2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)最新文献_第5页

Deep Learning Methods for Human Behavior Recognition 人类行为识别的深度学习方法

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290640

Jia Lu, M. Nguyen, W. Yan

引用次数: 8

Evaluating Learned State Representations for Atari 评估Atari的学习状态表示

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290609

Adam Tupper, K. Neshatian

{"title":"Evaluating Learned State Representations for Atari","authors":"Adam Tupper, K. Neshatian","doi":"10.1109/IVCNZ51579.2020.9290609","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290609","url":null,"abstract":"Deep reinforcement learning, the combination of deep learning and reinforcement learning, has enabled the training of agents that can solve complex tasks from visual inputs. However, these methods often require prohibitive amounts of computation to obtain successful results. To improve learning efficiency, there has been a renewed focus on separating state representation and policy learning. In this paper, we investigate the quality of state representations learned by different types of autoencoders, a popular class of neural networks used for representation learning. We assess not only the quality of the representations learned by undercomplete, variational, and disentangled variational autoencoders, but also how the quality of the learned representations is affected by changes in representation size. To accomplish this, we also present a new method for evaluating learned state representations for Atari games using the Atari Annotated RAM Interface. Our findings highlight differences in the quality of state representations learned by different types of autoencoders and their robustness to reduction in representation size. Our results also demonstrate the advantage of using more sophisticated evaluation methods over assessing reconstruction quality.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129522291","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

Vehicle-Related Scene Segmentation Using CapsNets 使用capnet的车辆相关场景分割

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290664

Xiaoxu Liu, W. Yan, N. Kasabov

引用次数: 4

Defects Detection in Highly Specular Surface using a Combination of Stereo and Laser Reconstruction 结合立体和激光重建的高高光表面缺陷检测

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290660

Arpita Dawda, M. Nguyen

{"title":"Defects Detection in Highly Specular Surface using a Combination of Stereo and Laser Reconstruction","authors":"Arpita Dawda, M. Nguyen","doi":"10.1109/IVCNZ51579.2020.9290660","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290660","url":null,"abstract":"Product inspection is an indispensable tool of the current manufacturing process. It helps maintain the quality of the product and reduces manufacturing costs by eliminating scrap losses [1]. In the modern era, the inspection process also needs to be automatic, fast and accurate [2]. “Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis [3].” However, highly specular (mirror-like) surfaces are still proven to be the limitation of many state-of-art three-dimensional (3D) reconstruction approaches. The specularity of the outer surface makes it difficult to 3D reconstruct the product model accurately. Along with accurate measurements, it is also essential to detect defects such as dents, bumps, cracks and scratches present in a product. As these defects are palpable and are not visible by the camera, it is tough to detect them using vision-based inspection techniques in ambient lighting conditions. This paper presents an automated defect detection technique using the concepts of laser line projection and stereo vision. This research activity came up as an evolution of a previous study in which, the ideas of stereo-vision reconstruction and laser line projection were used, for accurate 3D measurement of highly specular surfaces. In this paper, the detection of three defect types (Dents, Scratches and Bumps) are examined in ambient lighting conditions. In the end, the output 3D profile of the defected product is compared with the non-defective product for accuracy evaluation.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128370790","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: 2

Class Probability-based Visual and Contextual Feature Integration for Image Parsing 基于类概率的图像分析视觉与上下文特征集成

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290686

Basim Azam, Ranju Mandal, Ligang Zhang, B. Verma

{"title":"Class Probability-based Visual and Contextual Feature Integration for Image Parsing","authors":"Basim Azam, Ranju Mandal, Ligang Zhang, B. Verma","doi":"10.1109/IVCNZ51579.2020.9290686","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290686","url":null,"abstract":"Deep learning networks have become one of the most promising architectures for image parsing tasks. Although existing deep networks consider global and local contextual information of the images to learn coarse features individually, they lack automatic adaptation to the contextual properties of scenes. In this work, we present a visual and contextual feature-based deep network for image parsing. The main novelty is in the 3-layer architecture which considers contextual information and each layer is independently trained and integrated. The network explores the contextual features along with the visual features for class label prediction with class-specific classifiers. The contextual features consider the prior information learned by calculating the co-occurrence of object labels both within a whole scene and between neighboring superpixels. The class-specific classifier deals with an imbalance of data for various object categories and learns the coarse features for every category individually. A series of weak classifiers in combination with boosting algorithms are investigated as classifiers along with the aggregated contextual features. The experiments were conducted on the benchmark Stanford background dataset which showed that the proposed architecture produced the highest average accuracy and comparable global accuracy.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129632402","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

Fast Portrait Segmentation of the Head and Upper Body 头部和上身的快速肖像分割

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290654

S. Loke, B. MacDonald, Matthew Parsons, B. Wünsche

{"title":"Fast Portrait Segmentation of the Head and Upper Body","authors":"S. Loke, B. MacDonald, Matthew Parsons, B. Wünsche","doi":"10.1109/IVCNZ51579.2020.9290654","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290654","url":null,"abstract":"Portrait segmentation is the process whereby the head and upper body of a person is separated from the background of an image or video stream. This is difficult to achieve accurately, although good results have been obtained with deep learning methods which cope well with occlusion, pose and illumination changes. These are however, either slow or require a powerful system to operate in real-time. We present a new method of portrait segmentation called FaceSeg which uses fast DBSCAN clustering combined with smart face tracking that can replicate the benefits and accuracy of deep learning methods at a much faster speed. In a direct comparison using a standard testing suite, our method achieved a segmentation speed of 150 fps for a 640x480 video stream with median accuracy and F1 scores of 99.96% and 99.93% respectively on simple backgrounds, with 98.81% and 98.13% on complex backgrounds. The state-of-art deep learning based FastPortrait / Mobile Neural Network method achieved 15 fps with 99.95% accuracy and 99.91% F1 score on simple backgrounds, and 99.01% accuracy and 98.43 F1 score on complex backgrounds. An efficacy-boosted implementation for FaceSeg can achieve 75 fps with 99.23% accuracy and 98.79% F1 score on complex backgrounds.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"478 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140386","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

Machine Learning with Synthetic Data – a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time 基于合成数据的机器学习——一种实时学习和分类图像增强现实标记的新方法

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290606

H. Le, M. Nguyen, W. Yan

{"title":"Machine Learning with Synthetic Data – a New Way to Learn and Classify the Pictorial Augmented Reality Markers in Real-Time","authors":"H. Le, M. Nguyen, W. Yan","doi":"10.1109/IVCNZ51579.2020.9290606","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290606","url":null,"abstract":"The idea of Augmented Reality (AR) appeared in the early 60s, which recently received a large amount of public attention. AR allows us to work, learn, play, and connect with the world around us both virtually and physically in real-time. However, picking the AR marker to match the users’ needs is one of the most challenging tasks due to different marker encryption/decryption methods and essential requirements. Barcode AR cards are fast and efficient, but they do not contain much visual information; pictorial coloured AR card, on the other hand, is slow and not reliable. This paper proposes a solution to obtain detectable arbitrary pictorial/colour AR cards in real-time by applying the benefit of machine learning and the power of synthetic data generation techniques. This technique solves the issue of labour-intensive tasks of manual annotations when building a massive training dataset of deep-learning. Thus, with a small number of input of the AR-enhanced target figures (as few as one for each coloured card), the synthetic data generated process will produce a deep-learning trainable dataset using computer-graphic rendering techniques (ten of thousands from just one image). Second, the generated dataset is then trained with a chosen object recognition convolutional neural network, acting as the AR marker tracking functionality. Our proposed idea works effectively well without modifying the original contents (of the chosen AR card). The benefits of using synthetic data generated techniques help us to improve the AR marker recognition accuracy and reduce the marker registration time. The trained model is capable of processing video sequences at approximately 25 frames per second without GPU Acceleration, which is suitable for AR experience on the mobile/web platform. We believed that it could be a promising low-cost AR approach in many areas, such as education and gaming.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116886257","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

Automatically localising ROIs in hyperspectral images using background subtraction techniques 利用背景减法技术自动定位高光谱图像中的roi

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290728

Munir Shah, V. Cave, Marlon dos Reis

引用次数: 2

A Tip-Tilt Mirror Control System for Partial Image Correction at UC Mount John Observatory 加州大学约翰山天文台部分图像校正的倾斜镜控制系统

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290543

Jiayu Liu, Vishnu Anand Muruganandan, R. Clare, María Celeste Ramírez Trujillo, S. Weddell

引用次数: 1

Plant Trait Segmentation for Plant Growth Monitoring 植物性状分割用于植物生长监测

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290575

Abhipray Paturkar, G. S. Gupta, D. Bailey

引用次数: 3