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In this paper, we use a vision-based image processing technique to propose a method of real-time one-dimensional barcode localization robust for rotation and scale. The proposed method consists of three main steps. The first step generates and analyzes an orientation histogram of input images and removes their background regions and small clutters. The second step analyzes a local entropy-based orientation...
Instead of using HOG feature on cells or blocks, the extraction of HOG features on corner points is proposed for multiple object visual tracking system in which single or multiple moving objects could be classified. Background subtraction and extraction of corner feature are applied to track and classify the moving objects. Firstly, moving objects will be detected in the form of regions from background...
Image co-segmentation is the problem of extracting the common objects from multiple images. Foreground segmentation is always effected by the diverse objects and complex background. However, the existing methods didn't pay much attention to images' background as object, especially the similar background. To address the similar scene co-segmentation problems, a method which considers the foreground...
This paper proposes a novel inherently rotation invariant local descriptor which combined intensity information and gradient information of key feature. The CS-LBP shows a better performance than SIFT and do not need large computation. To further enhance its performance and robustness, we calculated the gradient of key feature and computed a combined histogram included intensity and gradient information...
Steganography is a branch of computer science where original information bits are to be transmitted on some carrier file[1][7]. This carrier file may be audio file, video file or image file etc. Here in this proposed work, instead of embedding a source image in a carrier image, only stego key is generated on sender side and transmitted on the channel, with some logic this source image is reconstructed...
Lung segmentation is an important first step towards an automated CAD (Computer Aided Detection) system for a variety of medical applications. These applications range from lung nodule detection for identifying cancerous tumors to acinar shadow detection for identifying Tuberculosis. In our prior work we had used the Concave Hull algorithm for lung segmentation. However, our results showed over segmentation...
This paper presents an iterative motion estimation and error evaluation method for efficient occlusion detection. From a pair of reference and target images, the proposed method detects occlusions using four steps: (i) pre-processing for robust motion estimation, (ii) detection of candidates for the occlusion, (iii) one-dimensional motion estimation, and (iv) motion evaluation and update in the target...
We present a robust particle filter based visual tracker based on an earlier approach called mean-transform which can track a window with orientation and scale changes. This work is the first work combining sparse coding, mean transform and particle filtering in visual tracking. We show that particle filter is effective in enhancing the mean-transform tracker. From the result, we see that such architecture...
Local feature descriptor plays a fundamental role in many visual tasks, and its rotation invariance is a key issue for many recognition and detection problems. This paper proposes a novel rotation invariant descriptor by ordinal pyramid pooling of local Fourier transform features based on their radial gradient orientations. Since both the low-level feature and pooling strategy are rotation invariant,...
This paper proposes a frontal gait recognition system using a single camera, which is robust to changes in clothing and carrying condition. User silhouettes are derived from 2D plus depth (2.5D) sequences, using background subtraction. Silhouettes are integrated into a 3D point cloud, corresponding to a marching in place (MIP) representation of the sequence of observed silhouettes. Features are then...
Existing tracking approaches are design-varied. Several features have been proposed to describe low, mid and high level cues and used to drive frame to frame tracking correspondences under mainly two strategies: purely matching and discriminative matching. However, despite the enormous amount of approaches proposed, single object tracking is still an unresolved task. In this paper we explore a, to...
With the development of Internet and computer technology, the digital videos increase explosively, so how to get the interesting video clips from the massive video dataset quickly and efficiently has become an urgent problem in the field of information retrieval (IR). In this paper, a new Instance-based video search (INS) engine is proposed to solve the urgent problem. Firstly, video key-frames are...
Automatic gender classification of face images is an area of growing interest with multiple applications. Appropriate classifiers should be robust against variations such as illumination, scale and orientation that occur in real world applications. This can be achieved by normalizing the images in order to reduce those variations (alignment, re-scaling, histogram-equalization, etc.), or by extracting...
This paper explores the combining of powerful local texture descriptors and the advantages over single descriptors for texture classification. The proposed system is composed of three components: (i) highly discriminative and robust sorted random projections (SRP) features; (ii) a global Bag-of-Words (BoW) model; and (iii) the use of multiple kernel Support Vector Machines (SVMs) combining multiple...
Today many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (PDAs, mobile phones, etc.) take videos. However the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. A fundamental issue is the overall robustness with respect to different scene contents (indoor,...
We propose two novel algorithms for fully-unsupervised, super-fast, and cross-channel TV commercial mining in this paper. The tasks involved in the process include: 1) mining commercial clusters from streams of individual channels, and 2) grouping identical commercial clusters across multiple channels. The first process is achieved with a dual-stage hashing algorithm, which searches for recurring...
In this paper, a multilingual text detection method is proposed, which focus on finding all of the text regions in natural scene regardless of their language type. According to rules of writing system, three different texture features are selected to describe the multilingual text: histogram of oriented gradient (HOG), mean of gradients (MG) and local binary patterns (LBP). Finally, cascade AdaBoost...
Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like algorithm selection and parameterization...
This paper addresses the problem of object tracking by learning a discriminative classifier to separate the object from its background. The online-learned classifier is used to adaptively model object's appearance and its background. To solve the typical problem of erroneous training examples generated during tracking, an online multiple instance learning (MIL) algorithm is used by allowing false...
Content based image retrieval is an essential task in many image processing applications, among which, color based methods have been receiving constant attentions in past years, because color information is a discriminative descriptor for image retrieval, especially in case of large database. A limitation of previous color based methods is their unsuitability for retrieving similar scenes under varying...
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