2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)最新文献_第2页

Visual Object Tracking in Spherical 360° Videos: A Bridging Approach 球形360°视频中的视觉对象跟踪:桥接方法

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290549

Simon Finnie, Fang-Lue Zhang, Taehyun Rhee

引用次数: 1

Automatic Identification of Diatom Morphology using Deep Learning 基于深度学习的硅藻形态自动识别

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290564

Dana Lambert, R. Green

引用次数: 3

A machine learning approach for image retrieval tasks 图像检索任务的机器学习方法

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290617

Achref Ouni

引用次数: 1

Development of a Virtual Environment Based Image Generation Tool for Neural Network Training 基于虚拟环境的神经网络训练图像生成工具的开发

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290491

R. Arenas, P. Delmas, Alfonso Gastelum-Strozzi

{"title":"Development of a Virtual Environment Based Image Generation Tool for Neural Network Training","authors":"R. Arenas, P. Delmas, Alfonso Gastelum-Strozzi","doi":"10.1109/IVCNZ51579.2020.9290491","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290491","url":null,"abstract":"We present a computational tool to generate visual and descriptive data used as additional training images for neural networks involved in image recognition tasks. The work is inspired by the problem posed to acquire enough data, in order to train service robots, with the goal of improving the range of objects in the environment with which they can interact. The tool provides a framework that allows users to easily setup different environments with the visual information needed for the training, accordingly to their needs. The tool was developed with the Unity engine, and it was designed to be able to import external prefabs. These models are standardized and catalogued into lists, which are accessed to create more complex and diverse virtual environments. Another component of the tool adds an additional layer of complexity by creating randomized environments with different conditions (scale, position and orientation of objects, and environmental illumination). The performance of the created dataset was tested by training the information on the YOLO-V3 (You Only Look Once) architecture and testing on both artificial and real images.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"118 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":"133913845","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

Image and Text fusion for UPMC Food-101 using BERT and CNNs 基于BERT和cnn的UPMC Food-101图像和文本融合

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290622

I. Gallo, Gianmarco Ria, Nicola Landro, Riccardo La Grassa

引用次数: 11

Melanoma and Nevi Classification using Convolution Neural Networks 使用卷积神经网络进行黑色素瘤和痣分类

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290736

R. Grove, R. Green

{"title":"Melanoma and Nevi Classification using Convolution Neural Networks","authors":"R. Grove, R. Green","doi":"10.1109/IVCNZ51579.2020.9290736","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290736","url":null,"abstract":"Early identification of melanoma skin cancer is vital for the improvement of patients’ prospects of five year disease free survival. The majority of malignant skin lesions present at a general practice level where a diagnosis is based on a clinical decision algorithm. As a false negative diagnosis is an unacceptable outcome, clinical caution tends to result in a low positive predictive value of as low at 8%. There has been a large burden of surgical excisions that retrospectively prove to have been unnecessary.This paper proposes a method to identify melanomas in dermoscopic images using a convolution neural network (CNN). The proposed method implements transfer learning based on the ResNet50 CNN, pretrained using the ImageNet dataset. Datasets from the ISIC Archive were implemented during training, validation and testing. Further tests were performed on a smaller dataset of images taken from the Dermnet NZ website and from recent clinical cases still awaiting histological results to indicate the trained network’s ability to generalise to real cases. The 86% test accuracy achieved with the proposed method was comparable to the results of prior studies but required significantly less pre-processing actions to classify a lesion and was not dependant on consistent image scaling or the presence of a scale on the image. This method also improved on past research by making use of all of the information present in an image as opposed to focusing on geometric and colour-space based aspects independently.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"8 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":"116126667","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

Heating Patterns Recognition in Industrial Microwave-Processed Foods 工业微波加工食品的加热模式识别

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290639

Sowmya Kasturi, S. L. Moan, D. Bailey, Jeremy Smith

引用次数: 0

Isotropic Remeshing by Dynamic Voronoi Tessellation on Voxelized Surface 体素化表面上动态Voronoi镶嵌的各向同性网格重划分

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290614

Ashutosh Soni, Partha Bhowmick

{"title":"Isotropic Remeshing by Dynamic Voronoi Tessellation on Voxelized Surface","authors":"Ashutosh Soni, Partha Bhowmick","doi":"10.1109/IVCNZ51579.2020.9290614","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290614","url":null,"abstract":"A novel algorithm for isotropic remeshing of a triangle mesh is presented in this paper. The algorithm is designed to work on a voxelized surface and integrates several novel ideas. One such is the notion of functional partitioning that aids in uniform distribution of seeds for initializing the process of dynamic Voronoi tessellation (DVT). The concept of DVT is also novel and found to be quite effective for iteratively transforming the input mesh into an isotropic mesh while keeping the tessellation aligned with the surface geometry. In each iteration, a Voronoi energy field is used to rearrange the seeds and to recreate the DVT. Over successive iterations, the DVT is found to keep on improving the mesh isotropy without compromising with the surface features. The Delaunay triangles corresponding to the final tessellation are further subdivided in high-curvature regions. The resultant mesh is finally projected back onto the original mesh in order to minimize the Hausdorff error. As our algorithm works in voxel space, it is readily implementable in GPU. Experimental results on various datasets demonstrate its efficiency and robustness.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"24 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":"121795086","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

Wavefront reconstruction with the cone sensor 锥形传感器的波前重建

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290735

R. Clare, B. Engler, S. Weddell

{"title":"Wavefront reconstruction with the cone sensor","authors":"R. Clare, B. Engler, S. Weddell","doi":"10.1109/IVCNZ51579.2020.9290735","DOIUrl":"https://doi.org/10.1109/IVCNZ51579.2020.9290735","url":null,"abstract":"Wavefronts of light from celestial objects are aberrated by Earth’s evolving atmosphere, causing images captured by ground-based telescopes to be distorted. The slope of the phase of the wavefront can be estimated by a pyramid wavefront sensor, which subdivides the complex field at the focal plane of the telescope, producing four images of the aperture. The cone wavefront sensor is the extension of the pyramid sensor to having an infinite number of sides, and produces an annulus of intensity rather than four images. We propose and compare the following methods for reconstructing the wavefront from the intensity measurements from the cone sensor: (1) use the entire aperture image, (2) use the pixels inside the intensity annulus only, (3) create a map of slopes by subtracting the slice of annulus 180 degrees opposite, (4) create x and y slopes by cutting out pseudo-apertures around the annulus, and (5) use the inverse Radon transform of the intensity annulus converted to polar co-ordinates. We find via numerical simulation with atmospheric phase screens that methods (1) and (2) provide the best wavefront estimate, methods (3) and (4) the smallest interaction matrices, while method (5) allows direct reconstruction without an interaction matrix.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"26 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":"126263357","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

PProCRC: Probabilistic Collaboration of Image Patches for Fine-grained Classification PProCRC:用于细粒度分类的图像补丁的概率协作

2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Pub Date : 2020-11-25 DOI: 10.1109/IVCNZ51579.2020.9290537

Tapabrata (Rohan) Chakraborty, B. McCane, S. Mills, U. Pal

引用次数: 1