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License plate detection and recognition are mostly studied on automobiles but only few on motorcycles. As motorcycles are becoming popular for local transportation and environmental friendliness, the demands for license plate recognition have been increasing in recent years. The primary difference between the license plate recognition in automobiles and motorcycles is on the detection of license plates,...
Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. Pedestrians are key participants in transportation systems, so pedestrian detection in video surveillance systems is of great significance...
The algorithms for computerized identification are used in different applications: from quality inspection up to complex security systems. The purpose of this paper is to investigate and present the results of applying methods for automated identification and classification of labeled chemicals. Also, a new method based on the histogram matching is investigated and compared to the known ones.
Recent studies show that the performances of face recognition systems degrade in presence of makeup on face. In this paper, a facial makeup detector is proposed to further reduce the impact of makeup in face recognition. The performance of the proposed technique is tested using three publicly available facial makeup databases. The proposed technique extracts a feature vector that captures the shape...
Obstacle detection for advanced driver assistance systems has focused on building detectors for only a few number of object categories so far, such as pedestrians and cars. However, vulnerable obstacles of other categories are often dismissed, such as wheel-chairs and baby strollers. In our work, we try to tackle this limitation by presenting an approach which is able to predict the vulnerability...
There has been an increased interest in the field of abnormal human activity detection to find a good descriptor with a lower computational cost. In this paper, we propose such a Spatio-Temporal Descriptor (STD) based on spatio-temporal features of an image sequence. Proposed descriptor is based on a texture map, known as Spatio-Temporal Texture Map (STTM) and is based on 3-dimensional Harris function...
Different methods employed for object detection are widely exploited covering application areas such as traffic monitoring, video surveillance and capturing various human activities and motion. The traditional methods that have earlier been proposed for detection are found to be beneficial if the detected object is properly identified. Moreover, minimizing the effect of dynamic changes as well as...
To evade detection by network-based anomaly detectors, sophisticated attackers are trying to make their malicious traffic resemble legitimate traffic by running attacks through ports used on a daily basis (e.g., port 80 for HTTP). This mimicry traffic is potentially neglected by detectors. In this paper, we propose a Kullback-Leibler (KL) divergence-based method for detecting anomalous traffic mimicking...
One fundamental problem in Simultaneous Localization and Mapping is place recognition, used to create loop closures, necessary to improve map quality. Ideally, the sensor data alone should be used to detect loop closures, referred to as appearance-based methods. Such methods usually employ feature descriptors, especially popular in underwater research are 2D visual features, and the bag-of-words approach...
Microcalcification (MC) detection in mammograms can be hampered by a number of factors ranging from imaging noise to inhomogeneity in breast tissue. Consequently, owning to the variability among subjects in their mammograms, the detection accuracy often varies from case to case even for a well-developed MC detector. To account for this variability, we propose to use a Bayes' risk approach to define...
We address the task of object localisation in 2D fetal ultrasound images, where invariance to factors such as image contrast and object orientation is desirable. We build on recent methods for rotation-invariant detection and combine them with oriented measures of image structure derived from the monogenic signal. We test our approach on images containing the fetal heart. Our results suggest that...
Persistent network communication can be found in many instances of malware. In this paper, we analyse the possibility of leveraging low variability of persistent malware communication for its detection. We propose a new method for capturing statistical fingerprints of connections and employ outlier detection to identify the malicious ones. Emphasis is put on using minimal information possible to make...
A subband-DCT approach for image watermarking is proposed in this communication. The watermark is casted in a selected number of coefficients of all four bands of a one-level decomposition. A great number of coefficients is being used. Each band gives a different detection output. The result is taken as the average detection result of all bands. It is shown that the final result is better than the...
Local detail features of face are important bases for recognizing different persons. For its invariant to monotonic gray-scale transformations and it's a non-parametric kernel which summarizes the local special structure of an image, the Local Binary Pattern (LBP) has becoming a popular technique for face representation. In this paper, the LBP technique and its application for representing faces are...
Voice Activity Detectors (VAD) play important role in audio processing algorithms. Most of the algorithms are designed to be causal, i.e. to work in real time using only current and past audio samples. Off-line processing, when we have access to the entire voice utterance, allows using different type of approaches for increased precision. In this paper we propose an algorithm for off-line VAD based...
Anomaly detection in a network is important for diagnosing attacks or failures that affect the performance and security of a network. Lately, many anomaly detection techniques have been proposed for detecting attacks whose nature is strange. A process for extracting useful features is implemented in the anomaly detection framework. Standard matrices are applied for measuring the operation of the anomaly...
An increasing number of photos in Internet photo collections comes with watermarks, timestamps, or frames (in the following called WTFs) embedded in the image content. In image retrieval, such WTFs often cause false-positive matches. In image clustering, these false-positive matches can cause clusters of different buildings to be joined into one. This harms applications like landmark recognition or...
Understanding how human emotion is evoked from visual content is a task that we as people do every day, but machines have not yet mastered. In this work we address the problem of predicting the intended evoked emotion at given points within movie trailers. Movie Trailers are carefully curated to elicit distinct and specific emotional responses from viewers, and are therefore well-suited for emotion...
We propose a primary visual cortex inspired oriented edge feature for object pose estimation. The neural feedback like feature is based on a Center-Surround Contrast excitation and a k-Winner-Take-AU inhibition, to extract different orientations of edge response from an image patch. To compute local descriptor, we model each oriented edge response with a PDF distribution, before concatenating their...
Nowadays pedestrian detectors are fast, scale-robust and quite efficient. Embedded within a UAV such a detector would open new possibilities. In this paper the very well known HOG detector is adapted for UAV use and a new kind of training dataset is proposed in order to increase the detector's angular robustness. A more appropriate set of detection windows, together with a new detection pipeline,...
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