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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

An Efficient Metric for Evaluating the Quality of Color Image Enhancement.

Breast cancer is the second leading cause of death among women according to Cancer Facts and Figures. In order to increase the survival rate of women due to breast cancer, early and accurate detection of breast cancer is very essential. The quality of the image acquired through different breast imaging modalities: mammography, ultrasonography, and magnetic resonance imaging (MRI) have drawbacks which reduces the efficiency of accurate detection. As per the literature survey, the quality of the image acquired through the breast imaging modalities is not optimal for the accurate detection of cancer at the early stage. Digital image enhancement methods have been widely used in radiology in order to enhance the image quality. Considerable research has been undertaken in the development of enhancement of the image to assist radiologists in the identification of breast abnormalities. In order to further improve the efficiency of detection, diagnosis, and treatment of cancer, the quality of the image has to be improved.

Early Detection of Breast Cancer Using Image Processing Techniques

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR) The performance of an optical character recognition depends upon printed quality of the input documents Many OCRs have been designed which correctly identifies the fine printed documents But, very few reported work has been found on the recognition of the degraded documents The efficiency of the OCRs system decreases if the input image is degraded In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed The approach makes use of gradient pattern of an individual character for recognition Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory

Degraded character recognition based on gradient pattern

Carcinoma of lungs is allied to the cancers that are causing the highest number of deaths all over the world. It is very important to improvise the detection methods so that the rate of survival can be increased. In this paper, new algorithm has been proposed to segment the lung regions using Active Contour method. Once the detection of nodules is through and Gray level Co-occurrence Matrix (GLCM) is used to calculate the texture features. HARALICK texture features are calculated and dominant features are extracted. Support Vector Machine (SVM) Classification of the nodules is done using SVM classifier. Satisfactory results have been obtained. Lung CT scan images are taken from LIDC-IDRI database.

Automatic Classification of Lung Nodules into Benign or Malignant Using SVM Classifier

Recognition of historical printed degraded Kannada characters is not solved completely and remains as a challenge to the researchers still. In this paper, a scale for measuring degradation of a character is proposed. Further, the degradation is characterised to high, medium and low based on this scale, and use it to study the efficiency of the character restoration technique designed. A new approach, fit discriminant analysis (FDA) for recognition is proposed and compares its recognition accuracy with the existing techniques support vector machines (SVM) and Fisher linear discriminant (FLD) analysis. Through extensive experimentation it is established that rebuilding of characters improves the recognition accuracy of learning-based approaches SVM, FDA, and FLD significantly. Further, it is established that the proposed approach FDA gives the best recognition accuracy for historical printed degraded documents. It is also proved that training-testing set applying the proposed degradation measure is required for better recognition accuracy.

D. R. Ramesh Babu

Papers

An Efficient Metric for Evaluating the Quality of Color Image Enhancement.

Early Detection of Breast Cancer Using Image Processing Techniques

Degraded character recognition based on gradient pattern

Automatic Classification of Lung Nodules into Benign or Malignant Using SVM Classifier

An efficient approach for handling degradation in character recognition