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Pedestrian recognition is one of the key components for assisted and autonomous driving. So far many researchers have investigated systems combining a high density LIDAR with cameras or stereo, which results in an expensive and complex setup where the LIDAR data is mostly used to extract regions of interest for the 2D sensor. Very few work has focused on using pure 3D data coming from the LIDAR to...
The recognition of human activities in the field of video surveillance is attracting more researchers. This has led to various approaches and proposals using different methods and techniques. The growing interest in the surveillance has also led researchers to give importance to abnormal human activities in order to propose appropriate and dedicated techniques to this type of activities. Unfortunately,...
In social network analysis, the fundamental idea behind the notion of roles is to discover actors who have similar structural signatures. Actors performing the same role have similar behavioural and functional characteristics. Few examples of structural roles are bridge nodes, clique members and star centers. Role discovery involves partitioning the nodes in a network based on their structural characteristics...
This paper presents an improved reversible data hiding algorithm using digital images based on the histogram shifting technique. Proposed method can accurately recover the original image and extract the hidden data accurately. The highest two peak values of the host image's histogram are selected for data hiding. This embedding process is repeated again and again, to attain larger embedding capacity...
Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named...
Session Initiation Protocol (SIP), which is an IP based telephony protocol, is used mainly for the purpose of starting, sustaining and ending sessions related to multimedia communications on the Internet. The SIP protocol, which works on the top of TCP or UDP, is basically an open text-based protocol. Hence, to ensure security is of utmost importance. The original SIP used HTTP-digest based challenge-response...
LSB substitution steganography only takes the least significant bits in the carrier into account, which has the problems of low security and poor robustness. This paper proposes a self-contained steganography combining the MSB matching and LSB substitution. It contains two types of encoding rules to define the matching result between the secret information binary stream and the most two-significant-bit...
The robustness and invisibility is the most important evaluating indicator of digital watermarking system. Besides, the data that must be analyzed and calculated is large for the video watermark. Thus, for the video watermark, watermarking algorithm should not only have strong robustness but also have low time complexity. In this paper, we put forward the idea to implement a frequency domain digital...
LSB techniques generally embed data in the same LSB position of consecutive samples which helps intruders to extract secret information easily. This paper solve this problem by introducing a robust audio steganography technique where data is embedded in multiple layers of LSB chosen randomly and in non-consecutive samples. The choice of random LSB layers and non-consecutive pixels for embedding increases...
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...
Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant...
This paper proposes an original method for extracting the centerline of 3D objects given only partial mesh scans as input data. Its principle relies on the construction of a normal vector accumulation map build by casting digital rays from input vertices. This map is then pruned according to a confidence voting rule: confidence in a point increases if this point has maximal votes along a ray. Points...
High-throughput technologies have enabled us to rapidly accumulate a wealth of diverse data types. These multi-view data contain much more information to uncover the cluster structure than single-view data, which draws raising attention in data mining and machine learning areas. On one hand, many features are extracted to provide enough information for better representations, on the other hand, such...
Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Skypatterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures...
Steganographic techniques are used to transmit secret information using a carrier file without visibility during communication. In this paper, we proposed an improved RGB image steganographic technique for secured communication between two authorized parties. This technique embeds the information within 2nd to 8th bit position of blue (B) or green (G) component or both of a pixel throughout the blue...
Outlier detection is a key technique in data ming and machine learning fields. The deviating characters of outliers make huge detrimental effects on the learning tasks. A lot of algorithms are therefore proposed to handle outliers from different perspectives, such as distance, density, angle and so on. Among these approaches, the density-based methods achieve better performance, but also suffer from...
This paper proposes an inaudible and robust audio-information-hiding scheme based on the singular-spectrum analysis (SSA) and a psychoacoustic model. SSA is used to decompose the host signals into several additive oscillatory components. The hidden information is embedded into the host signals by modifying amplitudes of some oscillatory components. To satisfy the inaudibility, we propose a novel method...
Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Intuition about clustering reflects the ideal case – exact data sets endowed with flawless dissimilarity between individual instances. In practice however, these cases are in the minority, and clustering applications are typically characterized by noisy data sets with approximate pairwise dissimilarities...
In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require...
Neighborhood Covering Reduction extracts rules for classification through formulating the covering of data space with neighborhoods. The neighborhoods of covering are constructed based on distance measure and strictly constrained to be homogeneous. However, this strategy over focuses on boundary samples and thus makes the neighborhood covering model sensitive to noise. To tackle this problem, we construct...
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