Convolutional Neural Network Based Segmentation (original) (raw)
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A New Model for Image Segmentation Based on Deep Learning
International Journal of Online and Biomedical Engineering (iJOE), 2021
Image segmentation of the medical image and its conversion into anatomical models is an important technique and main point in computer vision (CV) and image processing (IP), training tools that are used routinely in the fields of medicine and surgery. Segmenting images and converting them into a model that depends on its work on the different algorithms and the extent of technological advancement and method of application. The advancement of segmentation algorithms has led to the possibility of creating three-dimensional models for the patient to study without endangering his life. This paper describes a combination of two fields of solving segmentation problem to convert through the workflow of a hybrid algorithm structure Convolutional neural network (CNN, Active Contour & Deep Multi-Planar) and seg3d2 to switch DICOM medical rays “Digital Imaging and Communications in Medicine” into a 3Dimintional model, using data from active contour to be the input of deep learning. This resear...
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The task of semantic segmentation holds a fundamental position in the field of computer vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent times, significant advancements have been achieved in the field of semantic segmentation through the application of Convolutional Neural Networks (CNN) techniques based on deep learning. This paper presents a comprehensive and structured analysis of approximately 150 methods of semantic segmentation based on CNN within the last decade. Moreover, it examines 15 well-known datasets in the semantic segmentation field. These datasets consist of 2D and 3D image and video frames, including general, indoor, outdoor, and street scenes. Furthermore, this paper mentions several recent techniques, such as SAM, UDA, and common post-processing algorithms, such as CRF and MRF. Additionally, this paper analyzes the performance evaluation of reviewed state-of-the-art methods, pioneering methods, common backbone networks...
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World Congress on Electrical Engineering and Computer Systems and Science
Image segmentation is one of the most important branches of image processing. But it comes with various challenges and problems to be solved. Researchers are always working on improving the accuracy, quality and performance of image segmentation techniques. As in modern days, deep learning being involved in almost all problem solving, it is being used in image segmentation too. In this paper, we discussed few image segmentation techniques developed using deep learning, some implementation of these techniques to applications. And lastly, we addressed some limitations, challenges and research scopes for future.
A Semantic-based Scene segmentation using convolutional neural networks
AEU - International Journal of Electronics and Communications, 2020
Semantic segmentation is a crucial operation in the computer vision field. One of the promising techniques is the convolutional neural network (CNN). It can be utilized with both single and multidimensional arrays and is useful for processing 2D arrays in computer vision tasks. In this paper, a new model for semantic scene segmentation is proposed. In order to enhance the segmentation results, the model starts with classifying the input scene as either indoor or outdoor scenes. In this context, the MobileNet is used as it provides better results when compared to Inception-v3 and Inception-ResNet-v2 networks. The next step, two models based on Pyramid Scene Parsing Network (PSPNet) are used for image segmentation (indoor images are segmented by the indoor model and outdoor images are segmented by the outdoor model). Experimental results prove the concept that a specific scene model can achieve higher accuracy than general scene models on the semantic segmentation task.
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Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to a...
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
Knowledge-Based Systems, 2020
From the autonomous car driving to medical diagnosis, the requirement of the task of image segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in computer vision. This task is comparatively complicated than other vision tasks as it needs low level spatial information. Basically, image segmentation can be of two types: semantic segmentation and instance segmentation. The combined version of these two basic tasks is known as panoptic segmentation. In the recent era, the success of deep convolutional neural network (CNN) has influenced the field of segmentation greatly and gave us various successful models till date. In this survey, we are going to take a glance on the evolution of both semantic and instance segmentation work based on CNN. We have also specified architectural details of some state-of-the-art models and discuss their comparative training details to present a lucid understanding of hyper-parameter tuning of those models. We have also drawn a comparison among the performance of those models on different datasets.
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Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of the...
Two Approaches to Supervised Image Segmentation
arXiv (Cornell University), 2023
Though performed almost effortlessly by humans, segmenting 2D gray-scale or color images into respective regions of interest (e.g. background, objects, or portions of objects) constitutes one of the greatest challenges in science and technology as a consequence of several effects including dimensionality reduction(3D to 2D), noise, reflections, shades, and occlusions, among many other possibilities. While a large number of interesting related approaches have been suggested along the last decades, it was mainly thanks to the recent development of deep learning that more effective and general solutions have been obtained, currently constituting the basic comparison reference for this type of operation. Also developed recently, a multiset-based methodology has been described that is capable of encouraging image segmentation performance combining spatial accuracy, stability, and robustness while requiring little computational resources (hardware and/or training and recognition time). The interesting features of the multiset neurons methodology mostly follow from the enhanced selectivity and sensitivity, as well as good robustness to data perturbations and outliers, allowed by the coincidence similarity index on which the multiset approach to supervised image segmentation is founded. After describing the deep learning and multiset neurons approaches, the present work develops comparison experiments between them which are primarily aimed at illustrating their respective main interesting features when applied to the adopted specific type of data and parameter configurations. While the deep learning approach confirmed its potential for performing image segmentation, the alternative multiset methodology allowed for enhanced accuracy while requiring little computational resources.
Semantic Segmentation using Fully Convolutional Net: A Review
Semantic segmentation has paved its way in predicting the models using dense pixel-wise prediction method apart from classification. The presented models for semantic partitions the image into semantically meaningful chunks and classifies each chunk into one of the predetermined classes. The presented model reduces the parameters to be trained and helps in up-sampling; it describes quality and accuracy and efficient mechanism. Deeper the layers are, helps in capturing the high-level semantic features from the previous convolutional layers.