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Journal articles on the topic 'Shot segmentation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Yan, Zhenggang, Yue Yu, and Mohammad Shabaz. "Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games." Computational Intelligence and Neuroscience 2021 (September 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/4674140.

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The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.

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Bak, Hui-Yong, and Seung-Bo Park. "Comparative Study of Movie Shot Classification Based on Semantic Segmentation." Applied Sciences 10, no. 10 (May 14, 2020): 3390. http://dx.doi.org/10.3390/app10103390.

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The shot-type decision is a very important pre-task in movie analysis due to the vast information, such as the emotion, psychology of the characters, and space information, from the shot type chosen. In order to analyze a variety of movies, a technique that automatically classifies shot types is required. Previous shot type classification studies have classified shot types by the proportion of the face on-screen or using a convolutional neural network (CNN). Studies that have classified shot types by the proportion of the face on-screen have not classified the shot if a person is not on the screen. A CNN classifies shot types even in the absence of a person on the screen, but there are certain shots that cannot be classified because instead of semantically analyzing the image, the method classifies them only by the characteristics and patterns of the image. Therefore, additional information is needed to access the image semantically, which can be done through semantic segmentation. Consequently, in the present study, the performance of shot type classification was improved by preprocessing the semantic segmentation of the frame extracted from the movie. Semantic segmentation approaches the images semantically and distinguishes the boundary relationships among objects. The representative technologies of semantic segmentation include Mask R-CNN and Yolact. A study was conducted to compare and evaluate performance using these as pretreatments for shot type classification. As a result, the average accuracy of shot type classification using a frame preprocessed with semantic segmentation increased by 1.9%, from 93% to 94.9%, when compared with shot type classification using the frame without such preprocessing. In particular, when using ResNet-50 and Yolact, the classification of shot type showed a 3% performance improvement (to 96% accuracy from 93%).

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Tapu, Ruxandra, and Titus Zaharia. "Video Segmentation and Structuring for Indexing Applications." International Journal of Multimedia Data Engineering and Management 2, no. 4 (October 2011): 38–58. http://dx.doi.org/10.4018/jmdem.2011100103.

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This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot, the authors construct a static storyboard by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing techniques for similar performances. Finally, the authors propose a shot grouping strategy that iteratively clusters visually similar shots under a set of temporal constraints. Two different types of visual features are exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.

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Boccignone, G., A. Chianese, V. Moscato, and A. Picariello. "Foveated shot detection for video segmentation." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 3 (March 2005): 365–77. http://dx.doi.org/10.1109/tcsvt.2004.842603.

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Tian, Pinzhuo, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, and Yang Gao. "Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12087–94. http://dx.doi.org/10.1609/aaai.v34i07.6887.

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To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm. However, most existing methods only focus on the traditional 1-way segmentation setting (i.e., one image only contains a single object). This is far away from practical semantic segmentation tasks where the K-way setting (K > 1) is usually required by performing the accurate multi-object segmentation. To deal with this issue, we formulate the few-shot semantic segmentation task as a learning-based pixel classification problem, and propose a novel framework called MetaSegNet based on meta-learning. In MetaSegNet, an architecture of embedding module consisting of the global and local feature branches is developed to extract the appropriate meta-knowledge for the few-shot segmentation. Moreover, we incorporate a linear model into MetaSegNet as a base learner to directly predict the label of each pixel for the multi-object segmentation. Furthermore, our MetaSegNet can be trained by the episodic training mechanism in an end-to-end manner from scratch. Experiments on two popular semantic segmentation datasets, i.e., PASCAL VOC and COCO, reveal the effectiveness of the proposed MetaSegNet in the K-way few-shot semantic segmentation task.

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Zhao, Guanyi, and He Zhao. "One-Shot Image Segmentation with U-Net." Journal of Physics: Conference Series 1848, no. 1 (April 1, 2021): 012113. http://dx.doi.org/10.1088/1742-6596/1848/1/012113.

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7

Nascimento, Jacinto C., and Gustavo Carneiro. "One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 12 (December 1, 2020): 3054–70. http://dx.doi.org/10.1109/tpami.2019.2922959.

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8

Chen, Xiaoyu, Xiaotian Lou, Lianfa Bai, and Jing Han. "Residual Pyramid Learning for Single-Shot Semantic Segmentation." IEEE Transactions on Intelligent Transportation Systems 21, no. 7 (July 2020): 2990–3000. http://dx.doi.org/10.1109/tits.2019.2922252.

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9

Liao, ShengBo, Jingmeng Sun, and Haitao Yang. "Research on Long Shot Segmentation in Basketball Video." International Journal of Multimedia and Ubiquitous Engineering 10, no. 12 (December 31, 2015): 183–94. http://dx.doi.org/10.14257/ijmue.2015.10.12.19.

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Wang, Yang, Jingmeng Sun, Yifei Liu, and Yueqiu Han. "Research on Close Shot Segmentation in Sports Video." International Journal of Multimedia and Ubiquitous Engineering 11, no. 1 (January 31, 2016): 255–66. http://dx.doi.org/10.14257/ijmue.2016.11.1.25.

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Liu, Binghao, Jianbin Jiao, and Qixiang Ye. "Harmonic Feature Activation for Few-Shot Semantic Segmentation." IEEE Transactions on Image Processing 30 (2021): 3142–53. http://dx.doi.org/10.1109/tip.2021.3058512.

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12

Xiang, Yun Zhu. "Multi-Modality Video Scene Segmentation Algorithm with Shot Force Competition." Applied Mechanics and Materials 513-517 (February 2014): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.514.

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In order to quickly and effectively segment the video scene, a multi-modality video scene segmentation algorithm with shot force competition is proposed in this paper. This method is take full account of temporal associated co-occurrence of multimodal media data, to calculate the similarity between video shot by merging the video low-level features, then go to the video scene segmentation based on the judgment method of shot competition. The authors experiments show that the video scene can be efficiently separated by the method proposed in the paper.

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CAO, X., and P. N. SUGANTHAN. "NEURAL NETWORK BASED TEMPORAL VIDEO SEGMENTATION." International Journal of Neural Systems 12, no. 03n04 (June 2002): 263–69. http://dx.doi.org/10.1142/s0129065702001163.

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The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences.

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MOHANTA, PARTHA PRATIM, SANJOY KUMAR SAHA, and BHABATOSH CHANDA. "A NOVEL TECHNIQUE FOR SIZE CONSTRAINED VIDEO STORYBOARD GENERATION USING STATISTICAL RUN TEST AND SPANNING TREE." International Journal of Image and Graphics 13, no. 01 (January 2013): 1350001. http://dx.doi.org/10.1142/s0219467813500010.

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Storyboard consisting of key-frames is a popular format of video summarization as it helps in efficient indexing, browsing and partial or complete retrieval of video. In this paper, we have presented a size constrained storyboard generation scheme. Given the shots i.e. the output of the video segmentation process, the method has two major steps: extraction of appropriate key-frame(s) from each shot and finally, selection of a specified number of key-frames from the set thus obtained. The set of selected key-frames should retain the variation in visual content originally possessed by the video. The number of key-frames or representative frames in a shot may vary depending on the variation in its visual content. Thus, automatic selection of suitable number of representative frames from a shot still remains a challenge. In this work, we propose a novel scheme for detecting the sub-shots, having consistent visual content, from a shot using Wald–Wolfowitz runs test. Then from each sub-shot a frame rendering the highest fidelity is extracted as key-frame. Finally, a spanning tree based novel method is proposed to select a subset of key-frames having specific cardinality. Chronological arrangement of such frames generates the size constrained storyboard. Experimental result and comparative study show that the scheme works satisfactorily for a wide variety of shots. Moreover, the proposed technique rectifies mis-detection error, if any, incurred in video segmentation process. Similarly, though not implemented, the proposed hypothesis test has ability to rectify the false-alarm in shot detection if it is applied on pair of adjacent shots.

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15

Xing, Chencong, Shujing Lyu, and Yue Lu. "Few-Shot Image Segmentation Based on Dual Comparison Module and Sequential k-Shot Integration." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 886. http://dx.doi.org/10.2991/ijcis.d.210212.003.

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YOO, HUN-WOO, and DONG-SIK JANG. "AUTOMATED VIDEO SEGMENTATION USING COMPUTER VISION TECHNIQUES." International Journal of Information Technology & Decision Making 03, no. 01 (March 2004): 129–43. http://dx.doi.org/10.1142/s0219622004000957.

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A video segmentation method is proposed in this paper. For abrupt cut detection, inter-frame similarities are computed using gray-level and edge histograms and a cut is declared when the similarities are under the predetermined threshold value. Gradual shot boundary detection is decided based on the similarities between the current frame and the previous shot boundary frame. Correlation coefficients are used to obtain universal threshold values, which are applied to various video data. Experimental results show that the proposed method provides 95% recall and 80% precision rates for abrupt cuts, and 83% recall and 54% precision rates for gradual changes.

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Zhu, Zhenyue, Shujing Lyu, and Yue Lu. "A few-shot segmentation method for prohibited item inspection." Journal of X-Ray Science and Technology 29, no. 3 (May 11, 2021): 397–409. http://dx.doi.org/10.3233/xst-210846.

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BACKGROUND: With the rapid development of deep learning, several neural network models have been proposed for automatic segmentation of prohibited items. These methods usually based on a substantial amount of labelled training data. However, for some prohibited items of rarely appearing, it is difficult to obtain enough labelled samples. Furthermore, the category of prohibited items varies in different scenarios and security levels, and new items may appear from time to time. OBJECTIVE: In order to predict prohibited items with only a few annotated samples and inspect prohibited items of new categories without the requirement of retraining, we introduce an Attention-Based Graph Matching Network. METHODS: This model applies a few-shot semantic segmentation network to address the issue of prohibited item inspection. First, a pair of graphs are modelled between a query image and several support images. Then, after the pair of graphs are entered into two Graph Attention Units with similarity weights and equal weights, the attentive matching results will be obtained. According to the matching results, the prohibited items can be segmented from the query image. RESULTS: Experiment results and comparison using the Xray-PI dataset and SIXray dataset show that our model outperforms several other state-of-the-art learning models. CONCLUSIONS: This study demonstrates that the similarity loss function and the space restriction module proposed by our model can effectively remove noise and supplement spatial information, which makes the segmentation of the prohibited items on X-ray images more accurate.

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Zhou, Tianfei, Shunzhou Wang, Yi Zhou, Yazhou Yao, Jianwu Li, and Ling Shao. "Motion-Attentive Transition for Zero-Shot Video Object Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13066–73. http://dx.doi.org/10.1609/aaai.v34i07.7008.

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In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e., DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts. Code is available at: https://github.com/tfzhou/MATNet.

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Shen, Fengli, Zong-Hui Wang, and Zhe-Ming Lu. "Weakly supervised classification model for zero-shot semantic segmentation." Electronics Letters 56, no. 23 (November 12, 2020): 1247–50. http://dx.doi.org/10.1049/el.2020.2270.

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Pambala, Ayyappa Kumar, Titir Dutta, and Soma Biswas. "SML: Semantic meta-learning for few-shot semantic segmentation☆." Pattern Recognition Letters 147 (July 2021): 93–99. http://dx.doi.org/10.1016/j.patrec.2021.03.036.

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Hu, Tao, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, and Cees G. M. Snoek. "Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8441–48. http://dx.doi.org/10.1609/aaai.v33i01.33018441.

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Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot setting.

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Haq, Ijaz Ul, Khan Muhammad, Tanveer Hussain, Soonil Kwon, Maleerat Sodanil, Sung Wook Baik, and Mi Young Lee. "Movie scene segmentation using object detection and set theory." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771984527. http://dx.doi.org/10.1177/1550147719845277.

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Movie data has a prominent role in the exponential growth of multimedia data over the Internet, and its analysis has become a hot topic with computer vision. The initial step towards movie analysis is scene segmentation. In this article, we investigated this problem through a novel intelligent Convolutional Neural Network (CNN) based three folded framework. The first fold segments the input movie into shots, the second fold detects objects in the segmented shots and the third fold performs object-based shots matching for detecting scene boundaries. Texture and shape features are fused for shots segmentation, and each shot is represented by a set of detected objects acquired from a light-weight CNN model. Finally, we apply set theory with the sliding window–based approach to integrate the same shots to decide scene boundaries. The experimental evaluation indicates that our proposed approach outran the existing movie scene segmentation approaches.

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Haroon, Muhammad, Junaid Baber, Ihsan Ullah, Sher Muhammad Daudpota, Maheen Bakhtyar, and Varsha Devi. "Video Scene Detection Using Compact Bag of Visual Word Models." Advances in Multimedia 2018 (November 8, 2018): 1–9. http://dx.doi.org/10.1155/2018/2564963.

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Video segmentation into shots is the first step for video indexing and searching. Videos shots are mostly very small in duration and do not give meaningful insight of the visual contents. However, grouping of shots based on similar visual contents gives a better understanding of the video scene; grouping of similar shots is known as scene boundary detection or video segmentation into scenes. In this paper, we propose a model for video segmentation into visual scenes using bag of visual word (BoVW) model. Initially, the video is divided into the shots which are later represented by a set of key frames. Key frames are further represented by BoVW feature vectors which are quite short and compact compared to classical BoVW model implementations. Two variations of BoVW model are used: (1) classical BoVW model and (2) Vector of Linearly Aggregated Descriptors (VLAD) which is an extension of classical BoVW model. The similarity of the shots is computed by the distances between their key frames feature vectors within the sliding window of length L, rather comparing each shot with very long lists of shots which has been previously practiced, and the value of L is 4. Experiments on cinematic and drama videos show the effectiveness of our proposed framework. The BoVW is 25000-dimensional vector and VLAD is only 2048-dimensional vector in the proposed model. The BoVW achieves 0.90 segmentation accuracy, whereas VLAD achieves 0.83.

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Zhang, Kun, Yuanjie Zheng, Xiaobo Deng, Weikuan Jia, Jian Lian, and Xin Chen. "Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs." Electronics 9, no. 9 (September 14, 2020): 1508. http://dx.doi.org/10.3390/electronics9091508.

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The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time.

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Teng, Long, ZhongLiang Fu, Qian Ma, Yu Yao, Bing Zhang, Kai Zhu, and Ping Li. "Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning." Computational and Mathematical Methods in Medicine 2020 (March 19, 2020): 1–9. http://dx.doi.org/10.1155/2020/1487035.

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Background. Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student’s understanding by pixel-level translation between echocardiography and theoretically sketch images. Previous research studies split it into two aspects of image segmentation and synthesis. This split makes it hard to achieve pixel-level corresponding translation. Besides, it is also challenging to leverage deep-learning-based methods in each phase where a handful of annotations are available. Methods. To address interactive translation with limited annotations, we present a two-step transfer learning approach. Firstly, we train two independent parent networks, the ultrasound to sketch (U2S) parent network and the sketch to ultrasound (S2U) parent network. U2S translation is similar to a segmentation task with sector boundary inference. Therefore, the U2S parent network is trained with the U-Net network on the public segmentation dataset of VOC2012. S2U aims at recovering ultrasound texture. So, the S2U parent network is decoder networks that generate ultrasound data from random input. After pretraining the parent networks, an encoder network is attached to the S2U parent network to translate ultrasound images into sketch images. We jointly transfer learning U2S and S2U within the CGAN framework. Results and conclusion. Quantitative and qualitative contrast from 1-shot, 5-shot, and 10-shot transfer learning show the effectiveness of the proposed algorithm. The interactive translation is achieved with few-shot transfer learning. Thus, the development of new applications from scratch is accelerated. Our few-shot transfer learning has great potential in the biomedical computer-aided image translation field, where annotation data are extremely precious.

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ZHANG Hao-su, 张昊骕, 朱晓龙 ZHU Xiao-long, 胡新洲 HU Xin-zhou, and 任洪娥 REN Hong-e. "Shot segmentation technology based on SURF features and SIFT features." Chinese Journal of Liquid Crystals and Displays 34, no. 5 (2019): 521–29. http://dx.doi.org/10.3788/yjyxs20193405.0521.

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Guha Roy, Abhijit, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger. "‘Squeeze & excite’ guided few-shot segmentation of volumetric images." Medical Image Analysis 59 (January 2020): 101587. http://dx.doi.org/10.1016/j.media.2019.101587.

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Lv, Fengmao, Haiyang Liu, Yichen Wang, Jiayi Zhao, and Guowu Yang. "Learning Unbiased Zero-Shot Semantic Segmentation Networks Via Transductive Transfer." IEEE Signal Processing Letters 27 (2020): 1640–44. http://dx.doi.org/10.1109/lsp.2020.3023340.

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Zhang, Xiaolin, Yunchao Wei, Yi Yang, and Thomas S. Huang. "SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation." IEEE Transactions on Cybernetics 50, no. 9 (September 2020): 3855–65. http://dx.doi.org/10.1109/tcyb.2020.2992433.

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El-Khoury, Elie, Christine Sénac, and Philippe Joly. "Unsupervised Segmentation Methods of TV Contents." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/539796.

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We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

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Liu, Ke, Xin Bian, Li Xin Hou, and Xin Zhou. "Study on Star Shot Analysis for Physics Quality Assurance in Radiotherapy." Applied Mechanics and Materials 541-542 (March 2014): 1313–18. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.1313.

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Star shot analysis is one common and important module in physics quality assurance (QA) which is regularly performed on the radiotherapy machine. A star shot analysis method by image processing is proposed. First, through ROI selection and image binarization, the irrelevant image parts are excluded and the star-shots are highlighted. Then the center and beam branches of the shots are determined by iteration, coordinate transformation, threshold values analysis and image segmentation. Finally, beam center lines and a minimum circle encompassing beam intersections are calculated by mathematic operations. The proposed method is performed on three star shot films and all beam center lines, beam intersections and minimum circles are obtained instantly and showed clearly. The results present good consistency with the commercial verification software and indicate that the proposed method provides the physicist a considerable comparison in physics QA.

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Voulodimos, Athanasios, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, and Nikolaos Doulamis. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images." Sensors 21, no. 6 (March 22, 2021): 2215. http://dx.doi.org/10.3390/s21062215.

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Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 ± 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 ± 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).

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Li, Yikang, and Zhenzhou Wang. "3D Reconstruction with Single-Shot Structured Light RGB Line Pattern." Sensors 21, no. 14 (July 14, 2021): 4819. http://dx.doi.org/10.3390/s21144819.

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Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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Jiang, Xinghao, Tanfeng Sun, Jin Liu, Juan Chao, and Wensheng Zhang. "An adaptive video shot segmentation scheme based on dual-detection model." Neurocomputing 116 (September 2013): 102–11. http://dx.doi.org/10.1016/j.neucom.2011.11.037.

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Yang, Guangchao, Dongmei Niu, Caiming Zhang, and Xiuyang Zhao. "Recognizing novel patterns via adversarial learning for one-shot semantic segmentation." Information Sciences 518 (May 2020): 225–37. http://dx.doi.org/10.1016/j.ins.2020.01.016.

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Zhou, Tianfei, Jianwu Li, Shunzhou Wang, Ran Tao, and Jianbing Shen. "MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation." IEEE Transactions on Image Processing 29 (2020): 8326–38. http://dx.doi.org/10.1109/tip.2020.3013162.

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Sun, Jiande, and Ju Liu. "A blind video watermarking scheme based on ICA and shot segmentation." Science in China Series F 49, no. 3 (June 2006): 302–12. http://dx.doi.org/10.1007/s11432-006-0302-9.

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Wang, Yooseung, Dong hyuk Lee, Jiseong Heo, and Jihun Park. "One-Shot Summary Prototypical Network Toward Accurate Unpaved Road Semantic Segmentation." IEEE Signal Processing Letters 28 (2021): 1200–1204. http://dx.doi.org/10.1109/lsp.2021.3087457.

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Bao, Yanqi, Kechen Song, Jie Wang, Liming Huang, Hongwen Dong, and Yunhui Yan. "Visible and thermal images fusion architecture for few-shot semantic segmentation." Journal of Visual Communication and Image Representation 80 (October 2021): 103306. http://dx.doi.org/10.1016/j.jvcir.2021.103306.

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Ji, Hyesung, Danial Hooshyar, Kuekyeng Kim, and Heuiseok Lim. "A semantic-based video scene segmentation using a deep neural network." Journal of Information Science 45, no. 6 (December 19, 2018): 833–44. http://dx.doi.org/10.1177/0165551518819964.

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Abstract:

Video scene segmentation is very important research in the field of computer vision, because it helps in efficient storage, indexing and retrieval of videos. Achieving this kind of scene segmentation cannot be done by just calculating the similarity of low-level features presented in the video; high-level features should also be considered to achieve a better performance. Even though much research has been conducted on video scene segmentation, most of these studies failed to semantically segment a video into scenes. Thus, in this study, we propose a Deep-learning Semantic-based Scene-segmentation model (called DeepSSS) that considers image captioning to segment a video into scenes semantically. First, the DeepSSS performs shot boundary detection by comparing colour histograms and then employs maximum-entropy-applied keyframe extraction. Second, for semantic analysis, using image captioning that benefits from deep learning generates a semantic text description of the keyframes. Finally, by comparing and analysing the generated texts, it assembles the keyframes into a scene grouped under a semantic narrative. That said, DeepSSS considers both low- and high-level features of videos to achieve a more meaningful scene segmentation. By applying DeepSSS to data sets from MS COCO for caption generation and evaluating its semantic scene-segmentation task results with the data sets from TRECVid 2016, we demonstrate quantitatively that DeepSSS outperforms other existing scene-segmentation methods using shot boundary detection and keyframes. What’s more, the experiments were done by comparing scenes segmented by humans and scene segmented by the DeepSSS. The results verified that the DeepSSS’ segmentation resembled that of humans. This is a new kind of result that was enabled by semantic analysis, which was impossible by just using low-level features of videos.

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BABER, JUNAID, NITIN AFZULPURKAR, and SHIN'ICHI SATOH. "A FRAMEWORK FOR VIDEO SEGMENTATION USING GLOBAL AND LOCAL FEATURES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (August 2013): 1355007. http://dx.doi.org/10.1142/s0218001413550070.

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Rapid increase in video databases has forced the industry to have efficient and effective frameworks for video retrieval and indexing. Video segmentation into scenes is widely used for video summarization, partitioning, indexing and retrieval. In this paper, we propose a framework for scene detection mainly based on entropy and Speeded Up Robust Features (SURF) features. First, we detect the fade and abrupt boundaries based on frame entropy analysis and SURF features matching. Fade boundaries are smart indication of scenes beginning or ending in many videos and dramas, and are detected by frame entropy analysis. Before abrupt boundary detection, unnecessary frames which are obviously not abrupt boundaries, such as blank screens, high intensity influenced images, sliding credits, are removed. Candidate boundaries are detected to make SURF features efficient for abrupt boundary detection, and SURF features between candidate boundaries and their adjacent frames are used to detect the abrupt boundaries. Second, key frames are extracted from abrupt shots. We evaluate our key frame extraction with other famous algorithms and show the effectiveness of the key frames. Finally, scene boundaries are detected using sliding window of size K over the key frames in temporal order. In experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm for shot boundary achieves substantial improvements over state-of-the-art methods with the precision of 99% and the recall of 97.8%. Experimental results for video segmentation into scenes are also promising, compared to famous state-of-the-art techniques.

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Mačkowiak, Sławomir. "Segmentation of Football Video Broadcast." International Journal of Electronics and Telecommunications 59, no. 1 (March 1, 2013): 75–84. http://dx.doi.org/10.2478/eletel-2013-0009.

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Abstract In this paper a novel segmentation system for football player detection in broadcasted video is presented. Proposed detection system is a complex solution incorporating a dominant color based segmentation technique of a football playfield, a 3D playfield modeling algorithm based on Hough transform and a dedicated algorithm for player tracking, player detection system based on the combination of Histogram of Oriented Gradients (HOG) descriptors with Principal Component Analysis (PCA) and linear Support Vector Machine (SVM) classification. For the shot classification the several classification technique SVM, artificial neural network and Linear Discriminant Analysis (LDA) are used. Evaluation of the system is carried out using HD (1280×720) resolution test material. Additionally, performance of the proposed system is tested with different lighting conditions (including non-uniform pith lightning and multiple player shadows) and various camera positions. Experimental results presented in this paper show that combination of these techniques seems to be a promising solution for locating and segmenting objects in a broadcasted video.

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Glavaš, Goran, and Swapna Somasundaran. "Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7797–804. http://dx.doi.org/10.1609/aaai.v34i05.6284.

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Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model – a neural architecture consisting of two hierarchically connected Transformer networks – is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.

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SA, ANGADI, VILAS NAIK, and ASHWIN KUMAR. "AUTOMATIC ACTIVITY SEGMENTATION FROM SURVEILLANCE VIDEO USING CONVENTIONAL SHOT BOUNDARY DETECTION METHODS." International Journal of Machine Intelligence 4, no. 1 (May 30, 2012): 404. http://dx.doi.org/10.9735/0975-2927.4.1.404-404.

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Yin, Yingjie, De Xu, Xingang Wang, and Lei Zhang. "AGUnet: Annotation-guided U-net for fast one-shot video object segmentation." Pattern Recognition 110 (February 2021): 107580. http://dx.doi.org/10.1016/j.patcog.2020.107580.

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Kim, Seung-Hyun, and Doosung Hwang. "A shot change detection algorithm based on frame segmentation and object movement." Journal of the Korea Society of Computer and Information 20, no. 5 (May 30, 2015): 21–29. http://dx.doi.org/10.9708/jksci.2015.20.5.021.

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Chen, Xu, Chunfeng Lian, Li Wang, Hannah Deng, Steve H. Fung, Dong Nie, Kim-Han Thung, et al. "One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures." IEEE Transactions on Medical Imaging 39, no. 3 (March 2020): 787–96. http://dx.doi.org/10.1109/tmi.2019.2935409.

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Shahid, Lubna, Farrokh Janabi-Sharifi, and Patrick Keenan. "Image segmentation techniques for real-time coverage measurement in shot peening processes." International Journal of Advanced Manufacturing Technology 91, no. 1-4 (November 29, 2016): 859–67. http://dx.doi.org/10.1007/s00170-016-9756-0.

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Xuemei, Jiang, Liu Quan, and Wu Qiaoyan. "A new video watermarking algorithm based on shot segmentation and block classification." Multimedia Tools and Applications 62, no. 3 (August 10, 2011): 545–60. http://dx.doi.org/10.1007/s11042-011-0857-3.

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Guo, Saidi, Lin Xu, Cheng Feng, Huahua Xiong, Zhifan Gao, and Heye Zhang. "Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences." Medical Image Analysis 73 (October 2021): 102170. http://dx.doi.org/10.1016/j.media.2021.102170.

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