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Intrinsic image decomposition is an important topic in computer vision and computer graphics applications. However, this is a challenging problem by adopting the information of a single image. Therefore, additional priors or supplementary information such as multiply images or user interactions are necessary to address this problem. In this paper, we propose a novel scheme to use multiple images for...
Similarity measuring plays as an import role in stereo matching, whether for visual data from standard cameras or for those from novel sensors such as Dynamic Vision Sensors (DVS). Generally speaking, robust feature descriptors contribute to designing a powerful similarity measurement, as demonstrated by classic stereo matching methods. However, the kind and representative ability of feature descriptors...
At the last decades, face analysis remains a challenging research topic in the computer vision area. Beyond the visible band, infrared images had shown several advantages for face detection and recognition. From the proposed approaches for analyzing these images, the local analysis is recognized by its feasibility to overcome typical undesirable conditions such as noise, illumination, and affine transformations...
A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features...
The primary task in preprocessing image is moving estimation and compensation of image senor, that is to say, correction problem of image background. In this paper, a new method of motion background compensation based on robust regression is proposed. The background motion velocity is calculated by the estimation of the optical flow field model. Then robust iterative weighted least square method is...
In order to solve the issues of robustness recognition in ARToolkit system, this paper focuses on two main factors: illumination and motion. Firstly, a self-adaptive illumination algorithm is proposed based on the analysis of previous work. By establishing relationship between illumination and grayscale threshold, the algorithm can dynamically adjust the grayscale threshold, thus effectively improve...
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based...
Gaussian Mixture Model (GMM) and its variations process images by per pixel, so they may be corrupted by noises and the computational cost is high. In this paper, we propose a robust moving object detection algorithm with a background dictionary learning. To do this, we first divide an image into multiple image patches that have the same sizes. Each patch is the object or background. Then, A background...
Hand gesture recognition is an important topic in human-computer interaction. However, most of the current methods are complicated and time-consuming, which limits the use of hand gesture recognition in real-time circumstances. In this paper, we propose a data fusion-based hand gesture recognition model by fusing depth information and skeleton data. Because of the accurate segmentation and tracking...
Moving shadow detection is an important task in computer vision, with applications several fields, such as surveillance, video conference, visual tracking and object recognition. In this paper, we present a Spatio-Temporal based Moving Shadow Detection (STMSD) method, The main idea is: according to the gradient change of current image, we utilize the watershed algorithm to achieve adaptive segmentation...
Many emerging motion-related applications, such as virtual reality, decision making, and health monitoring, demand reliability and quick response upon input changes. Motion capture has been a well-researched topic in the past decades with applications in many industries. The ability to capture motion goes hand in hand with real-time capability in a system. This paper gives an overview on real-time...
Reliable banknote recognition is critical for detecting counterfeit banknotes in ATMs and help visual impaired people. To solve this problem, it was implemented a computer vision system that can recognize multiple banknotes in different perspective views and scales, even when they are within cluttered environments in which the lighting conditions may vary considerably. The system is also able to recognize...
Corresponding points matching is a one of the primitive problems in computer vision and image processing which is used in a vast verity of applications such as stereo vision, image registration, object detection, motion analysis and image retrieval. In this paper, we present a new framework to improve the performance of interest points matching using not only the feature descriptors of keypoints,...
In this paper, a new shaped marker is designed for augmented reality applications that has 360 degree viewing angle about its shaft axis. The main advantage of the designed marker is that it is very simple to extract marker area from images with basic image processing methods. And decoding of the marker codes is very simple with basic mathematical functions. Experimental results showed that, the designed...
Palm Vein Identification(PVI) systems have been attracting interests from academia, industry, and governments for their advantages such as identification accuracy and relative low costs. However, low cost Infrared (IR) camera sensors produce noisy images which degrades the robustness of these systems. This paper proposes a new PVI system that uses a mirror based stereo camera setup to increase the...
Computer vision has become the tool of two-dimensional image recognition and analysis, which mainly means extracting image features. But the problem of robustness and real-time property in complex scenarios makes feature extraction become a challenging task. Visual attention is an important psychological adjustment mechanism in the process of human visual information management, under the guidance...
Object detection and identification is a fundamental workflow in Computer vision. In this paper I am presenting a feature based approach to detect an object in cluttered scene using “Speeded Up Robust Features (SURF) and to identify object in real time manner using Bag-of-words (BoW) model. The System trains the model with different supervised Machine learning classifiers like Support Vector Machine...
In this paper, the comparison of a novel key-point image descriptors such as DAISY, BRISK, A-KAZE and LATCH with the well-known SIFT and SURF descriptors are tested and compared for the stereo matching algorithm. The main idea of this paper is to present an independent, comparative study and some of the benefits and drawbacks of these most popular image descriptors on stereo images. These descriptors...
For traditional computer vision methods the analysis of motion and behaviours in crowded scenes constitutes a challenging task, as barriers like occlusions, varying crowd densities and complex stochastic nature of their motions are difficult to overcome. As it has to be kept within reasonable limits, the one more complicating factor is the computational cost. It is very crucial to analyse crowded...
This paper presents the design and implementation of a Vision based Helipad detection algorithm in an aerial image by image processing techniques. The aerial image obtained from the on board camera of Unmanned Aerial Vehicle (UAV) was processed by several image processing techniques to remove the noise and to segment the helipad. Then this image was matched with a pre-loaded template of the Helipad...
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