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Leveraging Profiling for Facial Recognition Optimization

Leveraging Profiling for Facial Recognition Optimization

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Advances in Electrical Engineering and computer applications( AEECA), 2020 ieee international conference on

作者： Elena Roxana Buhuş Lacrimioara Grama Signal Processing Group Basis of Electronics Department Faculty of Electronics Telecommunications and Information Technology Technical University of Cluj-Napoca Cluj-Napoca Romania

This research presents an advanced approach to facial recognition systems through a combination of incremental supervised learning and histogram-based feature extraction. Building upon prior work, the study aims to optimize recognition accuracy while minimizing computation time. The proposed methodology employs the Simplified Fuzzy ARTMAP neural network for classification, enhancing its efficiency by leveraging a simplified version of the Fuzzy ARTMAP algorithm. Local Binary Patterns (LBP) are utilized for feature extraction, effectively capturing intricate facial textures. A comprehensive profiling analysis is conducted to ensure optimal performance, identifying time-intensive segments for strategic enhancements. Experimental validation is conducted on three open-source face databases, showcasing exceptional recognition rates across diverse scenarios. This approach proves particularly effective when the training data consists of subjects with limited facial expression variance, such as the AT&T database. Additionally, the application demonstrates superior outcomes when the datasets maintain an ordered distribution. These findings collectively underscore the robustness and promise of the proposed methodology for enhancing facial recognition accuracy in various real-world contexts, making meaningful contributions to the domain of facial authentication systems.

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Domain Adversarial Learning Towards Underwater image Enhancement

Domain Adversarial Learning Towards Underwater Image Enhance...

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international conference on computer Vision Workshops (ICCV Workshops)

作者： Meghna Kapoor Rohan Baghel Badri Narayan Subudhi Vinit Jakhetiya Ankur Bansal Indian Institute of Technology Jammu Jammu and Kashmir India

Underwater images are degraded due to the absorption and scattering of light inside the water. The underwater degradation causes the loss of information in terms of texture, style, color, and minute detail of edges and hence the degraded images are not useful in many higher-order applications. Several deep learning techniques are explored by researchers across the globe for the same. Further, deep learning networks learn the distribution of degraded training samples and fail when there is a deviation due to a change in water type. This paper proposes an encoder-decoder network that preserves the image content, texture, and style while maintaining overall global similarity by capturing the inherent distribution of the training samples. To overcome the deviation due to a change in water type, a classifier network is induced in the latent space of encoder-decoder architecture. The classifier loss and adversarial loss in the classifier network ensure the learning across domains and avoid setting priors on captured distribution. Hence, the proposed model is robust against the change of water type and can be deployed in real-life without retraining. To train the model, we use attenuation coefficients of underwater environments at different depths to recreate 5430 paired underwater images from the Underwater image Enhancement Benchmark (UIEB) dataset with six distinct types of water. Our proposed model enhanced the degraded images among different degradation levels due to depth and water type variations. The proposed model is evaluated on UIEB and EUVP benchmark databases. The performance of the proposed model is verified against twenty-two state-of-the-art methods in terms of underwater reference and no-reference image quality assessment metrics.

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GPT-4 versus Bard and Bing: LLMs for Fake image Detection

GPT-4 versus Bard and Bing: LLMs for Fake Image Detection

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Intelligent Cybernetics Technology & applications (ICICyTA), international conference on

作者： Omar Mustafa Al-Janabi Osamah Mohammed Alyasiri Elaf Ayyed Jebur College of Medicine University of Baghdad Baghdad Iraq Karbala Technical Institute Al-Furat Al-Awsat Technical University Karbala Iraq School of Computer Sciences Universiti Sains Malaysia Penang Malaysia

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deepfakes – a task that has become critically important in a world increasingly reliant on digital media. Our investigation rigorously assesses the capabilities of these advanced LLMs in identifying and differentiating manipulated imagery. We explore how these models process visual data, their effectiveness in recognizing subtle alterations, and their potential in safeguarding against misleading representations. The implications of our findings are far-reaching, impacting areas such as security, media integrity, and the trustworthiness of information in digital platforms. Moreover, the study sheds light on the limitations and strengths of current LLMs in handling complex tasks like image verification, thereby contributing valuable insights to the ongoing discourse on AI ethics and digital media reliability.

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Indoor Depth Recovery Based on Deep Unfolding with Non-Local Prior

Indoor Depth Recovery Based on Deep Unfolding with Non-Local...

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international conference on computer Vision (ICCV)

作者： Yuhui Dai Junkang Zhang Faming Fang Guixu Zhang School of Computer Science and Technology East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University

In recent years, depth recovery based on deep networks has achieved great success. However, the existing state-of-the-art network designs perform like black boxes in depth recovery tasks, lacking a clear mechanism. Utilizing the property that there is a large amount of non-local common characteristics in depth images, we propose a novel model-guided depth recovery method, namely the DC-NLAR model. A non-local auto-regressive regular term is also embedded into our model to capture more non-local depth information. To fully use the excellent performance of neural networks, we develop a deep image prior to better describe the characteristic of depth images. We also introduce an implicit data consistency term to tackle the degenerate operator with high heterogeneity. We then unfold the proposed model into networks by using the half-quadratic splitting algorithm. This proposed method is experimented on the NYU-Depth V2 and SUN RGB-D datasets, and the experimental results achieve comparable performance to that of deep learning methods.

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Comparative Evaluation of Fixed Windowing Strategies on CT Brain images Using Multiple Deep Learning Models

Comparative Evaluation of Fixed Windowing Strategies on CT B...

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international ieee conference on Signal-image Technologies and Internet-Based System

作者： Supatta Viriyavisuthisakul Natsuda Kaothanthong Parinya Sanguansat Toshihiko Yamasaki Dittapong Songsaeng School of Management Technology Sirinhorn International Institute of Technology Thammasat University Pathumthani Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Electrical Engineering The University of Tokyo Tokyo Japan Department of Radiology Siriraj Hospital Faculty of Medicine Mahidol University Bangkok Thailand

Window setting in CT brain images is the crucial pre-processing step to examine the abnormalities for diagnosing disease. Recently, many methods have been proposed to determine the suitable window automatically instead of fixing the window. However, fixed windowing methods may still be used in clinical practice due to their simplicity and ease of use. Here, we propose to evaluate the 45 different fixed windowing in noncontrast cranial computer tomography (NCCT) images without computer tomography perfusion (CTp). The 15 latest deep learning models are performed on all interested windows to classify between the hyperacute or acute phases of ischemic stroke and normal brain. The experiments can provide the reference fixed windowing value to optimize the deep learning model and help clinicians to choose the appropriate fixed windowing values.

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Surpass Teacher: Enlightenment Structured Knowledge Distillation of Transformer

Surpass Teacher: Enlightenment Structured Knowledge Distilla...

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ieee international conference on Systems, Man and Cybernetics

作者： Ran Yang Chunhua Deng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

It is difficult to train a trustworthy transformer model on a small image classification dataset. This research proposes a sophisticated structured knowledge distillation algorithm that uses CNNs as Transformer's sophisticated teachers, significantly lowering the number of training datasets needed. To better to develop the potential for CNN tutors, this research configures a public data set for CNN teaching as an enlightenment textbook to guide Transformer's training and avoid falling into local optimization prematurely. The distillation process then employs a “learn-digest-self-distillation” learning strategy to enable the Transformer to assimilate CNN knowledge in a structured manner. Sufficient experiments show that the proposed method is significantly better than the direct training Transformer under the condition of limited data sets. Moreover, in order to show the practical application value, this research contributed a practical data set for the classification of smoking and calling. The corresponding code and dataset will be released at https://***/wustdch/surpass-teacher if this paper is accepted.

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Progressive Perception Learning for Distribution Modulation in Siamese Tracking

Progressive Perception Learning for Distribution Modulation ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kun Hu Xianchen Zhou Mingyu Cao Mengzhu Wang Guangjie Gao Wenjing Yang Huibin Tan State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Sciences National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

We explore an innovative view on distribution modulation to boost Siamese trackers. Specially, we observed two cases of possible distribution inconsistency in Siamese tracking: 1) Two branches with different sizes may be in different distribution ranges after a shared backbone (including BN layers). 2) The background data may affect the total feature distribution of the search branch. To address these issues, we proposed a plug-and-play component named Progressive Perception Learning Module (P 2 LM) to modulate the distribution using three feature normalization blocks successively, i.e., Self-Aware Block (SAB), Target-Aware Block (TAB), and Region-Aware Block (RAB). SAB regulates the distribution of each branch independently for the first issue. TAB uses the target information to guide the distribution adjustments of the two branches. RAB divides the search image into foreground and background with a region mask and normalizes them separately to filter the background distractors for robust tracking. TAB and RAB synergistically alleviate the distribution shifts caused by environmental variance. Experiments on OTB100, UAV123, LaSOT, and GOT-10k verify the compelling effects of our module.

关键词： Target tracking Modulation Information filters Acoustics Speech processing Self-aware

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Efficient Feature Fusion for Learning-Based Photometric Stereo

Efficient Feature Fusion for Learning-Based Photometric Ster...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yakun Ju Kin-Man Lam Jun Xiao Cong Zhang Cuixin Yang Junyu Dong Department of Electronic and Information Engineering The Hong Kong Polytechnic University School of Computer Science and Technology Ocean University of China

How to handle an arbitrary number for input images is a fundamental problem of learning-based photometric stereo methods. Existing approaches adopt max-pooling or observation map to fuse an arbitrary number of extracted features. However, these methods discard a large amount of the features from the input images, impacting the utilization and accuracy, or ignore the constraints from the intra-image spatial domain. In this paper, we explore how to efficiently fuse features from a variable number of input images. First, we propose a bilateral extraction module, which categorizes features into positive and negative, to maximally keep the useful feature in the fusion stage. Second, we adopt a top-k pooling to both the bilateral information, which selects the k maximum response value from all features. These two modules proposed are "plug-and-play" and can be used in different fusion tasks. We further propose a hierarchical photometric stereo network, namely HPS-Net, to handle bilateral extraction and top-k pooling for multiscale features. Experiments in the widely used benchmark illustrate the improvement of our proposed framework in the conventional max-pooling method and the proposed HPS-Net outperforms existing learning-based photometric stereo methods.

关键词： Fuses Convolution Benchmark testing Feature extraction Acoustics Task analysis Speech processing

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An image-processing-Based System for Object Detection

An Image-Processing-Based System for Object Detection

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Advances in Computing, Communication and Applied Informatics (ACCAI), international conference on

作者： SruthyVidiyala SwathiKadari SushmaThippani AnikeTejaswi ArrabairuVeena Bathula Sony Bhoj Reddy Engineering College for Women Hyderabad Telangana India

Understanding how to recognize and track a moving object in real time is essential in the field of computer vision. Underwater computer vision can collect important data that might be used in a wide variety of practical applications. This concept is employed for surveillance, allowing us to keep tabs on the military installation, manage traffic, and coordinate with submerged devices to save lives. The robot’s position will be adjusted to the left, right, front, and rear depending on where the item is detected and identified to be moving in this project. In this way, the robot’s safe distance from its intended victim is never compromised. Hardware-wise, we're using an Arm11 Raspberry Pi, a picamera for Arduino mounting, and an Android device for tracking the robot’s movements. Python code running on Linux controls the pan and tilt camera’s ability to provide an object description through open cv.

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An Improved Convolutional Neural Network for Medical image Segmentation

An Improved Convolutional Neural Network for Medical Image S...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), ieee international conference on

作者： Safaa Hakeem OBAIDI al-Khafaji Kowstubha Palle Lenin Thingbaijam P. Venkata Prasad Balasubbareddy Mallala Sachin Pund College of Dentistry University of Babylon University of Babylon computer science Babylon Iraq Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Faculty IT MLCU India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper provides a progressed convolutional neural community (CNN) version for medical picture segmentation. It specializes in improving the accuracy and speed of today's segmenting of scientific photos and improving modern semantic segmentation of first-rate present-day clinical photos. Modern accuracy is improved by introducing pre-processing layer, residual connection, attention module, and up sampling layer to the CNN. The stepped-forward overall performance state-of-the-art pace is achieved through dual-project cutting-edge, wherein the segmentation task is divided into two sequential sub-obligations, modern-day semantic segmentation and shape regression. The improved version is examined on publicly available scientific datasets, achieving 49a2d564f1275e1c4e633abc331547db segmentation performance. The results show that the proposed method is robust, green, and generalizable..

关键词： Shape Semantic segmentation Education Liver Convolutional neural networks Lesions Task analysis

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