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Automatic crater detection in planetary images is an important task with many applications in planetary science, spacecraft navigation, landing, and control. Typically, crater detection algorithms consist of two main steps: candidate crater region extraction and crater verification. Various methods have been proposed for extracting candidate crater regions, ranging from detecting circular/elliptical...
Action recognition has received increasing attention from the computer vision and machine learning communities in the last decade. To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet. Apart from the "in-the-wild" datasets, the training...
We present the performance of three popular image feature extraction methods such as Scale Invariant Feature Transformation (SIFT), Speeded-Up Robust Features (SURF) and Histogram of Oriented Gradient (HOG). Specifically, we compare the performance of feature detection methods for images corrupted with different types of noise. The efficiency of three methods are measured by considering number of...
The efficient use of satellite images for practical purposes depends upon the ability to combine them for the purposes of analysis. However, this process involves many computationally intensive computer vision algorithms. Assembling large images usually requires costly hardware with a large amount of memory. This paper addresses this problem by proposing a system that implements algorithms required...
One technical challenge in the field of computer vision is to acquire information in three dimensions from an arbitrary environment. There have been successful algorithms that focus on the precision and robustness, the 3D vision, or a generalized vision system, yet none of them performs very well in all these aspects and approach the level of human vision. The research problem of this project is to...
Robust stereo matching under radiometric changes is a necessary method to use stereo vision in a real situation. This paper proposes a fast and robust stereo matching under various radiometric changes by using Census transform. Experimental result shows that proposed method has a better performance in terms of computational time and bad pixel error.
Optical flow is one of the key components in computer vision research area. Since the seminal work proposed by Horn and Schunck [1], numerous advanced algorithms have been proposed. Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. However, despite their major advances over last decade, conventional optical flow methods...
Convolutional Neural Network (CNN) has been used successfully in solving different computer vision tasks such as classification, detection, and segmentation. This paper addresses the problem of estimating object depth from a single RGB image. While stereo depth estimation is a straightforward task, predicting depth map of an object from a single RGB image is a more challenging task due to the lack...
The development of a fully automated robotic endoscopic steering system has been an active area of research for more than a decade. This paper aims at proposing a hardware-efficient iterative thresholding strategy to locate the lumen region in captured endoscopic images in order to enhance traditional endoscopes with certain degree of autonomy and intelligence. The proposed method is characterized...
There exists a range of feature detecting and feature matching algorithms; many of which have been included in the Open Computer Vision (OpenCV) library. However, given these different tools, which one should be used? This paper discusses the implementation and comparison of a range of the library's feature detectors and feature matchers. It shows that the Speeded-Up Robust Features (SURF) detector...
In this paper we evaluate the performance of CNN in regards to face recognition for real world applications. In recent years, many high performance deep neural networks have been proposed to the face recognition world. These deep networks were trained by images provided by the internet, and they commonly are of good quality when facial expression and posture are not particularly complex. However,...
This study presented a position estimation and control method for quadrotor using optical flow and GPS sensors. Firstly, an optical flow based location algorithm is shown that calculates relative position from continues images. Considering the drift problem of optical flow, an improved Kalman filter based position estimation method is presented to enhance performance of location system. Then a PD...
One of the most challenging problems faced by tracking algorithms is the issue of template drift. For robust object tracking, template should be adaptive enough to incorporate maximum changes of target appearance, and at same time it should be restrictive enough to reject any background information entering into its model so that drifting of template may be avoided. The existing template updating...
The existing pedestrian counting methods use the various keypoint detectors but there is no attempt to find a suitable keypoint detector for counting pedestrians. Therefore, in this paper, we compare the various keypoint detectors using a public dataset. Our evaluation framework uses the processing time of keypoint detection and matching as a performance measure. Also, we use the accuracy of moving...
The extreme variability in the appearance of a place across the four seasons of the year is one of the most challenging problems in life-long visual topological localization for mobile robotic systems and intelligent vehicles. Traditional solutions to this problem are based on the description of images using hand-crafted features, which have been shown to offer moderate invariance against seasonal...
Philipona & O’Regan (2006) [1] recently proposed a linear model of surface reflectance as it is sensed by the human eyes. In their model, the tristimulus response to reflected light is accurately approximated by a linear transformation of the tristimulus response to illumination, allowing the prediction of several perceptual characteristics of human vision. Later, Vazquez-Corral et al (2012) [2]...
This paper proposed robust object tracking using error filter. Proposed method consists of four steps: i) execution of tracking algorithm based on key point, ii) estimation of key point using error filter, iii) selection of key point as filtering outliers using Random Sample Consensus (RANSAC), and determination of object movement. The proposed method can track when object is occluded, abruptly change...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
For Augmented Reality to succeed in industrial appliances, industries demand not only robust and reliable tracking techniques, but also a scalable and performant pipeline, that is easy-to-integrate within the existing data- and content environment and that enables vendors to create tracking solutions on their own. In our demo, we present recent advances of our model tracking pipeline and tracking...
Registration is an important task in augmented reality (AR) systems. For markerless AR, feature descriptors are generally used as a basis of registration process, which is expected to be robust for various application scenarios. This work aims at exploring effective schemes to improve the registration results, especially for applications with large viewpoint angles. Using the proposed scheme, the...
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