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Fast Under Water image Enhancement for Real Time Applications 6

Fast Under Water Image Enhancement for Real Time Application...

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6th International conference for Convergence in Technology (I2CT)

作者： Bhat, Aruna Tyagi, Aadhar Verdhan, Aarsh Verma, Vaibhav Delhi Technol Univ Dept Comp Sci & Engn Delhi India Delhi Technol Univ Dept Software Engn Delhi India

ISBN: (纸本)9781728188768

Ocean exploration is a major challenge that we are facing today. With advancements to fields of marine engineering and aquatic robotics, we are capable of performing autonomous and complex decision making deep underwater. Significance of online Underwater computer vision Algorithms is ever increasing. Underwater images, however, suffer from inaccurate colors, hazing, colour cast and degradations because of unequal absorption of light by water. Algorithms designed for detection/enhancement in the air are of no use underwater. Although a lot of underwater image enhancement algorithms have come up in recent times, most of them are not suitable for real-time applications like AUV, due to their high computational times. these algorithms are more suitable for offline analysis. In this paper, we propose an algorithm which is fast enough for real-time systems such as AUVs/ROVs and is comparable to the offline state of the art image enhancement algorithms. We will be exploring histogram equalization techniques for dehazing and automatic white balancing algorithms for color correction. UIEB (Underwater image Enhancement Benchmark) is used for evaluation of our algorithm. the codes and results are available at https://***/opgp/underwater-image-processing.

关键词： underwater image enhancement real-time color-cast CLAHE white balancing

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Attention-based Multi-scale ViT Fine-grained Visual Classification 6

Attention-based Multi-scale ViT Fine-grained Visual Classifi...

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6th International conference on computer Science and Artificial Intelligence, CSAI 2022

作者： Li, Rujia Liu, Junya Yang, Zhen Zhou, Xin Yin, Zhijian Jiangxi Science and Technology Normal University China

ISBN: (纸本)9781450397773

Fine-grained visual classification (FGVC) is a challenging task in image classification due to the small differences between classes and the large differences within subclasses. In the early works, some methods mainly rely on constructing bounding box annotations and integrating attention mechanisms based on CNN methods for fine-grained visual classification. In recent years, the vision Transformer (ViT) has begun to show better performance in image classification, object detection, and object tracking. To further investigate the performance of ViT in FGVC, this paper proposes to combine the CNN method with ViT and introduce a dual-path hierarchy into the pyramid structure-top-down feature path and bottom-up channel-spatial attention path;DropBlock is used to accurately localize discriminative regions;SENet and global covariance pooling (GCP) measures are used to further enhance the ability of the network model to extract feature maps information. the Attention-based Multi-scale ViT Fine-grained Visual Classification (AMViT-CNN) proposed in this work has achieved good classification results on public fine-grained datasets (CUB-200-2011, Stanford-Cars). © 2022 ACM.

关键词： image classification

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image reconstruction based on deep iterative shrinkage network 6

Image reconstruction based on deep iterative shrinkage netwo...

引用

6th International conference on image, vision and Computing, ICIVC 2021

作者： Li, Jiahang Zhu, Qi Wu, Yuezhou Gao, Xuyang School of Geoscience and Technology Southwest Petroleum University Sichuan Chengdu610500 China College of Mechatronic Engineering Southwest Petroleum University Sichuan Chengdu610500 China School of Computer Science Civil Aviation Flight University of China Sichuan Guanghan618307 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781665443685

the fast compressed sensing (CS) reconstruction for natural image processing aims to infer the original pixel vector from randomized measurements as correctly as possible. Current first-order proximal mainstream schemes, including alternating direction methods of multipliers (ADMM) and the iterative shrinkage thresholding algorithm (ISTA), have advantages in convergence and stability at the expense of computing speed and complex tuning strategy. this paper proposes the deep iterative shrinkage network for real-time applications, where network layers replace the iterative optimization steps. the hyperparameters associated with shrinkage thresholds and step sizes are adjusted by standard deep learning updates. Numerical results suggest that the proposed method has flexibility and effectiveness in image reconstruction with different compression ratios while preserving the computational simplicity of end-to-end methods. © 2021 IEEE.

关键词： image reconstruction

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A vision-based System for Recognition of Words used in indian Sign Language Using MediaPipe 6

A Vision-based System for Recognition of Words used in India...

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6th International conference on image Information processing, ICIIP 2021

作者： Adhikary, Subhangi Talukdar, Anjan Kumar Kumar Sarma, Kandarpa Gauhati University Department of Electronics and Communication Engineering Guwahati781014 India

ISBN: (纸本)9781665433617

indian Sign Language (ISL) is a form of communication used in India by the speech and hearing impaired community. It conveys linguistic information through gestures of the hands, arms, face, and head. However, the gestures used may not always be directly related to the referent term, resulting in a significant communication gap. Hence there is a need for a translator that can translate ISL into text or speech. the proposed system aims to recognize signs of ISL and translate them into texts that can be easily read. the ISL recognition system is based on Google's MediaPipe as a feature extractor and Random Forest Classifier is used for classification. An accuracy of 97.4% is achieved. the results show that the integration of MediaPipe with ML algorithms may be effectively employed to correctly recognise signs of ISL. © 2021 IEEE.

关键词： Audition

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A Platform for Large Scale Auto Annotation of Scanned Documents Featuring Real-Time Model Building and Model Pooling 1

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6th International conference on computer vision and image processing, CVIP 2021

作者： Prashanth, Komuravelli Kowndinya, Boyalakuntla Vijay, Chilaka Teja, Dande Rodge, Vidya Velaga, Ramya Deshmukh, Reena Abasaheb Kalidas, Yeturu Indian Institute of Technology Tirupati Tirupati India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

Document digitization is an active area of research especially involving handwritten manuscripts. While the most common use cases involve digital libraries, there are other important applications in the area of electronic health records where handwritten text is predominant in developing worlds. the state-of-the-art approaches are domain-specific, and scaling across domains is still an open research problem. We report here a platform for real-time annotation and training of sub-region models in scanned documents using model pools and plug-n-play of annotation services. Given a document, sub-regions are annotated with textual labels. the textual regions themselves may correspond to characters or words or any other pattern of interest. For a given sub-region category, several sub-regions may be present in a given page or across pages. In the proposed system, a user needs to annotate only some of the sub-regions. A convolutional neural network (CNN) model is built for each of the sub-region categories, and named sets or pools of such models are prepared for application on any new document. We observe that a sub-region label may be provided by an existing optical character recognition system instead of a human annotator. In this regard, we have provisioned annotation as a service where any third-party system can be integrated into a plug-n-play mechanism. the state-of-the-art systems focused on having a pre-trained monolithic model which suffers from the problem of catastrophic forgetting when new sub-region classes are added over time. In our approach, due to sub-region specific models, the previous data models are not touched and hence providing a truly incremental learning solution. We have carried out the validation by choosing handwritten data sets belonging to different languages such as Devanagari, Kannada, Telugu, English that span diverse text patterns and the models produced by our sub-region detection algorithm were evaluated on documents containing hundreds

关键词： Optical character recognition

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Performance analysis of CPU GPU for Real Time image/Video 6

Performance analysis of CPU GPU for Real Time Image/Video

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6th International conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Harakannanavar, Sunil S. Gagan Karthik, S. Ramachandran, Srinath thribhuvan Gupta, S. Ramachandra, C.A. Electronics and Communication Engg Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India

ISBN: (纸本)9781665435598

computer vision algorithms are used in applications which require the given system to process and display images or video inputs. However, this will be computationally intensive on the machine that is performing the automated task based on the visual inputs and results in a large overhead or a lag between input and output processed video. To address this issues, parallel processing is a very useful strategy and can be achieved by dividing the computational tasks among the given hardware of the system which would make use of it efficiently and would work around the limitations of the hardware in the system. In this paper, the parallelism is achieved by multi-threading the algorithm that will divide the computation among the number of cores in the CPU and further optimize by dividing the load with both CPU and GPU for an efficient use of the system hardware and to obtain an optimized result. When the algorithms are executed without any optimization, the obtained output video fps is high and is undesirable. In this proposed method, a speedup factor of 91 times is recorded in AIELI algorithm. © 2021 IEEE.

关键词： computer vision

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Identification of Tampering image Using SIFT Descriptor 6

Identification of Tampering Image Using SIFT Descriptor

引用

6th International conference on image Information processing, ICIIP 2021

作者： Chakraborty, Debjani Choudhury, Sandeep Dutta, Sanjib Kumar Haldar, Biswajit Narula Institute of Technology Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665433617

In the recent era, image tampering has become one of the threatening security problems in digital platforms. there are many software's available for tampering with an image that depicts as an original image. Different tampering techniques are used to hide important portions from an image or document, one very common practice is a copy-move forgery that is quite impossible to distinguish with an open eye. Authentications of such images are an ardent research area in image processing and computer vision but still a challenging problem. this paper presents a method to identify image tampering that is based on SIFT (Scale Invariant Feature Transform) algorithm. SIFT descriptor is used to extract keypoint features from the input image and a hierarchical clustering algorithm is used to improve the accuracy of identifying the tampered location. the execution time of our proposed method is proportional to image resolution. If one portion of the image is copied and pasted on multiple locations on the same image, our proposed method can identify such occurrences. Finally, Homography is used to show the tampering points and their matching. © 2021 IEEE.

关键词： image resolution

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Utilizing Convolutional Neural Networks for Object Recognition with image Filtering and Edge Detection in Deep Learning

Utilizing Convolutional Neural Networks for Object Recogniti...

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International conference on Recent Trends in Advance Computing (ICRTAC)

作者： J. Shajeena Vinay Kumar Jarugula A. Bindhu S. Kayalvizhi Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Infant Jesus College of Arts and Science for Women Mulagumoodu Kanniyakumari District India Department of CSE Malla Reddy Institute of Engineering and Technology Secunderabad India

the optimal technique for object detection directly in the compressed domain of a video sequence or an image is presented in this study, we delve into the application of Convolutional Neural Networks (CNNs) for object recognition, employing image filtering and edge detection techniques within the framework of deep learning. the paper leverages the power of CNNs to automatically learn and extract essential features from images, enabling effective object recognition. We explore the significance of image filtering and edge detection as preprocessing steps to enhance feature extraction, leading to improved classification accuracy. through comprehensive experiments and analyses, we demonstrate the efficacy of our approach in various real-world scenarios, highlighting the potential for CNNs to play a pivotal role in advancing object recognition technology. this paper offers valuable insights into the synergy between CNNs and image processing techniques, with promising implications for diverse applications, including computer vision, robotics, and autonomous systems.

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3D Object Detection and Tracking Methods using Deep Learning for computer vision Applications 6

3D Object Detection and Tracking Methods using Deep Learning...

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6th International conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Shreyas, E. Sheth, Manav Hiren Mohana Electronics and Telecommunication Engineering RV College of Engineering® Bengaluru India

ISBN: (纸本)9781665435598

3D multi-object detection and tracking is an essential constituent for many applications in today's world. Object detection is a technology related to computer vision and image processing that allows us to detect instances of certain classes. there are numerous applications like robotics, autonomous driving and augmented reality. A bounding box often defines the region of interest and then is used to classify into respective categories. Due to identical appearance and shape of various objects and the interference of lighting and shielding, object detection has always been a challenging problem in computer vision. Conventional 2D object detection yields four degrees of freedom axis-aligned bounding boxes with centre (x, y) and 2D size (w, h), the 3D bounding boxes generally have 6 Degrees of freedom: 3D physical size (w, h, l), 3D centre location (x, y, z). 2D object detection and tracking methods do not provide depth information to perform essential tasks in various computer vision applications. One among them is the Autonomous driving. 3D object detection includes depth information that provides more information on the structure of detected object. More information is required to make decisions accurately in different fields where 3D object detection and tracking can be applied. In this paper various 3D object detection and tracking methods are elaborated for various computer vision applications, this includes various fields such as robotics, driving, space field and also in the military. © 2021 IEEE.

关键词： computer vision

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Abnormality detection and classification of macular diseases from optical coherence tomography images: Using feature space comparison 21

Abnormality detection and classification of macular diseases...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Ashok, L.R. Sreeni, K.G. Computer Vision Lab Dept.Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

Optical Coherence Tomography (OCT) is a non-invasive imaging technology for diagnosing various macular pathologies. It assists ophthalmologists to detect abnormalities in the retina and thereby avoid many sight-threatening conditions. However, manual detection process depends on the experience of the clinical practitioner and it is a time-consuming process. Automatic detection of abnormalities using computer algorithms allows the clinical practitioner to detect the abnormalities in a faster and accurate manner. Here, a Deep learning-based approach is proposed which can detect abnormal retinal images in various disease classes which are not limited to classes present in the training data-set. the performance of the Abnormality detector is tested on UCSD, Kaggle & OCTID data-sets and obtained an accuracy of 99.80%, 99.90% & 94.59%, respectively. the performance of Classifier is also tested on UCSD & Kaggle data-sets and obtained an accuracy of 97.7%, 99.79%, respectively. © 2021 ACM.

关键词： Optical tomography

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