SCK - Sparse Coding Based Key-point Detectors for Knowledge Discovery

SCK - 基于稀疏编码的知识发现关键点检测器

• 批准号: RGPIN-2020-06051
• 负责人: Guan, Ling
• 金额: $ 4.01万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718218
• 关键词: SCK; Sparse; Coding; Based; Key

Detection of key-points or distinctive points in images is a very important step in numerous vision-based applications i.e. image matching, image stitching and object detection/recognition. The research in the field has seen successful introduction of many key-point detectors such as Harris, SIFT, SURF, MSER, SFOP. However, the very nature of being developed based on a human pre-defined structure (a corner, blob or junction, etc.) makes these classical (hand-crafted) detectors inflexible when being used in unintended situations. In recent years, deep learning has been applied to key-point detection. Yet, the main stream of learning based methods remains using hand-crafted detectors to provide input to their training algorithms, essentially making them hand-crafted as well. These facts clearly indicate that the development of new classical detectors is still an indispensable task. 1. We will analytically verify the property of scale and rotational invariance possessed by SCK. It is expected that, by incorporating this property, the application range of SCK will be significantly broadened to handle images of very different scales and rotational angles. 2. We will investigate how SCK can be used to enhance the available detectors, classical or learning based alike. By fusing SCK and the hand-crafted detectors, it is expected that the robustness of SCK against non-uniform lighting, scale and rotational changes will improve the quality of classical detectors. A learning based detector with SCK providing reliable input will also be studied. 3. We will propose and develop a new feature descriptor, to replace the popular SIFT descriptor which possesses limited information discrimination power. The new descriptor is expected to work coherently with the SCK detector. 4. We will apply the developed algorithms in the previous three tasks to multiple practical vision tasks, especially focusing on two areas in which we have high level of expertise and broad experience: non-invasive biometrics and multimedia information processing. The expected outcome of the proposed research is the generation of a novel and complete framework with associated tools for key-point detection and feature descriptor extraction. The gained knowledge will generate significant societal benefits to Canada and provide enabling technology for applied research such as image matching, image stitching, human-computer interaction, multimedia information retrieval, and solution to a broad range of real world problems, including autonomous vehicles, high quality photography, new media production/delivery, preservation of cultural heritage, e-health, smart homes, gaming and e-entertainment, and creating life-like experience in immersive multimedia environment.

图像中关键点或特征点的检测在许多基于视觉的应用中是非常重要的一步，例如图像匹配、图像拼接和目标检测/识别。该领域的研究已经成功地引入了许多关键点检测器，如Harris，SIFT，SURF，MSER，SFOP。然而，基于人类预定义的结构（角落、斑点或接合点等）开发的本质是不可能的。使得这些经典的（手工制作的）检测器在非预期情况下使用时不灵活。近年来，深度学习已被应用于关键点检测。然而，基于学习的方法的主流仍然是使用手工制作的检测器来为其训练算法提供输入，基本上也是手工制作的。这些事实清楚地表明，发展新的经典探测器仍然是一项不可或缺的任务。 1.我们将从理论上验证SCK所具有的尺度和旋转不变性。预计，通过结合这一属性，SCK的应用范围将显着扩大到处理非常不同的尺度和旋转角度的图像。 2.我们将研究如何SCK可以用来增强现有的检测器，经典的或基于学习的类似。通过融合SCK和手工制作的探测器，预计SCK对非均匀照明，尺度和旋转变化的鲁棒性将提高经典探测器的质量。一个基于学习的检测器与SCK提供可靠的输入也将被研究。 3.我们将提出并发展一个新的特征描述子，以取代目前流行的具有有限信息鉴别能力的SIFT描述子。新的描述符预期与SCK检测器相干地工作。 4.我们将把前三个任务中开发的算法应用于多个实际的视觉任务，特别是专注于我们拥有高水平专业知识和广泛经验的两个领域：非侵入性生物识别和多媒体信息处理。 所提出的研究的预期成果是生成一个新的和完整的框架与相关的工具，用于关键点检测和特征描述符提取。所获得的知识将为加拿大带来重大的社会效益，并为应用研究提供技术支持，如图像匹配，图像拼接，人机交互，多媒体信息检索，以及解决广泛的真实的世界问题，包括自动驾驶汽车，高品质摄影，新媒体制作/交付，文化遗产保护，电子健康，智能家居，游戏和电子娱乐，在沉浸式多媒体环境中创造逼真的体验。