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In the present work, automatic corner detection in soccer games based on image features (e.g., object-based features) has been studied. For this purpose, a framework has been proposed that consists of five steps. This paper mainly focuses on the first three steps and specially ball detection step. Ball position on the field plays an important role in determining which event has occurred in the game...
Mathematical morphology supplies powerful tools for low level image analysis, with applications in many areas. In this paper, the development of a novel reconfigurable hardware using a genetic algorithm and a pipeline architecture is proposed for the task of shape recognition in binary images. For the recognition process, a large sized convex structuring element representing the object shape to be...
Human movement analysis is a long-studied, but still important and challenging research area in visual surveillance. It involves many fundamental problems in computer vision such as human detection, segmentation and tracking, and higher level problems such as human gesture, action and event recognition. Shape is the most dominant cue for detecting humans due to large appearance variability. In this...
In this paper, we address the problem of estimating the 3D structure and motion of a non-rigid object based on feature points throughout a image sequence. The main limitation of existing factorization methods is that they are difficult to provide correct structure and motion estimates: the motion matrix has a repetitive structure which is not represented by these methods. In order to cope with this...
Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By “component” we refer to a rectangular subregion of the face, containing an anatomical component (e.g., “eye”). We present a fully-automated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bag-of-words...
This work introduces techniques to facilitate large-scale Augmented Reality (AR) experiences in unprepared outdoor environments. We develop a shape-based object detection framework that works with limited texture and can robustly handle extreme illumination and occlusion issues. The contribution of this work is a purely geometric approach for detecting marker-like objects under difficult and realistic...
The objective is to develop a probabilistic approach for vision-based fire detection in videos. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these...
An approach toward pedestrian detection applied to natural images using improved Random Forest (RF) is proposed. We take a more discriminative method for object part detection by applying the feature of pixel-based. We firstly train a pedestrian random forest which directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. For a testing image...
Recently, detection schemes of specific phenomena, called continuous objects, such as wild fire and bio-chemical material have been actively studied. A continuous object might cover a wide area and it could dynamically change its own shape according to physical environments, e.g. wind and geographical features. For continuous object detection, the schemes have focused on detection of the current boundary...
Non-rigid object detection is a challenging open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative...
In this paper , we present a on-line machine vision system for Hami big jujubes' size and shape detection, which solved the problems such as fast processing the large amount of image information, improving system performance for real time dynamic image capture and processing capability, increasing precision of detection etc. Based on the application of Visual Basic 6.0 designed the user's interface...
The objective of the proposed work is object position estimation, in which the system, after training with examples of images including objects such as cars, should be capable of indicating accurately by coordinates. The method is different from simple object detection, since it uses the context, i.e. the whole image. The key idea is to take an approach with Relevance Vector Machine (RVM) since it...
In this paper, we address the problem of vehicle detection and tracking with low-angle cameras by combining windshield detection and feature points clustering, effectively fusing several primitive image features such as color, edge and interest point. By exploring various heterogenous features and multiple vehicle models, we achieve at least two improvements over the existing methods: higher detection...
In this work, a combination of artificial neural network (ANN), Fourier descriptors (FD) and spatial domain analysis (SDA) has been proposed for the development of an automatic fruits identification and sorting system. Fruits images are captured using digital camera inclined at different angles to the horizontal. Segmentation is used for the classification of the preprocessed images into two non-overlapping...
In wireless sensor networks, reliable event detection is one of the most important research issues. For the reliable event detection, existing studies usually assume that events are individual objects such as tanks, people, and animals. The existing studies estimate detection reliability of an individual object by the ratio of the number of successfully received data packets at a sink to the number...
In this paper, we solve the problem of human detection in crowded scenes using a Bayesian 3D model based method. Human candidates are first nominated by a head detector and a foot detector, then optimization is performed to find the best configuration of the candidates and their corresponding shape models. The solution is obtained by decomposing the mutually related candidates into un-occluded ones...
Human figure identification is always a challenging move in field of pattern recognition. This paper presents a complete algorithm to find a single object (human body) and identify the object as human being. The algorithm starts the segmentation process with basic frame difference method and use morphological operators, edge detection, feature point generation and finally spline interpolation to find...
Developing usable technologies for indoor target detection is currently a topic of great interest. Ultra wide-band (UWB) radar is promising in this regard because of its high range-resolution. However, conventional UWB radar imaging systems are costly and impractical since they require a large antenna array to obtain high spatial resolution. This study proposes a new imaging method for a simple UWB...
This paper proposes a new method to detect objects in images. Boundaries contain shape of the objects. To detect objects in cluttered images, we use boundary fragments. Boundary fragments are obtained by our new training procedure. Poisson equation is used to divide edges and extract generic model of the object. Gaussian Mixture Model (GMM) is used to model shape of the object. This creates relation...
The authors are researching about Three dimensional mapping using mobile robot and 3-D laser scanner. We developed 3-D laser scanner which can measure whole 3-D shape with a combination of 2-D laser scanner and Pan-Tilt base. However, measuring surrounding areas using the scanner on mobile robot, there are some areas where can't measure whole 3-D shape caused by overview camera and wireless LAN's...
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