Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization

Nadipally, Manasa

doi:10.1016/b978-0-12-815553-0.00002-1

Cited by 30 publications

(15 citation statements)

References 29 publications

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“…Firstly, the performance of the face reconstruction algorithm was assessed and compared using the Peak Signal to Noise Ratio (PSNR) [5] and the Structural Similarity Index Measure (SSIM) [6], as described in Subsection 4.2. While these metrics are widely recognized for evaluating the effectiveness of image reconstruction algorithms, they do not consider the potential impact on biometric recognition capabilities.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Research in this area ranges from sophisticated algorithms for reconstructing facial features in damaged or occluded images to advanced techniques for improving image quality. These solutions approach the problem from an image quality perspective using Peak Signal to Noise Ratio [5] and Structural Similarity Index [6] metrics, effectively generating higherquality images. However, this approach may not be sufficient for biometrics, which we will address later in this study.…”

Section: Related Workmentioning

confidence: 99%

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Reconstruction and enhancement techniques for overcoming occlusion in face recognition

Pleško,

Goldmann,

Malinka

2024

Preprint

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Face occlusions on CCTV cameras obscure important key facial features, preventing face recognition (FR) systems from recognizing people. This work mainly focuses on reconstructing these missing facial parts using Generative Adversarial Neural Networks (GANs) to improve FR accuracy while maintaining a low False Acceptance Rate (FAR). In addition, we are trying to improve the generated images further by using different image enhancement methods to test whether they can be used to improve the FR accuracy. To test the results, we perform experiments using state-of-the-art FR methods such as QMagFace and ArcFace to see whether image reconstruction and image enhancement help to improve FR accuracy.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reconstruction and enhancement techniques for overcoming occlusion in face recognition

Pleško,

Goldmann,

Malinka

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Mean Squared Error (MSE) is performed for original images and the encrypted and decrypted images 107 . In general, these metrics have the ground-truth reference image in evaluating the performance of the image fusion algorithms.…”

Section: Mean Squared Errormentioning

confidence: 99%

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Das

Chandra

2022

ECS Trans.

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Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.

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“…We also evaluate our deblurring network without fine-tuning on the reblurring network ("ours"). We use PSNR [36] and SSIM [53] as evaluation metrics. All the methods are trained on the GoPro dataset following the same strategy.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Huo¹,

Masoumzadeh²,

Yang³

2021

Preprint

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Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high; 2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets.

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Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization

Cited by 30 publications

References 29 publications

Reconstruction and enhancement techniques for overcoming occlusion in face recognition

Reconstruction and enhancement techniques for overcoming occlusion in face recognition

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

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