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This paper presents an efficient image exploration scheme for the unshaped object using semantic modelling. The local regions of an image have been classified with respect to the frequency of occurrences. The semantic concept is evaluated using RGB histogram dissimilarity factor, overall dissimilarity factor and regional dissimilarity factor. The dissimilarities determine the local concept with accuracy...
Good performance of pedestrian detection in an automatic driving system is a necessary task. Many pedestrian detection algorithm use Histogram Oriented Gradient (HOG) for feature extraction and Support Vector Machine (SVM) for classification. Some papers use additional features with HOG, such as Local Binary Pattern (HOG-LBP), to improve the performance. Neural Network and Extreme Learning Machine...
This paper addresses two contributions for improving the accuracy and speed of preceding car detection systems. First, it proposes a feature description using Scalable Histogram of Oriented Gradient (SHOG) to solve scale problem of car region on the image. Without resizing the images to a fixed size, it is capable to extract a high-discriminated features with on the same feature space. Second, instead...
'Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by...
The Pedestrian detection using Histograms of Oriented Gradients (HOG) is the most popular method to detect a human from a picture. However, it, calculates the HOG description, will cost too much time and can't meet the real-time request for detecting pedestrian from the video surveillance system. In this paper we present a novel algorithm for detecting a human from a video. Firstly, The improved approach...
Pedestrian detection plays important roles in various applications such as automobile driving assistance and surveillance camera system. The co-occurrence histograms of oriented gradients (CoHOG) feature descriptor showed good performance since thirty co-occurrences at each pixel position represent various spatial characteristics of object shapes. Though extraction of co-occurrence histogram features...
This paper proposes a new information fusion approach that employs two information components for mobile landmark recognition, which includes: content analysis and context analysis. Existing landmark recognition works are mainly based on PC platform, which uses content analysis alone for recognition, and thus has a large computation cost and cannot satisfy mobile users' fast response time requirements...
This paper is concerned with “structural” change detection in pair of images. This is a challenging and open problem since the difficulties stemming from the confusion between real changes (depending on the objects/structures inside the images) and visual changes (observed through the difference in terms of image luminance) are numerous. We propose to solve this labeling problem as the minimization...
Auto-focusing is a key feature of modern camera systems that ensures a focused image is captured without the need for any user intervention. Passive auto-focusing is achieved based on a measure of sharpness or a sharpness function that is extracted from a series of images at different focal positions. With the rapid growth of stereo camera systems for 3D applications, this paper introduces a sharpness...
In this paper, we propose a novel method to implement fast detection of Common Visual Pattern (CVP). The purpose of CVP detection is to find the correspondences between the common visual regions of two given partial duplicate images. There are two major components of the proposed method which guarantee the good performance. First, we establish the Radiate-Geometric-Model (RGM). The RGM is represented...
A new steganographic algorithm is proposed for JPEG images by modifying the block DCT coefficients. Firstly, an embedding algorithm called LSB+ matching is generated to approximately preserve the marginal distribution of DCT coefficients. We further divide the DCT coefficients into four frequency bands, including the direct current (DC), low-frequency, middle-frequency, and high-frequency. Via matrix...
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,...
Hyperspectral images consist of large number of spectral bands but many of which contain redundant information. Therefore, band selection has been a common practice to reduce the dimensionality of the data space for cutting down the computational cost and alleviating from the Hughes phenomenon. This paper presents a new technique for band selection where a sparse representation of the hyperspectral...
Discriminating computer generated graphics from photographic images is a challenging problem of digital forensics. An important approach to this issue is to explore usual image statistics. In this way, when the statistical distributions (i.e., histograms) of some types of residual images are established, previous works usually apply operations on these histograms or compute statistical quantities...
In state-of-the-art image retrieval systems, an image is represented by bag-of-features (BOF). As BOF representation discards geometric relationships among local features, exploiting geometric constraints as post-processing procedure has been shown to greatly improve retrieval precision. However, full geometric constraints are computationally expensive and weak geometric constraints have limited range...
This paper presents a novel approach towards identity management strategies for application in large scale interactive systems while separating pure detection from the identity management processes. Detection is achieved by employing a scalable, modal, network-based, real-time multi-camera tracking system in which numerous cameras are used to cover large areas. Objects are detected by employing blob...
We address the problem of abandoned object classification in video surveillance. Our aim is to determine (i) which feature extraction technique proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features), and (ii) how the resulting features affect classification accuracy and false positive...
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