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Anomaly detection in hyperspectral images aims at detecting small size objects of unknown spectra. The major problem with anomaly detection is the absence of prior knowledge. Consequently, the extraction of true anomalies from the background and noise is a challenging task. In fact, the image scene already contains the background, noises and anomalous pixels and even in presence of prior knowledge,...
Face alignment is one of the most popular research areas in computer vision. It can be used in many fields, such as foundation of 3D-model of a face, face swap, face recognition. But most methods proposed were based on single images. In this paper, several strategies are proposed to enhance the performance of face alignment in videos. Through these strategies, three improvements have been achieved,...
Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they...
In this paper, we address the problem of visual tracking in videos without using a pre-learned model of the object. This type of model-free tracking is a hard problem because of limited information about the object, abrupt object motion, and shape deformation. We propose to integrate an object-agnostic prior, called objectness, which is designed to measure the likelihood of a given location to contain...
This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic...
Object detection in high resolution remote sensing images is a crucial yet challenging problem for many applications. With the development of satellite and sensor technologies, remote sensing images attain very high spatial resolution, giving rise to the employment of many computer vision algorithms. Therefore, the object detection is usually formalized as a supervised classification task. In this...
Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number...
Searching through and selecting data sets from large traffic databases with sensor information is often a cumbersome manual process. In this paper we present an idea that may dramatically fasten and streamline this process. The idea is to build a fast search index (COSI: COngestion Search engIne) based on meta data in combination with features from the traffic patterns along routes. Instead of ploughing...
Metamorphic malware are able to change their appearance to evade detection by traditional anti-malware software. One of the ways to help mitigate the threat of new metamorphic malware is to determine their origins, i.e., the families to which they belong. This type of metamorphic malware analysis is not typically handled by commercial software. Moreover, existing works rely on analyzing the op-code...
This paper presents a robust machine learning based computational solution for human detection. The proposed mechanism is specifically applicable for pose-variant situations in video frames. In order to address the pose variance problem, features are extracted using an improved variant of Histograms of Gradients (HoG) and local Binary Pattern features (LBP). The two feature sets are combined to form...
We tackle the problem of reducing the false positive rate of face detectors by applying a classifier after the detection step. We first define and study this post classification problem. To this end, we first consider the multiple-stage cascade structure which is the most common face detection architecture. Here, each cascade stage aims to solve a binary classification problem, denoted the Face/non-Face...
Part-based models have demonstrated their merit in object detection. However, there is a key issue to be solved on how to integrate the inaccurate scores of part detectors when there are occlusions, abnormal deformations, appearances, or illuminations. To handle the imperfection of part detectors, this paper presents a probabilistic pedestrian detection framework. In this framework, a deformable part-based...
One of the pivotal issues which must be tackled when an effective brain-computer interface (BCI) is to be designed, is to reduce the enormous space of features extracted from fNIRS signals. BCI researchers often use genetic algorithms (GA) as the technique to extract features. The classic genetic algorithm obtains a feature set with the high classification accuracy; however, it is unable to create...
In order to distinguish falls from normal activities exactly, a fall early warning wearable detector combining angle with acceleration features was proposed in this paper. The detector consists of MEMS inertial sensor and smartphone. The application to solve classification algorithm using Support Vector Machine is developed. Experimental trials which young adults participated in involved 250 falls...
The number of times that a cow performs a walking can be indicative of the estrus, presence of lesions and welfare degree. In order to identify the characteristic movements associated with walking, it is common the use of inertial devices attached to the animal's leg. The procedure of the device's installation entails risks to humans, animals and to the device. This paper aims to clarify if the inertial...
During the last decades topics such as video analysis and image understanding techniques have experimented an important evolution due to its inclusion in applications such as surveillance, intelligent spaces and assisted living. In order to validate all related works different datasets have been distributed within the research community: CAVIAR, KTH, Weizmann, INRIA or MuHAVI are some of the most...
Detection of human beings in a complex background environment is a great challenge in the area of computer vision. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy we propose a new descriptor that fuses the local phase information, image gradient, and texture features...
Facial feature extraction is the process of extracting face component features like eyes, nose, mouth, etc from human face image. Facial feature extraction is very much important for the initialization of processing techniques like face tracking, facial expression recognition or face recognition. Among all facial features, eye localization and detection is essential, from which locations of all other...
To determine the real-time traffic state accurately in road network or intersections, traffic state identification method is proposed based on image processing technology. During the image process, by analyzing the image texture features, the multi-scale block local binary patterns are taken as the features. The road traffic state identification model is established based on support vector classification...
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a novel method for rotation invariant insulator detection. Rotation invariance is achieved by an efficient approach for estimating rotation angle of all insulator of an image. Sliding window based local directional pattern (LDP) feature is extracted from the image and support vector...
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