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This paper presents a novel and robust descriptor, depth-projection-map-based bag of contour fragments, which is applied to extraction of hand shape and structure information from depth maps. Our method projects depth maps onto three orthogonal planes to generate the depth projection maps. Then, the bag of contour fragment descriptors are extracted from the three depth projection maps and concatenated...
Text data present in scene images may be the important clue for indexing, automatic footnote, and indexing of images. Now-a-days extraction of text from images has become one of the fastest growing research areas in the field of computer vision. In scene images, text data are present with huge variations in font sizes, styles, alignments, and orientations. These variations make the task of detection...
Due to the wide variety of copy videos, the existing video copy detection methods using single feature face great challenges, especially for video content matching, which are difficult to deal with various copy video transformations. To overcome this problem, a video copy detection method based on sparse representation of MPEG-2 spatial and temporal features is proposed in this paper. Firstly, the...
Person re-identification plays a key role in video monitoring. Aiming for current person re-identifications for numerical complexity and extraction difficulty, we propose a simple and fast multi-feature. On the basis of analysis of difference excitation and orientation of Weber Local Descriptors, showing graphic texture features by difference excitation of circular field, showing graphic edge orientation...
Text localization in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we proposed a novel and effective approach to accurately localize scene texts. Firstly, Maximally stable extremal regions(MSER) are extracted as letter candidates. Secondly, after elimination of non-letter candidates by using geometric information, candidate regions are...
In this paper, we present novel approach for text extraction. In computer vision r esearch area, text is very important in images. Here we use edge based extraction of text using ISEF (infinit e symmetrical edge filter). ISEF is optimal edge det ector which gives accurate results for text in imag es. Text extraction involves detection, localization, tracking and enhancement. Large numbers of te chnique...
This paper presents a novel design of visual servo control of a mobile manipulator for autonomous grasping of a target object. In this design, scale invariant feature transform (SIFT) algorithm is adopted to search and recognize the object to grasp. Random sample consensus (RANSAC) algorithm is used to remove outliers and find the refined homography matrix between database and current image. Robust...
This paper proposes a novel and effective method to match objects between multiple cameras, which is based on wavelet salient features consisting of color information and salient values at salient points. Learning templates are set up and updated based on salient features in one camera, and used to match objects in the other camera without any geometric constraints. Experiments show the effectiveness...
We propose a Near-Duplicate Keyframe (NDK) retrieval method that can handle extreme zooming and significant object motion. The first stage consists of eliminating false keypoint matches using symmetric property and a ratio of nearest and second-nearest neighbor distances. Then, a pattern coherency score is assigned to each pair of keyframes. These two features are combined through linear discriminant...
This paper presents image description and matching scheme for identical image searching. In the proposed extraction scheme, an image is described by spatial and statistical features, and these features are combined. The concentric square partition is used for spatial feature. And three-transforms are adapted for statistical feature. The spatial feature is formed to binary code by hashing and the statistical...
SIFT (scale invariant feature transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (speeded-up robust features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents...
Text separation in natural scenes is a crucial step to recognize scene text. Since computational power in a mobile device is limited, current text extraction methods are impractical in real-time devices. We propose efficient text extraction methods by utilizing user's indication. When user simply indicates focus or draws the line on touch screen, the system can extract text in natural scenes efficiently...
One of the challenges to creating robust trackers is the construction of robust appearance Model. This paper presents a robust appearance model for object tracking. The robust object distribution is acquired by comparing the two Gaussian Mixture Models of the object and background. The probability image generated by the robust object distribution is used for the CAMSHIFT tracking. Experiments on several...
In this paper, we describe the design of a mosaicing technique for images from a microscope system with automatically controlled object stage and image capture unit. Due to the limited field of view in microscope imagery, larger objects are split up into many adjacent, but slightly overlapping frames. In many fields, such as medicine or biology, it is vastly beneficial that these image patches are...
This paper presents an evaluation of the SIFT (scale invariant feature transform), Colour SIFT, and SURF (speeded up robust feature) descriptors on very low resolution images. The performance of the three descriptors are compared against each other on the precision and recall measures using ground truth correct matching data. Our experimental results show that both SIFT and colour SIFT are more robust...
Mobile robots rely on their ability of scene recognition to build a topological map of the environment and perform location-related tasks. In this paper, we describe a novel lightweight scene recognition method using an adaptive descriptor which is based on color features and geometric information for omnidirectional vision. Our method enables the robot to add nodes to a topological map automatically...
Object tracking is important for video analysis applications. However, tracking through occlusions is a difficult task due to significant appearance changes of the objects. Approaches based on either global features or one kind of local features can not solve the problem completely. In this paper, a multi-cue based tracking approach is introduced. It combines a corner tracking with a color and a shape...
In this paper, we address these challenges in real-world unconstrained environments where the background is complex and dynamic. In the algorithm proposed, we extract the features in a color space, accumulate the feature information over a short time, and fuse high-level knowledge and low-level feature information. A fuzzy technique is also developed to detach silhouettes of moving objects from a...
In this paper, we propose a new method based on wavelet transform, statistical features and central moments for both graphics and scene text detection in video images. The method uses wavelet single level decomposition LH, HL and HH subbands for computing features and the computed features are fed to k means clustering to classify the text pixel from the background of the image. The average of wavelet...
Robust extraction of text from scene images is essential for successful scene text recognition. Scene images usually have non-uniform illumination, complex background, and existence of text-like objects. The common assumption of a homogeneous text region on a nearly uniform background cannot be maintained in real applications. We proposed a text extraction method that utilizes user's hint on the location...
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