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A Spatial Constraint Image Segmentation Algorithm Based on Block Clustering
2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
Image segmentation is a key preprocessing step for object recognition and has a profound effect on the subsequent classification and recognition. Visual spatial clustering based segmentation is a commonly used method in image segmentation, which clusters pixels using visual descriptors by space similarity measure. It can achieve good results in simple image segmentation wit...Show More
A Hybrid Method of Block-Based Region Growing and Visible Color Difference for Color Image Segmentation
2009 First International Workshop on Education Technology and Computer Science
Year: 2009 | Conference Paper |
In this paper we propose a color image segmentation algorithm based on visible color difference and block-based region growing techniques. Firstly, the original image is divide into image blocks which are not overlapped; then, the mean and variance of each image black was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks...Show More
HBNet: Hybrid Blocks Network for Segmentation of Gastric Tumor from Ordinary CT Images
Yongtao Zhang;Baiying Lei;Chao Fu;Jie Du;Xinjian Zhu;Xiaowei Han;Lei Du;Wenwen Gao;Tianfu Wang;Guolin Ma
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
Year: 2020 | Conference Paper |
Cited by: Papers (9)
Gastric cancer has been one of the leading causes of cancer death. To assist doctors on diagnosis and treatment planning of gastric cancer, an accurate and automatic segmentation of gastric tumor method is very necessary for clinical practices. In this paper, we develop an improved U-Net called hybrid blocks network (HBNet) to automatically segment gastric tumor. In contrast to the sta...Show More
Improving Residual Block for Semantic Image Segmentation
2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
Currently, most state-of-the-art semantic segmentation methods employ residual network as base network. Residual network is composed of residual blocks. In this paper, we present an improved residual block called pyramid residual block to explicitly exploit context information and enhance useful features. In contrast to the standard residual block, the proposed pyrami...Show More
Color Image Coding Based on Block Truncation Coding using Quadtree Segmentation
2018 3rd International Conference on Computer and Communication Systems (ICCCS)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
In this paper, we proposed a bit-rate adjustable color image compression technique based on block truncation coding. To exploit the similarity among the neighboring pixels, the quadtree segmentation technique is used to divide the color image into variable-sized blocks based on their block activities. Different rules are used to encode the image blocks of different si...Show More
A block-based MAP segmentation for image compressions
A novel block-based image segmentation algorithm using the maximum a posteriori (MAP) criterion is proposed. The conditional probability in the MAP criterion, which is formulated by the Bayesian framework, is in charge of classifying image blocks into edge, monotone, and textured blocks. On the other hand, the a priori probability is responsible for edge connectivity and ho...Show More
Underwater Sonar Image Segmentation Based on Deep Learning of Receptive Field Block and Search Attention Mechanism
2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS)
Year: 2021 | Conference Paper |
Cited by: Papers (10)
Because of the special imaging environment, sonar images have some problems, such as gray distortion, blurred edge, various shapes, and missing dataset. To solve the missing of underwater sonar images dataset, a dataset of underwater sonar images dataset is established, including synthetic sonar dataset and real sonar dataset. According to the characteristics of sonar images, a new segmentation...Show More
Architecture and FPGA-Implementation of Scalable Picture Segmentation by 2D Scanning with Flexible Pixel-Block Size
2010 First International Conference on Networking and Computing
Year: 2010 | Conference Paper |
We report a two-dimensional (2D) pixel block scanning architecture for image segmentation by segment growing. This architecture can optimize processing speed, power consumption, and circuit area by modifying size and shape of the pixel block. Real-time processing can be maintained by using additional the two important techniques of (i) boundary-scan of the grown segment only, (ii...Show More
Encoder- and Decoder-Based Networks Using Multiscale Feature Fusion and Nonlocal Block for Remote Sensing Image Semantic Segmentation
With the development of convolutional neural networks, the semantic segmentation of remote sensing images has been widely developed, but there are still some unsolved problems in this field due to the lack of multiscale information and the feature mismatch at the upsampling process. To solve these problems, we propose a network called multiscale feature fusion and alignment network (MFANet)....Show More
SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information
2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
The fast development of Convolution Neural Networks (CNN) based on U-shaped architecture has shown innovative improvements in the fields of image segmentation. However, these approaches cannot learn global information in images due to the local aspect of the convolution operation. This paper deals with designing a hybrid method of medical image segmentation. Taking advantage of Shifted...Show More
SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information
Year: 2022 | Conference Paper |
Spine X-ray Image Segmentation Based on Transformer and Adaptive Optimized Postprocessing
Lingrong Zhang;Jinglin Yang;Dong Liu;Feng Zhang;Sibo Nie;Yuchen Tan;Taipeng Guo
2022 IEEE 2nd International Conference on Software Engineering and Artificial Intelligence (SEAI)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Accurate segmentation of vertebral blocks in X-ray images of the whole spine is necessary for intelligent diagnosis of spinal diseases. However, the vertebral block in X-ray spine image has similar appearance with background, which cause a huge challenge to segmentation performance. Even though the classical Unet network and existing popular methods have been proved as effe...Show More
Semantic Segmentation of Satellite Images Using a U-Shaped Fully Connected Network with Dense Residual Blocks
2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Semantic segmentation is the task of clustering pixels into an object class. In the field of remote sensing semantic segmentation has wide applications ranging from scene cover classification to change detection for scene understanding. With the success of deep learning algorithms for classification tasks, there has been much work to apply convolutional neural networks in remote sensin...Show More
Secure Image Encryption U sing Dynamic Block Segmentation and Adaptive Pixel Modification with Chaotic Masking
2024 International Conference on Engineering and Emerging Technologies (ICEET)
Year: 2024 | Conference Paper |
The widespread use of digital technologies has resulted in an increased generation, transmission and storage of digital images, which need to be secured effectively and efficiently. This paper proposes and evaluates a novel image encryption algorithm that comprises two secure components as its diffusion and confusion modules, i.e., a dynamic block segmentation and permutation module fo...Show More
Manipulating Neural Network Block for Robust Image Segmentation
2022 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2022 | Conference Paper |
Convolutional neural networks have presented a new paradigm in the field of computer vision, but their fixed convolutional filter size causes long-range dependency loss problems. Also, because of the equal importance of all convolution filters, unnecessary information is included in the convolution result. These problems are more serious in image segmentation, where every pixel is labeled, a...Show More
To understand minute segments of a soft copy image, we zoom the image or magnify it. The underlying motivation of this work is to enable the machine to learn to magnify an image to capture very small and fine details of it. Retinal Blood Vessel Segmentation is such an area where segmentation of small and fine vessels is needed. In this paper, we have designed RIMNet (Image Magnificatio...Show More
Block-based segmentation and adaptive coding for visually lossless compression of scanned documents
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)
Year: 2001 | Conference Paper |
This paper presents a novel block-based segmentation and adaptive coding (BSAC) algorithm for visually lossless compression of scanned documents that contain not only photographic images but also text and graphic images. For such a compound image source, we structure the image into nonoverlapping blocks and classify each block into four different classes based on the empiri...Show More
Optimizing text detachment from the document image using block-based segmentation and wavelet transform
2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)
Year: 2017 | Conference Paper |
Text detachment from the background is a challenging problem in document image processing. There are many techniques for this goal. Two common methods to detach text from the background are block-based segmentation using histogram of local data and AC coefficients. Unfortunately, these methods are not accurate enough to detect block type due to noise and various distribution of t...Show More
Fingerprint image segmentation based on rectangular block
2010 IEEE International Conference on Progress in Informatics and Computing
Year: 2010 | Conference Paper |
The state-of-the-art algorithms of fingerprint segmentation, usually based on square block, are too dependent on the images of high quality and regular shape, so they have many deficiencies in dealing with low quality or irregular fingerprint images, such as high complexity, time-consuming and unsatisfactory segmentation results, etc. In order to adapt to different quality images...Show More
Retinal Blood Vessel Segmentation Using Residual Block Incorporated U-Net Architecture and Fuzzy Inference System
2018 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)
Year: 2018 | Conference Paper |
Cited by: Papers (11)
This paper presents two efficient methods for vessel segmentation in retinal images. In this paper, we formulate the segmentation challenge using two different methods, Fuzzy classifier and an U-net autoencoder incorporated with Residual blocks. For fuzzy classifier, we consider the mean and median property of a fundus image for feature extraction and then use a fuzzy Interface t...Show More
A W-Net Based Architecture with Residual Block for Liver Segmentation
2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
One of the traditionally used methods for segmenting images is by using a convolutional neural network (CNN). CNN is helpful in various applications like object detection, image recognition, optical character recognition (OCR), and image segmentation. Consequently, this research work has designed a new model by utilizing similar CNN-based architecture as a modification of W-Net. Specifically...Show More
Block-Based and Segmentation-Based Approaches for Component Substitution based Hyperspectral Pansharpening
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Pansharpening is the fusion of panchromatic (PAN) image and multispectral (MS) or hyperspectral (HS) images and provides high spatial and high spectral resolution MS or HS images. Pansharpening mainly extracs the high frequency details from the PAN image, and then injects these details to the MS or HS image. This detail injection procedure can be performed in a variety of ways: using global, bl...Show More
Enhanced U-Transformer Networks for Automatic Pulmonary Vessel Segmentation in Ct Images
Hao Qi;Jiabao Jin;Gang Ding;Xiangxing Chen;Sunkui Ke;Yinran Chen;Xiongbiao Luo
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Pulmonary vessel CT segmentation is important to clinical diagnosis of lung diseases. But it is still a challenge due to limited CT quality and complicated vascular structures. This paper proposes new enhanced U-transformer networks that combine transformers, a contrast enhancement block with a reverse attention block to perform end-to-end vessel segmentation. Specifically,...Show More
Woodland Segmentation of Gaofen-6 Remote Sensing Images Based on Deep Learning
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Gaofen-6 (GF-6) is a geostationary, earth-observation satellite, rely on it's multi-spectral images, GF-6 has the ability to support the monitoring of woodland resources. In this paper, the multi-spectral images sent by GF-6 are studied as dataset, and a model called Infrared Attention Network (InfAttNet) which based on semantic segmentation method is proposed to distinguish woodland from ot...Show More
Correction Learning for Medical Image Segmentation
Guang Zhang;Shuhao Dong;Hui Xu;Hongyu Zhang;Yongjian Wu;Yanwei Zhang;Xiaoming Xi;Yilong Yin
Breast tumor segmentation is useful to diagnose breast cancer. However, challenges, such as intensity inhomogeneity and shadowing artifacts arise in this task. To address these two issues, this paper proposes a robust ultrasound image segmentation method based on correction learning. At first, a novel idea of correction learning is introduced. In contrast to traditional methods that de...Show More
Noise estimation using adaptive Gaussian filtering and variable block size image segmentation
IEEE EUROCON 2017 -17th International Conference on Smart Technologies
Year: 2017 | Conference Paper |
Cited by: Papers (6)
This paper proposes an efficient algorithm for noise level estimation in still images. The images are assumed to be corrupted by additive white Gaussian noise. The proposed method relies on block-based image segmentation and Gaussian filtering to estimate the standard deviation of Gaussian noise. The proposed method employs adaptive image segmentation, where the size of segmen...Show More
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