spectroscopy, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their desktop computer. principles of fluorescence spectroscopy is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the principles of fluorescence spectroscopy is universally compatible with any devices to read.

Principles Of Fluorescence Spectroscopy

International Journal of Computer and Applications

Traditional and recent approaches in background modeling for foreground detection: An overview

Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm’s robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui .

Low-Rank Embedding for Robust Image Feature Extraction

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.

SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image spar- sity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared to full k-space acquisition, which results in reduced MRI data acquisition time. The images resulting from MRA are sparse in native representation, hence yielding themselves well to CS. Recently our group has proposed a novel CS method called Region of Interest Compressed Sensing (ROICS) as a part of Region of Interest (ROI) weighted CS. This work aims at the implementation of ROICS for the first time on MRA data to reduce MR data acquisition time. It has been demonstrated qualitatively and quantitatively that ROICS outperforms CS at higher acceleration factors. ROICS technique has been applied to 3D angiograms of the brain data acquired at 1.5T. It helps to reduce the MRA data acquisition time and improves the visualization of arteries. ROICS technique has been applied on 4 brain angiogram data sets at different acceleration factors from 2x to 10x. Reconstructed images show ROICS technique performs better than conventional CS technique and is quantified by the comparative Signal to Noise Ratio (SNR) in the ROI.

Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography

Mammography is an effective method for breast cancer detection and breast tumor analysis. In mammography, low dose x-ray is used for imaging, due to which the images are poor in contrast and are contaminated by noise. Hence it is difficult for the radiologist to screen the mammograms for diagnostic signs such as micro calcifications and masses. This ensures the need for image enhancement to aid radiologist. In this paper we present a different algorithm for enhancement of digital mammographic images. The proposed methodology uses mathematical morphology for contrast enhancement and wavelet for denoising. The main contribution of this paper is in differentiating the edge pixels from noise. A quantitative measure of Contrast Improvement Index (CII) and Edge Preservation Index (EPI) are used to evaluate the performance of the algorithm. The algorithm has been tested on a large number of images from standard dataset, comparing the results with the state-of-the- art. By both the analytical indices and ROC analysis, the proposed algorithm shows promising results in early detection of breast cancer and diagnosis.

Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet

LoPP: Locality Preserving Projections for Moving Object Detection

Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing.

Vison based tracking in aerial images has its own significance in the areas of both civil and defense applications.  A novel algorithm called aerial tracking learning detection which works on the basis of the popular tracking learning detection algorithm to effectively track single and multiple objects in aerial images is proposed in this study. Tracking learning detection (TLD) considers both appearance and motion features for tracking. It can handle occlusion to certain extent, and can work well on long duration video sequences. However, when objects are tracked in aerial images taken from platforms like unmanned air vehicle, the problems of frequent pose change, scale and illumination variations arise adding to low resolution, noise and jitter introduced by motion of the camera.  The proposed algorithm incorporates compensation for the camera movement, algorithmic modifications in combining appearance and motion cues for detection and tracking of multiple objects and enhancements in the form of inter object distance measure for improved performance of the tracker when there are many identical objects in proximity. This algorithm has been tested on a large number of aerial sequences including benchmark videos, TLD dataset and many classified unmanned air vehicle sequences and has shown better performance in comparison to TLD.

D. R. Ramesh Babu

Papers

Application of Region of Interest Compressed Sensing to accelerate magnetic resonance angiography

Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet

LoPP: Locality Preserving Projections for Moving Object Detection

Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing.

Multi-object Tracking in Aerial Image Sequences using Aerial Tracking Learning and Detection Algorithm