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Skin Disease Detection Using Saliency Maps and Segmentation Techniques 7th

Skin Disease Detection Using Saliency Maps and Segmentation ...

引用

7th International conference on Computer Vision and image processing, CVIP 2022

作者： Toteja, Rishit Gupta, Dhruv Gautam, Vibhor Vishwakarma, Dinesh Kumar Delhi Technological University Shahbad Daulatpur Main Bawana Road Delhi110042 India

ISBN: (纸本)9783031314063

Skin diseases are the most common disease on the planet. When detecting skin diseases, dermatologists must have a high degree of expertise and precision, which is why computer-aided diagnosis is so helpful. An approach for detecting skin diseases has been presented to give more information. Several investigations have been carried out to aid in diagnosing skin disorders such as skin cancer and skin tumours. However, exact identification of skin disease is challenging to cure because of the following factors: visual likeness, little contrast in-between the lesions and skin that is between the affected and non-diseased areas, etc. In this paper we propose a model that uses an image set which is pre-processed by various techniques like deblurring, noise reduction, and then enhanced. The images are then segmented using U-Net and MobileNetV2 is used for classification. The disease is detected at the output for a corresponding input image based on the definite pattern pertaining to a dispersant in the processed image. Then, we compute a class saliency map for the provided image and class. We demonstrate how ConvNets may classify and segment skin diseases using such maps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image segmentation

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Multi-GRU Based Automated image Captioning for Smartphones 29

Multi-GRU Based Automated Image Captioning for Smartphones

引用

29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Keskin, Rumeysa Moral, Ozge Taylan Kilic, Volkan Onan, Aytug Izmir Katip Celebi Univ Dept Elect & Elect Engn Izmir Turkey Izmir Katip Celebi Univ Dept Comp Engn Izmir Turkey

ISBN: (纸本)9781665436496

image captioning is the description of an image with natural language expressions using computer vision and natural language processing fields. Recent advances in hardware and processing power in smartphones lead the development of many image captioning applications. In this study, a novel automatic image captioning system based on the encoder-decoder approach that can be applied in smartphones is proposed. While high-level visual information is extracted with the ResNet152V2 convolutional neural network in the encoder part, the proposed decoder transforms the extracted visual information into natural expressions of the images. The proposed decoder with the multilayer gated recurrent unit structure allows generating more meaningful captions using the most relevant visual information. The proposed system has been evaluated using different performance metrics on the MSCOCO dataset and it outperforms the state-of-the-art approaches. The proposed system is also integrated with our custom-designed Android application, named IMECA, which generates captions in offline mode unlike similar applications. Thus, image captioning is intended to be practical for more people.

关键词： image captioning computer vision natural language processing Android application

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Spatial Domain Method for image Analysis: A Grey-Level Computation Approach 7th

Spatial Domain Method for Image Analysis: A Grey-Level Compu...

引用

Proceedings of the 7th International conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Deepika, Kumari Punj, Deepika Jyoti Atur Centre Model Colony Pune411016 India J C Bose University of Science and Technology 6 Mathura Rd Sector 6 Haryana Faridabad121006 India

ISBN: (纸本)9783031379390

This paper discusses image enhancement, which involves adjusting captured images to improve their visual quality and suitability for display or analysis using techniques like filtering, histogram equalization, and contrast adjustment. The spatial domain method is used for manipulating image grey levels, and the study analyses the transformed images using BRIQUE, NIQE, and PIQE quality metric descriptors. The study shows that the type of image used for processing depends on the application, and binary images have the highest PIQE score. Power law transformation or Gamma-corrected images can reduce or enhance contrast, depending on the gamma’s value. The transformed images have different scores based on the quality metric used. The study also finds that the sum of the global threshold values of an input image and its inverse is 1, and the scores for binary images using both thresholds are similar. Finally, the study shows that BRISQUE and PIQE scores are higher for gamma and more significant than 1;NIQE scores are similar for both gamma values. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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Mono+Sub: Compressing Light Field as Monocular image and Subsidiary Data

Mono+Sub: Compressing Light Field as Monocular Image and Sub...

引用

IEEE visual communications and image processing (VCIP)

作者： Ryosuke Imazu Chihiro Tsutake Keita Takahashi Toshiaki Fujii Nagoya University Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

A light field is usually represented as a set of multi-view images captured from a two-dimensional (2-D) array of viewpoints and requires a large amount of data compared with a standard 2-D image. We propose a 2-D compatible light-field compression method for encoding a light field as a 2-D monocular image and subsidiary data. In terms of the image quality, we prioritize the central image (regarded as the 2-D monocular image) over the other images in the light field, because the light field is considered an extension of the 2-D monocular image. To this end, we encode and decode the monocular image using a standard image codec and introduce a learned encoder and decoder pair for the subsidiary data. Experimental results indicate that our method achieved promising rate-distortion performance, especially for extremely low bit-rate ranges. Even though our method requires only a small amount of subsidiary data compared with those for the monocular image, the entire light field can be reconstructed with reasonable visual quality.

关键词： visualization image coding visual communication Two-dimensional displays Scalability Rate-distortion Network architecture Light fields Standards Optimization

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Comparison of the State-of-the-Art image and Video Quality Assessment Metrics 29

Comparison of the State-of-the-Art Image and Video Quality A...

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29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Akkaya, Emre Ozbek, Nukhet Ege Univ Muhendislik Fak Elekt Elekt Muhendisligi Bolumu Izmir Turkey

ISBN: (纸本)9781665436496

Today, thanks to the developing technology, changes are experienced in many areas and these changes sometimes bring some problems, issues that need to be solved or improved. In the field of digital image and video processing, the viewer experience has been greatly improved with the development of image and video coding methods and technologies in recent years. However, due to these developments, the importance of video and image quality evaluation techniques has increased. In the field of imaging technology, although the human visual system and human judgment experiments will give us the most accurate results, this rapidly growing field has made human judgment experiments unsuitable with the increasing content. At this point, effective and automatic quality assessment metrics are needed to evaluate and optimize advanced compression technologies in terms of visual perception quality. In this paper, our aim is to compare the performances of some of the new generation image and video quality assessment metrics.

关键词： human visual system traditional image&video quality metrics the state-of-the-art image and video quality metrics

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Accurate Recognition of Kiwifruit Based on Improved YOLOv5 5

Accurate Recognition of Kiwifruit Based on Improved YOLOv5

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Wei, Sun Jun, Sun Yi Chen, Li Zhao Jing, Guo Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350302219

In order to meet the urgent needs of automation and intelligent picking of kiwifruit, aiming at the problems of unreasonable construction of kiwifruit data set, low fruit recognition accuracy and poor spatial positioning in the natural environment of orchard, a precise recognition and visual positioning method of kiwifruit based on improved Yolov5s was proposed. In view of the growth characteristics of kiwifruit in trellis orchards, a multi-type kiwifruit data set was first constructed. Furthermore, the attention mechanism and multi-scale module are combined to improve the Yolov5s network structure, identify kiwifruit and extract the center coordinates of the prediction box. The experimental results show that the average accuracy of the model for six kiwifruit types under different weather and light conditions is 98 %. The single image recognition time of 1280× 720 pixel is about 13.8 ms, and the weight is only 15.21 Mb. It can be seen that this study can provide technical support for the vision system of kiwifruit automatic picking robot, and provide reference for the intelligent recognition and positioning of other fruits (such as apples, mangoes and oranges). © 2023 IEEE.

关键词： Orchards

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Distance Weighted Refining Segmentation Method for visual Quality Improvement in V-PCC

Distance Weighted Refining Segmentation Method for Visual Qu...

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IEEE visual communications and image processing (VCIP)

作者： Min Ku Lee Yong-Hwan Kim Intelligent Image Processing Research Center Korea Electronics Technology Institute Seongnam-si Korea

In this paper, we propose an innovative method for refining segmentation method that improves the visual quality of Video-based Point Cloud Compression (V-PCC) encoder. Recently standardized as an international standard by MPEG, V-PCC standard provides state-of-the-art performance in compressing dynamic and dense point cloud object. However, lossy V-PCC encoder has an unavoidable problem of visual quality degradation due to lost points. When converting a 3D point cloud to 2D patches in the V-PCC encoder, some points constituting a point cloud are not converted. In particular, in the refining segmentation of the 2D patch generation process, points that are changed the projection plane due to over-smoothing can be discarded. We propose a distance weighted refining segmentation method that reduces the number of missed points to improve visual quality. Experimental results show a noticeable improvement in visual quality with minor coding gain.

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FSDN: image frequency and semantic decomposition network for image dehazing

FSDN: Image frequency and semantic decomposition network for...

引用

IEEE visual communications and image processing (VCIP)

作者： Zongyang Tong Mingyu Liu Xin Jin Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

image dehazing plays a crucial role in autonomous driving and outdoor surveillance. However, as haze affects different components of an image in various ways and degrees, existing methods treat the image as a singular input and overlook the need to decouple different components, leading to mutual interference during the enhancement of each component. Consequently, issues such as insufficient color restoration or blurred edges may arise. In this paper, we introduce a novel tri-branch network for Single image Dehazing that independently extracts low-frequency, high-frequency, and semantic information from images using three distinct sub-networks. A meticulously designed fusion network is then employed to integrate the information from these three branches to produce the final dehazed image. To facilitate the training of such a complex network, we propose a two-stage training approach. Experimental results demonstrate that our approach achieves state-of-the-art (SOTA) performance.

关键词： Training visual communication image color analysis Surveillance image edge detection Semantics Interference image restoration Data mining Synthetic data

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Deviation Control for Learned image Compression

Deviation Control for Learned Image Compression

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IEEE visual communications and image processing (VCIP)

作者： Yuqi Li Haotian Zhang Xiaomin Song Zheng Liu Huiming Zheng Li Li Dong Liu University of Science and Technology of China Sichuan Xinshi Chuangwei Ultra HD Technology Co. Ltd

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we present visual analyses on latent features of learned image compression and find that the latent variables are spread over a wide range, which may lead to complex entropy coding processes. To address this, we introduce a Deviation Control (DC) method, which applies a constraint loss on latent features and entropy parameter μ. Training with DC loss, we obtain latent features with smaller values of coding symbols and σ, effectively reducing entropy coding complexity. Our experimental results show that the plug-and-play DC loss reduces entropy coding time by 30-40% and improves compression performance.

关键词： Training Analytical models visualization image coding visual communication Transform coding Symbols Entropy coding Entropy Complexity theory

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Vision Transformer and Bidirectional RoBERTa: A Hybrid image Captioning Model Between VirTex and CPTR 12th

Vision Transformer and Bidirectional RoBERTa: A Hybrid Imag...

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12th International Advanced Computing conference, IACC 2022

作者： Lam, Khang Nhut Le, Diem-Kieu Thi Ngo, Truong Dinh Kalita, Jugal Can Tho University Can Tho Viet Nam University of Colorado Colorado Springs United States

ISBN: (纸本)9783031356407

image captioning neural networks are trained simultaneously on image recognition sub-models and natural language processing sub-models to generate description sentences for images. This paper presents several image captioning models based on the encoder-decoder framework. We change the neural sub-models used for the encoder as well as the decoder, and make comparisons. First, we experiment with several ResNet architectures (viz., ResNet-50, ResNet-101, and ResNet-152) as encoders, and Transformer or bidirectional Transformer models as decoders. Second, we use the combination of the Vision Transformer as a visual encoder, and the standard Transformer or RoBERTa as the language decoder. Finally, we propose an image captioning model using Vision Transformer for encoding images and bidirectional Transformer for predicting image captions. The models are trained on the Flickr8k dataset in English and Vietnamese and evaluated using the BLEU metric. The combination model between the Vision Transformer and the bidirectional RoBERTa model outperforms the existing image captioning models, including VirTex and CPTR models. The BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of our best image captioning model are 0.870, 0.661, 0.443, and 0.331 on the English dataset, and 0.829, 0.647, 0.483, and 0.387 on the Vietnamese dataset. © 2023, Springer Nature Switzerland AG.

关键词： image recognition

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