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In this paper we present a divisive hierarchical method for the analysis and segmentation of visual images. The proposed method is based on the use of the k-means method embedded in a recursive algorithm to obtain a clustering at each node of the hierarchy. The recursive algorithm determines automatically at each node a good estimate of the parameter k (the number of clusters in the k-means algorithm)...
In this paper, we propose an algorithm to track welding line of TiG welding in welding pipe or tube. The proposed algorithm consists of detecting edge, binarization, segmenting target area, locating welding torch and locating welding line. Firstly, we use prewitt operator to form the edge of image which is acquired from industrial camera. Secondly, we achieve the binary image by using maximal variance...
Advantages and disadvantages of two common algorithms frequently used in the moving target detection: background subtraction method and frame difference method are analyzed and compared in this paper. Then based on the background subtraction method, a moving target detection algorithm is proposed. The background image used to process the next frame image is generated through superposition of the current...
In this paper, we present a key point recognition scheme, which consists of a novel feature detector and an efficient descriptor. Inspired by FAST (features from accelerated segment test), our feature detector is easy to compute and has high repeatability. Scale-invariance and optimized robustness are gained by extending traditional FAST to scale space.We combine this detector with an adapted version...
Moving object detection is a very important research topic of computer vision and video processing areas. The process of moving object detection based on the background extraction is divided into two steps, background extraction and moving object detection. Improved method of obtaining background image based on common region is cited. The basic idea is to capture a series of video pictures of the...
Many camera lenses, particularly low-cost or wide-angle lenses, can cause significant image distortion. This means that features extracted naively from such images will be incorrect. A traditional approach to dealing with this problem is to digitally rectify the image to correct the distortion, and then to apply computer vision processing to the corrected image. However, this is relatively expensive...
Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance...
In this paper we present a Bayesian framework for segmenting images into their constituent visual patterns. The segmentation algorithm optimizes the posterior probability and outputs a scene representation as a hierarchical graph representation, in a spirit similar to stochastic grammars in natural language. This computational framework integrates two popular inference approaches-generative (top-down)...
Moving vehicle detection based on computer vision is an important aspect in the application of Intelligent Transportation Systems (ITS). How to get the accurate parameters of the vehicle in real-time, especially in complicated background situation is very critical. In this paper an improved detection algorithm of moving vehicle is proposed. According to the characteristics of some present detection...
Developing robust computer vision algorithms to detect fruit in trees is challenging due to less controllable conditions, including variation in illumination within an image as well as between image sets. There are two classes of techniques: local-feature-based techniques and shape-based techniques, which have been used extensively in this application domain. Out of the two classes, the local-feature-based...
In this paper we address the problem of recovering object contour in infrared video sequences using active contours and level set methods. We propose an approach for variational segmentation of infrared images containing non-rigid, moving objects. The local regions-of-interest (ROIs) are identified firstly using statistical background-subtraction method. While the initialization of background model...
A novel method is presented based on image segmentation and features point for stereo matching. Firstly, we analyse texture of the original image for distinguishing less texture and similar texture regions, as a result, we can achieve image segmentation by label image texture region. Meanwhile, we can remove smaller regions by blob filter; Then, SIFT features point and matching can achieve reliable...
Region of interest extraction is very important in video analysis and understanding. A simple and fast region of interest extraction method for sport scene images is proposed in this paper. Firstly, a simple method is applied to detect the interest pixels in images by the defined interest pixels extraction function and a simple strategy is applied to improve the computation speed of mean for each...
Accurately counting people waiting at bus stops is essential for automated bus fleet scheduling and dispatch. Estimating the passenger demand in regular open bus stops is a nontrivial problem because of the varying conditions, such as illumination, crowdedness, people poses, to name a few. This paper presents a simple, but very effective approach to estimate the passenger count using people density...
This paper presents a novel image based detection method for pedestrians at very small scales (between 16 ?? 20 and 32 ?? 40). We propose a set of new distinctive image features based on collections of local image gradients grouped by a superpixel segmentation. Features are collected and classified using AdaBoost. The positive classified features then vote for potential hypotheses that are collected...
Images segmentation is an important issue for many applications as pattern recognition and computer vision. Thresholding is an important and fast technique used in most applications. Gaussian Otsu's method is a thresholding technique based on between class variance. Gamma distribution models data more than Gaussian distribution. In this paper, we developed a new formula using Otsu's method for estimating...
Vehicle tracking is an essential requirement of any vision based Intelligent Transportation System for extracting different traffic parameters, efficiently. Handling inter-object occlusion is the most challenging part of tracking as a process of finding and following interested objects in a sequence of video frames. In this paper we present a system, based on code-book background model for motion...
This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally...
Visual attention detection is an important technique in many computer vision applications. In this paper, we propose an algorithm to extract a salient object from an image using bottom-up and top-down computations. In bottom-up computation, segment-based color contrast and attention values are employed to compose a bottom-up saliency map. In top-down computation, in-focus areas of the image are extracted...
Background subtraction is a widely used method for moving object detection in computer vision field. To cope with highly dynamic and complex environments, the mixture of models has been proposed. In this paper, a background subtraction method is proposed based on the popular Gaussian Mixture Models technique and a scheme is put forward to adaptively adjust the number of Gaussian distributions aiming...
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