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2020 International Conference on Machine Vision and Image Processing (MVIP)最新文献

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An Abstraction of Semantic Segmentation Algorithms 语义分割算法的一种抽象
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116916
Reihaneh Teymoori, Zahra Nabizadeh, N. Karimi, S. Samavi
{"title":"An Abstraction of Semantic Segmentation Algorithms","authors":"Reihaneh Teymoori, Zahra Nabizadeh, N. Karimi, S. Samavi","doi":"10.1109/MVIP49855.2020.9116916","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116916","url":null,"abstract":"semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, semantic segmentation is used in many tasks like cancer detection, robot-assisted surgery, satellite images, self-driving car, etc. in this process, accuracy and efficiency are the two crucial goals for this purpose, there are several state-of-the-art neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and saving the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researchers to achieve a comprehensive view. Due to this, in this paper, an abstract framework for semantic segmentation is offered. This framework consists of 4 blocks that cover the majority of the methods that have been proposed for semantic segmentation. In this paper, we also attempt to compare different approaches and consider the importance of each part in semantic segmentation. Although our proposed framework considers most of the previous methods, maybe a few papers need new blocks.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126086482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation 面向知识提炼的深度神经网络师生建模技术
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2019-12-31 DOI: 10.1109/MVIP49855.2020.9116923
Sajjad Abbasi, M. Hajabdollahi, N. Karimi, S. Samavi
{"title":"Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation","authors":"Sajjad Abbasi, M. Hajabdollahi, N. Karimi, S. Samavi","doi":"10.1109/MVIP49855.2020.9116923","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116923","url":null,"abstract":"Knowledge distillation (KD) is a new method for transferring knowledge of a structure under training to another one. The conventional application of KD is in the form of learning a small model (named as a student) by soft labels produced by a complex model (named as a teacher). Due to the novel idea introduced in KD, recently, its notion is used in different methods such as compression and processes that are going to enhance the model accuracy. Although different techniques are proposed in the area of KD, there is a lack of a model to generalize KD techniques. In this paper, various studies in the scope of KD are investigated and analyzed to build a general model for KD. All the methods and techniques in KD can be summarized through the proposed model. By utilizing the proposed model, different methods in KD are better investigated and explored. The advantages and disadvantages of different approaches in KD can be better understood and developing a new strategy for KD can be possible. Using the proposed model, different KD methods are represented in an abstract view.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128225041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
Image Seam-Carving by Controlling Positional Distribution of Seams 控制接缝位置分布的图像缝雕刻
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2019-12-31 DOI: 10.1109/MVIP49855.2020.9116888
Mahdi Ahmadi, N. Karimi, S. Samavi
{"title":"Image Seam-Carving by Controlling Positional Distribution of Seams","authors":"Mahdi Ahmadi, N. Karimi, S. Samavi","doi":"10.1109/MVIP49855.2020.9116888","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116888","url":null,"abstract":"Image retargeting is a new image processing task that renders the change of aspect ratio in images. One of the most famous image-retargeting algorithms is seam-carving. Although seam-carving is fast and straightforward, it usually distorts the images. In this paper, we introduce a new seam-carving algorithm that not only has the simplicity of the original seam-carving but also lacks the usual unwanted distortion existed in the original method. The positional distribution of seams is introduced. We show that the proposed method outperforms the original seam-carving in terms of retargeted image quality assessment and seam coagulation measures.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128523121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A General Framework for Saliency Detection Methods 显著性检测方法的一般框架
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2019-12-27 DOI: 10.1109/MVIP49855.2020.9116881
F. Mostafaie, Zahra Nabizadeh, N. Karimi, S. Samavi
{"title":"A General Framework for Saliency Detection Methods","authors":"F. Mostafaie, Zahra Nabizadeh, N. Karimi, S. Samavi","doi":"10.1109/MVIP49855.2020.9116881","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116881","url":null,"abstract":"Saliency detection is one of the most challenging problems in the fields of image analysis and computer vision. Many approaches propose different architectures based on the psychological and biological properties of the human visual attention system. However, there is not still an abstract framework, which summarized the existing methods. In this paper, we offered a general framework for saliency models, which consists of five main steps: pre-processing, feature extraction, saliency map generation, saliency map combination, and post-processing. Also, we study different saliency models containing each level and compare their performance together. This framework helps researchers to have a comprehensive view of studying new methods.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134196374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Efficient Approach for Using Expectation Maximization Algorithm in Capsule Networks 胶囊网络中期望最大化算法的一种有效方法
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2019-12-11 DOI: 10.1109/MVIP49855.2020.9116870
M. Hasani, Amin Nasim Saravi, Hassan Khotanlou
{"title":"An Efficient Approach for Using Expectation Maximization Algorithm in Capsule Networks","authors":"M. Hasani, Amin Nasim Saravi, Hassan Khotanlou","doi":"10.1109/MVIP49855.2020.9116870","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116870","url":null,"abstract":"Capsule Networks (CapsNets) are brand-new architectures that have shown ground-breaking results in certain areas of Computer Vision (CV). In 2017, Hinton and his team introduced CapsNets with routing-by-agreement in \"Sabour et al\" and in a more recent paper \"Matrix Capsules with EM Routing\" they proposed a more complete architecture with Expectation-Maximization (EM) algorithm. Unlike the traditional convolutional neural networks (CNNs), this architecture is able to preserve the pose of the objects in the picture. Due to this characteristic, it has been able to beat the previous state-of-the-art results on the smallNORB dataset, which includes images with various view points. Also, this new architecture is more robust to white box adversarial attacks. However, CapsNets have two major drawbacks. They can’t perform as well as CNNs on complex datasets and, they need a huge amount of time for training. We try to mitigate these shortcomings by finding optimum settings of EM routing iterations for training CapsNets. Unlike the past studies, we use un-equal numbers of EM routing iterations for different stages of the CapsNet. We manage to achieve higher accuracies than the original CapsNet while training the network up to three times faster. For our research, we use three datasets: Yale face dataset, Belgium Traffic Sign dataset, and Fashion-MNIST dataset.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Effect of Scene Context on Weakly Supervised Semantic Segmentation 场景上下文对弱监督语义分割的影响
2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2019-02-12 DOI: 10.1109/MVIP49855.2020.9116890
Mohammad Kamalzare, R. Kahani, A. Talebpour, Ahmad Mahmoudi Aznaveh
{"title":"The Effect of Scene Context on Weakly Supervised Semantic Segmentation","authors":"Mohammad Kamalzare, R. Kahani, A. Talebpour, Ahmad Mahmoudi Aznaveh","doi":"10.1109/MVIP49855.2020.9116890","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116890","url":null,"abstract":"Image semantic segmentation is parsing image into several partitions in such a way that each region of which involves a semantic concept. In a weakly supervised manner, since only image-level labels are available, discriminating objects from the background is challenging, and in some cases, much more difficult. More specifically, some objects which are commonly seen in one specific scene (e.g. \"train\" typically is seen on \"railroad track\") are much more likely to be confused. In this paper, we propose a method to add the target-specific scenes in order to overcome the aforementioned problem. Actually, we propose a scene recommender which suggests to add some specific scene contexts to the target dataset in order to train the model more accurately. It is notable that this idea could be a complementary part of the baselines of many other methods. The experiments validate the effectiveness of the proposed method for the objects for which the scene context is added.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"2671 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127486956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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