In this paper, we propose a visual tracking approach based on 'bag of features' (BoF) algorithm. First we use incremental PCA visual tracking (IVT) in the first few frames and collect image patches randomly sa...
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In this paper, we propose a visual tracking approach based on 'bag of features' (BoF) algorithm. First we use incremental PCA visual tracking (IVT) in the first few frames and collect image patches randomly sampled within the tracked object region in each frame for constructing the codebook;the tracked object then can be converted to a bag. Second we construct two codebooks using color (RGB) features and localbinarypattern (LBP) features instead of only one codebook in traditional BoF, thereby extracting more informative details. We also devise an updating mechanism to deal with pose and appearance changes of objects. In the tracking process, a constant number of candidates are generated by sampling technique in each frame. Image patches are then randomly sampled and candidates are represented as bags by codebooks. Thus, we can compute patch similarity of a candidate with the codewords and bag similarity with trained bags. The actual object is then located by finding the maximal combined similarity of patches and bags. Experiments demonstrate that our approach is robust in handling occlusion, scaling and rotation.
Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular dise...
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Automatic extraction of retinal blood vessels plays an important role in the diagnosis of many retinal diseases and also for diagnosing several complicated diseases such as stroke, hypertension and cardiovascular diseases. Due to the complex nature of retinal vessel network, the manual segmentation of vessels is a tedious task which also requires high training and skills. This study presents a new method for blood vessel segmentation in colour retinal images using supervised approach. Initially, a set of core features including Gabor filter responses, Frangi's vesselness measure (1D), local binary pattern feature (1D), Hu moment invariants (7D) and grey-level co-occurrence matrix features (3D) are considered. The neural network is trained with the different subsets of core features and it is found that the model with 13D features excluding the Hu moment invariants results in better performance. This model is used for evaluation. The proposed supervised segmentation approach is tested on publicly available structured analysis of the retina, digital retinal images for vessel extraction and CHASE_DB1 databases which contain manually labelled images. The performance of the proposed algorithm is evaluated on the basis of accuracy, sensitivity, specificity and area under the curve. The proposed technique achieves high mean accuracy and sensitivity while it is compared with the several previously proposed algorithms.
Introduction: In recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial resear...
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Introduction: In recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial research is focusing on detecting antinuclear, anti-neutrophil, and anti-double-stranded (anti-dsDNA) antibodies. Within this framework, we present a CAD system for automatic analysis of dsDNA antibody images using a multi-step classification approach. The final classification of a well is based on the classification of all its images, and each image is classified on the basis of the labeling of its cells. Methods: We populated a database of 342 images-74 positive (21.6%) and 268 negative (78.4%)-belonging to 63 consecutive sera: 15 positive (23.8%) and 48 negative (76.2%). We assessed system performance by using k-fold cross-validation. Furthermore, we successfully validated the recognition system on 83 consecutive sera, collected by using different equipment in a referral center, counting 279 images: 92 positive (33.0%) and 187 negative (67.0%). Results: With respect to well classification, the system correctly classified 98.4% of wells (62 out of 63). Integrating information from multiple images of the same wells recovers the possible misclassifications that occurred at the previous steps (cell and image classification). This system, validated in a clinical routine fashion, provides recognition accuracy equal to 100%. Conclusion: The data obtained show that automation is a viable alternative for Crithidia luciliae immunofluorescence test analysis.
Human face based gender recognition is a challenging issue in image processing and machine vision domain. In this paper we proposed an approach for gender recognition using combination of statistical features and Loca...
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ISBN:
(纸本)9781479980215
Human face based gender recognition is a challenging issue in image processing and machine vision domain. In this paper we proposed an approach for gender recognition using combination of statistical features and localbinarypattern (LBP). The optimal block size and statistical features set are determined by sequential forward floating selection (SFFS) algorithm for gender recognition improvement. The assessment and comparison with other methods have been carried out using Iranian facial image dataset. The proposed approach can carry out the classification more accurately. The rates of true classification using Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) classifiers are 99.41% and 99.31 respectively.
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