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FResFormer: Leukemia Detection Using Fusion-Enabled CNN and Attention 8th

FResFormer: Leukemia Detection Using Fusion-Enabled CNN and ...

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8th international conference on computer vision and image processing (cvip)

作者： Perumal, Murukessan Goutham, E. Varshini, U. Shivani Sri Srinivas, M. Subramanyam, R. B. V. Natl Inst Technol Warangal Telangana India Inst Aeronaut Engn Hyderabad Telangana India

ISBN: (纸本)9783031581731;9783031581748

In this paper, the binary classification of normal and abnormal (malignant) cells from the microscopic images is done. It is quite challenging due to the appearance of both cells morphologically similar. In particular, manual identification of malignant and benign cells from the microscopic images in early stages is difficult because of the similar resemblance of both cells in their appearance. This early diagnosis process requires advanced techniques like flow cytometry that are currently used and more expensive and, therefore, are not accessible in all places. Additionally, a medical expert is also required. Therefore, by using automated diagnostic tools, we can perform better diagnoses with low-cost microscopic image data. In this paper, we propose a classification of normal and malignant cells in the lymphocytes using a fusion-enabled CNN and Attention-based neural network, which we named Fusion-based Residual Transformer (FRESFORMER) architecture. Our proposed model tops the performance on the benchmark ISBI 2019 challenge dataset. The proposed model achieves an F1-Score of 84.89.

关键词： Acute Lymphoblastic Leukemia Chronic Leukemia Deep Learning Transformers computer vision

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Free-Throw Prediction in Basketball Sport Using Object Detection and computer vision 8th

Free-Throw Prediction in Basketball Sport Using Object Detec...

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8th international conference on computer vision and image processing (cvip)

作者： Gowda, Mayur S. Shindhe, S. Dhruv Omkar, S. N. Ramaiah Inst Technol Ctr Imaging Technol Bangalore Karnataka India Indian Inst Sci ARTPARK Bangalore Karnataka India Indian Inst Sci Dept Aerosp Bangalore Karnataka India

ISBN: (纸本)9783031581731;9783031581748

The most important things that must be taken care of in a basketball game include teamwork, perfect team coordination, and execution of the skills of the players at the right point of time, without which it will be difficult to win a game. So, a player must realize these things while he is playing, but in order to do that, he must analyze his posture and the way he plays the game. In order to help the players, a system is developed using computer vision techniques which provides better insights into improving games using deep learning algorithms such that the insights can be used in improving the player performance and strategies in individual or team games. This paper provides a deeper knowledge of the kinematic and physiological markers that might better capture athletic performance by looking at the present state-of-the-art AI approaches by analyzing how AI methods and techniques are applied in basketball play. This work mainly concentrates on the Basketball sport, in which different features such as the release angle of the basketball, and the shot predictions are analyzed and tested in real-time. The object detection is performed using the YOLOv5 algorithm and we have obtained a mean average precision of 96.8%. Further, the release angle is calculated using the combination of pose landmarks and object detection and has resulted in the optimal angle for a perfect free throw, which is to be within the range of 45 to 60 degrees. Based on the combination of object detection, release angle, and the polynomial regression, shot prediction is performed and has resulted in accurate results for real-time experimental analysis.

关键词： Basketball Analysis computer vision (You Only Look Once) YOLOv5 Pose estimation Polynomial Regression

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Adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation 8th

Adjust Your Focus: Defocus Deblurring from Dual-Pixel Images...

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8th international conference on computer vision and image processing (cvip)

作者： Swami, Kunal Samsung Res Bangalore Karnataka India Indian Inst Sci Bengaluru India

ISBN: (纸本)9783031581809;9783031581816

Defocus blur is a common problem in photography. It arises when an image is captured with a wide aperture, resulting in a shallow depth of field. Sometimes it is desired, e.g., in portrait effect. Otherwise, it is a problem from both an aesthetic point of view and downstream computer vision tasks, such as segmentation and depth estimation. Defocusing an out-of-focus image to obtain an all-in-focus image is a highly challenging and often ill-posed problem. A recent work exploited dual-pixel (DP) image information, widely available in consumer DSLRs and high-end smartphones, to solve the problem of defocus deblurring. DP sensors result in two sub-aperture views containing defocus disparity cues. A given pixel's disparity is directly proportional to the distance from the focal plane. However, the existing methods adopt a naive approach of a channel-wise concatenation of the two DP views without explicitly utilizing the disparity cues within the network. In this work, we propose to perform an explicit cross-correlation between the two DP views to guide the network for appropriate deblurring in different image regions. We adopt multi-scale cross-correlation to handle blur and disparities at different scales. Quantitative and qualitative evaluation of our multi-scale cross-correlation network (MCCNet) reveals that it achieves better defocus deblurring than existing state-of-the-art methods despite having lesser computational complexity.

关键词： Dual-Pixel Sensors Defocus Deblurring Cross-Correlation All-in-focus image

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Federated Scaling of Pre-trained Models for Deep Facial Expression Recognition 1

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8th international conference on computer vision and image processing (cvip)

作者： Srihitha, P. V. N. Pooja Verma, Mridula Prasad, Munaga V. N. K. Inst Dev & Res Banking Technol Hyderabad Telangana India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Building an efficient deep learning-based Facial Expression Recognition (FER) system is challenging due to the requirements of large amounts of personal data and the rise in data privacy concerns. Federated learning has emerged as a promising solution for such problems, which however is communication-inefficient. Recently, pre-trained models have shown effective performance in federated learning setups regarding convergence. In this paper, we extend the traditional FER towards a new paradigm, where we study the performance of federated fine-tuning of standard vision pre-trained models for FER. More specifically, we propose a Federated Deep Facial Expression Recognition (FedFER) framework, where clients jointly learn to fuse the representations generated by pre-trained deep learning models rather than training a large-scale model from scratch without sharing any data. With the help of extensive experimentation using standard pre-trained vision models (ResNet-50, VGG-16, Xception, vision Transformers) and benchmark datasets (CK+, FERG, FER-2013, JAFFE, MUG), this paper presents interesting perspectives for future research in the direction of federated Deep FER.

关键词： Facial Expression Recognition Deep Neural Networks Federated Learning Pre-trained vision models

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Damage Segmentation and Restoration of Ancient Wall Paintings for Preserving Cultural Heritage 1

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8th international conference on computer vision and image processing (cvip)

作者： Baath, Hardaat Singh Shinde, Soham Keniya, Jinam Mishra, Priyanshu Ranjan Saini, Anil Dhiraj Birla Inst Technol & Sci Pilani Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Rajasthani paintings are unique and intriguing art forms representing India's rich cultural heritage. However, these ancient master pieces are deteriorating due to the passage of time, environmental factors, and human actions. Preserving and Restoring these delicate artworks is crucial. One approach to aid their digital restoration is leveraging advanced technologies like deep learning. This study applies image segmentation and restoration techniques to restore the Rajasthani murals in the Mandawa region of rural rajasthan, India. The main objective is to segment the damaged murals, generate their corresponding binary masks and restore the corresponding areas of the damaged image. The research aims to achieve robust and accurate predicted masks for the murals by utilizing state-of-the-art deep learning models and using their outputs as inputs for image restoration as the final restored output image. Extensive comparisons with different segmentation models show that the proposed approach outperforms the rest with an mIOU of 0.892. The proposed method also demonstrates remarkable inpainting results with an SSIM score of 0.9812 on test images. Results show that the method achieves promising restoration of damaged ancient Indian Wall Paintings.

关键词： image segmentation Ancient Indian murals Restoration image Inpainting Ensemble approach STAPLE

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Multi-scale image Fusion Scheme Based on Gray-Level Edge Maps and Adaptive Weight Maps 3rd

Multi-scale Image Fusion Scheme Based on Gray-Level Edge Map...

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3rd international conference on computer vision and image processing (cvip)

作者： Pradhan, Jitesh Raj, Ankesh Pal, Arup Kumar Banka, Haider Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9789813292918;9789813292901

Digital cameras and other digital devices cannot focus on all significant objects within the single frame due to their limited depth of focus. Consequently, few objects get attention in the captured image while the rest of the objects becomes background information. This problem can be overcome using different multi-focus image fusion techniques because it combines all the partially focused objects of different parent images into a single fully focused fused image. Hence, the final fused image focuses on each and every object of the parent images. In this paper, a novel multi-focus image fusion technique has been proposed which uses different edge-finding operators (like Sobel, Prewitt, Roberts, and Scharr) on preprocessed images. These different edge-finding operators have great pixel discrimination property which helps us to locate all the vital textural information of different partially focused images. Subsequently, an adaptive weight calculation approach has been introduced to generate weight maps of different parent images. Finally, all these parent image weight maps have been deployed into the winner-take-all scheme to integrate all parent images into a single fused image. Further, we have also considered different sets of partially focused images for experimental analysis. The experimental outcomes reveal that the proposed scheme is outperforming as compared to recent state of the arts.

关键词： Adaptive weight maps Depth of focus Edge maps image fusion Multi-focus

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An Effective CNN-Based Approach for Synthetic Face image Detection in Pre-social and Post-social Media Context 8th

An Effective CNN-Based Approach for Synthetic Face Image Det...

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8th international conference on computer vision and image processing (cvip)

作者： Dey, Protyay Jadhav, Abhilasha S. Rana, Kapil Indian Inst Technol Ropar Rupnagar Punjab India

ISBN: (纸本)9783031581731;9783031581748

The proliferation of image manipulation techniques, including DeepFake technology, has posed significant threats to the authenticity and credibility of images. Accurately classifying real and fake images has become crucial in fields such as forensics, security, and media authentication. However, detecting fake images during downloading and uploading from social networks is even more challenging. In this paper, we present an approach based on the EfficientNet model to learn discriminative features for classifying real and synthetic face images shared on social networks. We conducted extensive experiments using the TrueFace dataset, which comprises real and synthetic facial images shared on three major social media platforms. We employed the EfficientNet-B2 model trained on a combination of pre-social and post-social images from the TrueFace dataset. The presented approach outperforms all other methods, achieving accuracies of 99.98%, 100%, and 100% for images shared on Facebook, Telegram, and Twitter. This approach demonstrates exceptional performance when evaluated on a distinct dataset of images shared on social media platforms, separate from the images used for training.

关键词： EfficientNetB2 TrueFace dataset DeepFakes image Forensics Fake face identification

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Cross-Domain Feature Extraction Using CycleGAN for Large FoV Thermal image Creation 1

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8th international conference on computer vision and image processing (cvip)

作者： Rathore, Sudeep Upadhyay, Avinash Sharma, Manoj Yadav, Ajay Chand, G. Shyam Singhal, Amit Mukherjee, Prerana Lall, Brejesh Bennett Univ Greater Noida UP India Netaji Subhas Univ Technol New Delhi India Jawaharlal Nehru Univ New Delhi India Indian Inst Technol Delhi New Delhi India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Thermal images are extensively utilized in defense operations due to their ability to capture thermal radiation. However, the limited Field of View (FoV) in thermal imaging systems often results in insufficient and fragmented large Field of View (FoV) simulation data, thereby compromising the effectiveness of defense applications using large Field of View (FoV) cameras. To address this challenge, we propose an innovative approach employing image stitching techniques to create comprehensive and detailed views. Traditional image feature extractors such as Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are optimized for Red, Green, and Blue (RGB) images. Hence, their performance on thermal images is often sub-optimal as the thermal images contain low-resolution, reduced contrast and dynamic range, non-uniform thermal emissions, and the absence of color information, making identifying and matching robust features and descriptors difficult. To enhance feature extraction, we adopt the Cycle Generative Adversarial Network (CycleGAN) technique to convert thermal images into their RGB counterparts prior to feature extraction. This enables us to leverage the robustness of RGB-optimized feature extractors and amalgamate them with the features obtained from the original thermal images, resulting in a richer and more comprehensive feature set. The performance of this proposed network has been demonstrated and validated on various datasets, showing promising results for large Field of View (FoV) thermal image creation.

关键词： Thermal image stitching Panorama Feature Matching image Blending CycleGAN Deeplearning

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Enhancement of Screening Mammograms Using Dual-Tree Complex Wavelet Transform 8th

Enhancement of Screening Mammograms Using Dual-Tree Complex ...

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8th international conference on computer vision and image processing (cvip)

作者： Joseph, Annie Julie Naz, Farah Pournami, P. N. Natl Inst Technol Calicut Kozhikode Kerala India

ISBN: (纸本)9783031581809;9783031581816

Breast cancer has become one of the most common cancers worldwide. It is also one of the leading causes of death in women. Early detection of breast cancer is the need of the hour as it offers increased treatment options, better survival chances, and improved quality of life. The most economical technique is screening mammograms to promote breast cancer identification at an early stage. This paper proposes an enhancement strategy for screening mammograms based on Dual-Tree Complex Wavelet Transform (DTCWT). The DTCWT yields six directionally selective high-pass sub-bands on multiple scales. The reconstruction from the decomposed bands produces a resultant image with enhancement in directional edges. Thus, edge details are enhanced, improving the visualisation of mass boundaries inside the breast tissues. The proposed method offers PSNR values of 54.3612, 46.043, 48.8719 and SSIM is 0.9977, 0.9869, 0.9916 for the MIAS, DDSM and Inbreast datasets respectively. The proposed DTCWT-based mammogram enhancement can be used as an efficient preprocessing technique in a computer-aided diagnosis system to detect breast cancer in its early stages. The comparison of the proposed method with many of the recent algorithms depicts the overall dominance of its performance, with minimum error and good signal enhancement.

关键词： Breast cancer computer aided diagnosis Dual-tree complex wavelet transform image enhancement

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Uncovering the Extent of Flood Damage using Sentinel-1 SAR imagery: A Case Study of the July 2020 Flood in Assam 8th

Uncovering the Extent of Flood Damage using Sentinel-1 SAR I...

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8th international conference on computer vision and image processing (cvip)

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala 147004 Punjab India

ISBN: (纸本)9783031581731;9783031581748

In India, Assam is known for being highly susceptible to floods, with the state being hit by multiple flooding events each year. The floods in Assam in July 2020 were mainly due to the heavy monsoon rainfall in the region. The Brahmaputra River and its tributaries, which pass through the state, received substantial rainfall, leading to a sudden increase in water levels and flooding in multiple districts. The state's topography, hills, and valleys also exacerbated the situation as the mountain water flowed into the plains and inundated low-lying areas. Identifying areas that floods have impacted is of utmost importance in facilitating an efficient response to the flood. The current work focuses on the case study of the 2020 Assam flood and aims to develop an operational methodology for potential flood-damaged area mapping. Sentinel-1 Synthetic Aperture Radar (SAR) images from April 2020 (before the flood) and July 2020 (after the flood) were used to create water masks based on global thresholding. This was followed by an RGB composite image highlighting the inundation extent. The resulting flood map was compared to a ground truth RGB composite generated by stacking the pre- and post-disaster images. The study's methodology can be replicated to map floods in other areas around the world.

关键词： Flood mapping Sentinel-1 SAR image Backscatter RGB composite Water mask Flood Mask

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