Curve Parameterization and Curvature via Method of Hurwitz-Radon Matrices

2011 ◽  
Vol 16 (1-2) ◽  
pp. 49-56 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Object Recognition ◽  
Explicit Form ◽  
Local Maximum ◽  
Parametric Representation ◽  
Feature Points ◽  
Implicit Representation ◽  
Computer Vision Applications ◽  
Interpolation Nodes

Curve Parameterization and Curvature via Method of Hurwitz-Radon MatricesParametric representation of the curve is more appropriate in computer vision applications then explicit formy=f(x)or implicit representationf(x, y) = 0. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in parameterization and interpolation of curves in the plane. Suitable parameterization leads to curvature calculations. Points with local maximum curvature are treated as feature points in object recognition and image analysis. This paper contains the way of curve parameterization and computing the curvature in the range of two successive interpolation nodes via MHR method. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. It is shown how to create the orthogonal OHR and how to use it in a process of curve parameterization and curvature calculation.

Object Recognition with a Limited Database Using Shape Space Theory

2012 ◽  
pp. 128-147
Author(s):  
Yuexing Han ◽  
Bing Wang ◽  
Hideki Koike ◽  
Masanori Idesawa
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Shape Space ◽  
Image Understanding ◽  
Data Models ◽  
Hard Work ◽  
Space Theory ◽  
Single Object ◽  
Intermediate Data ◽  
Computer Vision Applications

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has been deeply developed for the last three decades, and a lot of approaches have been proposed. Generally, these methods of object recognition can successfully achieve their goal by relying on a large quantity of data. However, if the observed objects are shown to diverse configurations, it is difficult to recognize them with a limited database. One has to prepare enough data to exactly recognize one object with multi-configurations, and it is hard work to collect enough data only for a single object. In this chapter, the authors will introduce an approach to recognize objects with multi-configurations using the shape space theory. Firstly, two sets of landmarks are obtained from two objects in two-dimensional images. Secondly, the landmarks represented as two points are projected into a pre-shape space. Then, a series of new intermediate data can be obtained from data models in the pre-shape space. Finally, object recognition can be achieved in the shape space with the shape space theory.

scholarly journals Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

2020 ◽  
Vol 7 ◽  
Author(s):  
Arthur Francisco Araújo Fernandes ◽  
João Ricardo Rebouças Dórea ◽  
Guilherme Jordão de Magalhães Rosa
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Computer Vision Applications ◽  
Animal Sciences

Vision-Guided Control Algorithms for Micro Aerial Vehicles

2013 ◽  
Vol 33 (1) ◽  
pp. 271-288
Author(s):  
Mirosław Wijaszka
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Micro Air Vehicles ◽  
Control Algorithms ◽  
Feature Points ◽  
Analysis Method ◽  
Hypothetical Model ◽  
Stability And Control ◽  
Constant Altitude ◽  
And Control

AbstractThis study presents an image analysis method used in the vision guided control system for Micro Air Vehicles (MAVs). The paper describes a hypothetical model of a MAV located in the GPS-denied unknown environment, somewhere indoors. The model keeps moving autonomously following ‘the track’ marked with corners and other feature points recorded with a monocular camera pointed at the far end of a corridor and slightly tilted down at the angle β (20° - 30°). The flight stability and control are provided with an on-board autopilot that maintains zero pitch and roll angles and constant altitude. The image analysis has been based on the real-time computer vision library - OpenCV (Open Source Computer Vision library - http://opencv.willowgarage.com).

OBJECT CLASSIFICATION AND OCCLUSION HANDLING USING QUADRATIC FEATURE CORRELATION MODEL AND NEURAL NETWORKS

2011 ◽  
Vol 25 (02) ◽  
pp. 287-298 ◽  
Author(s):  
NA FAN
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Object Recognition ◽  
State Of The Art ◽  
Feature Points ◽  
Correlation Model ◽  
Occlusion Handling ◽  
Occluded Objects ◽  
Feature Correlation

Occlusion handling is an old but important problem for the computer vision and pattern recognition community. Features from different objects may twist with each other, and any matched feature points may belong to different objects for many traditional object recognition algorithms. To recognize occlusions, we should not only match objects from different view points but also match features extracted from the same object. In this paper, we propose a method to consider these two perspectives simultaneously by encoding various types of features, such as geometry, color and texture relationships among feature points into a matrix and find the best quadratic feature correlation model to fit them. Experiments on our own built dataset and the publicly available PASCAL VOC dataset shows that, our method can robustly classify objects and handle occluded objects under large occlusions, and the performance is among the state-of-the-art.

Object Recognition with a Limited Database Using Shape Space Theory

2013 ◽  
pp. 181-200
Author(s):  
Yuexing Han ◽  
Bing Wang ◽  
Hideki Koike ◽  
Masanori Idesawa
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Shape Space ◽  
Image Understanding ◽  
Data Models ◽  
Hard Work ◽  
Space Theory ◽  
Single Object ◽  
Intermediate Data ◽  
Computer Vision Applications

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has been deeply developed for the last three decades, and a lot of approaches have been proposed. Generally, these methods of object recognition can successfully achieve their goal by relying on a large quantity of data. However, if the observed objects are shown to diverse configurations, it is difficult to recognize them with a limited database. One has to prepare enough data to exactly recognize one object with multi-configurations, and it is hard work to collect enough data only for a single object. In this chapter, the authors will introduce an approach to recognize objects with multi-configurations using the shape space theory. Firstly, two sets of landmarks are obtained from two objects in two-dimensional images. Secondly, the landmarks represented as two points are projected into a pre-shape space. Then, a series of new intermediate data can be obtained from data models in the pre-shape space. Finally, object recognition can be achieved in the shape space with the shape space theory.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

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