Image analysis and machine learning for detecting malaria

Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

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Handbook Of Image And Video Processing

Different applied median filter in salt and pepper noise

We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using such imbalance data, thus achieving inferior results. The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy. Specifically, we design a novel dense regression network (DRN) to regress the distances from each frame to the starting (ending) frame of the video segment described by the query. We also propose a simple but effective IoU regression head module to explicitly consider the localization quality of the grounding results (i.e., the IoU between the predicted location and the ground truth). Experimental results show that our approach significantly outperforms state-of-the-arts on three datasets (i.e., Charades-STA, ActivityNet-Captions, and TACoS).

Dense Regression Network for Video Grounding

In this work, a vision-based approach is used to build a dynamic hand gesture recognition system. Various challenges such as complicated background, change in illumination and occlusion make the detection and tracking of hand difficult in any vision-based approaches. To overcome such challenges, a hand detection technique is developed by combining three-frame differencing and skin filtering. The three-frame differencing is performed for both colored and grayscale frames. The hand is then tracked using modified Kanade---Lucas---Tomasi feature tracker where the features were selected using the compact criteria. Velocity and orientation information were added to remove the redundant feature points. Finally, color cue information is used to locate the final hand region in the tracked region. During the feature extraction, 44 features were selected from the existing literatures. Using all the features could lead to overfitting, information redundancy and dimension disaster. Thus, a system with optimal features was selected using analysis of variance combined with incremental feature selection. These selected features were then fed as an input to the ANN, SVM and kNN model. These individual classifiers were combined to produce classifier fusion model. Fivefold cross-validation has been used to evaluate the performance of the proposed model. Based on the experimental results, it may be concluded that classifier fusion provides satisfactory results (92.23 %) compared to other individual classifiers. One-way analysis of variance test, Friedman's test and Kruskal---Wallis test have also been conducted to validate the statistical significance of the results.

Dynamic hand gesture recognition using vision-based approach for human---computer interaction

Impulse noise removal using SVM classification based fuzzy filter from gray scale images

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (~1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2016.0320

Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images

In this paper, a robust image watermarking system in lifting wavelet transform domain using different sub-bands has been proposed. SVM classifier is used during watermark extraction to obtain improved robustness under diverse attack conditions. In this work, a detailed analysis of imperceptibility and robustness performance with the use of different sub-bands has been presented. The performance on different sub-band has been analyzed so as to maximize the robustness against different attacks keeping imperceptibility at adequate level. Robustness is observed against various attacks such as noising attacks, denoising attacks, image processing attacks, lossy compression attacks and geometric attacks. It is seen that high-frequency sub-band provides better invisibility, whereas variation of robustness performance on different sub-bands depend on the type of attacks. It is observed from the performance analysis that all the attacks do not have exactly same effect on the frequency content of the image. For instance, noising attack affects every frequency component of the image almost equally, whereas the embedding in high-frequency band makes the system fragile to lossy compression attack. The algorithm is tested on a large image database to observe the variation in the performance of the system. Comparative analysis suggests that the proposed sub-band provides improved performance over some benchmark methods in most of the cases.

SVM-based robust image watermarking technique in LWT domain using different sub-bands

This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ~3dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods.

Amarjit Roy

Papers

Dynamic hand gesture recognition using vision-based approach for human---computer interaction

Impulse noise removal using SVM classification based fuzzy filter from gray scale images

Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images

SVM-based robust image watermarking technique in LWT domain using different sub-bands

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images