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The paper presents two advanced methods for comparative study in the field of computer vision. The first method involves the implementation of the Scalar Invariant Fourier Transform (SIFT) algorithm for the leaf recognition based on the key descriptors value. The second method involves the contour-based corner detection and classification which is done with the help of Mean Projection algorithm. The...
Botnet detection systems that use Network Behavioral Analysis (NBA) principle struggle with performance and privacy issues on large-scale networks. Because of that many researchers focus on fast and simple bot detection methods that at the same time use as little information as possible to avoid privacy violations. Next, deep inspections, reverse engineering, clustering and other time consuming approaches...
Input Injections are considered as the most common and effective vulnerabilities to exploit in many software systems (esp. web apps). In this paper, we propose a way to detect such vulnerabilities, such as SQL injection, command injection, and cross-site scripting. Input injection is caused by executing user inputs which have not been validated or sanitized, so that the purpose of execution is changed...
Emotion conversion using a small speech corpus is very important for expressive text to speech systems. Applying the unit selection paradigm for intonation conversion has been widely used for different languages using different intonation units. In this paper, an emotion conversion system is proposed for expressive Arabic speech. This system combines the transformation of both spectral and prosodic...
In this paper, we implement a facial landmarking system to improve the performance of landmark location accuracy for the tree-structured based facial detector proposed recently by Zhu and Ramanan. Our main objective is to overcome their limitation where very small faces could not be detected and landmarked. Furthermore, we also want to improve the landmarking accuracy and reduce false positive rate...
The Data Quality Monitoring (DQM) Software is a central tool in the CMS experiment. Its flexibility allows for integration in several key environments: online, for real-time detector monitoring; offline, for the final, fine-grained data certification; release validation, to constantly validate the functionalities and the performance of the reconstruction software; in Monte Carlo productions. Since...
Early detection of vascular lesions such as exudates, hemorrhages, and tumors in the retina is important in early detection of diabetes, hypertension, and cancer. For example, diabetic retinopathy is the leading cause of blindness in adults in the United States and the presence of exudates in fundus imagery is an early sign of diabetic retinopathy. In this paper we present a novel technique to automatically...
In order to realize a smart home to recognize human activities of daily living (ADL), it is necessary to develop a human activity recognition technology. This study proposes a method of awareness of human activities by detecting state change of room equipment, which includes doors, chairs, desks, a light and a fridge. A characteristic of the proposed method is an indirect recognition method in the...
Up till now there does not exist an easy, mobile mechanism that allows to easily capture, recognize and count defined, multiple objects that are visible in surroundings of the user. For this purpose, feature detectors (such as SIFT, SURF or BRISK) are utilized to create a database of products box images and extracted keypoints are stored. Existing algorithms based on keypoints analysis do not allow...
In this paper, a simple and fast algorithm was proposed to detect face. Firstly, some interest points marking skin regions were searched by only using simple chrominance Cr information instead of simultaneously using chrominance Cr and Cb. And then, a conventional and popular AdaBoost algorithm was employed to make a decision whether around the detected interest points was there face and then located...
Many intelligent systems are required to deal with the situation of human-computer interaction. As one of the most important front ends, gender classification plays an irreplaceable role. For practical use, a real-time robust gender classification system is presented in this paper. The system consists of three principal modules: image preprocessing, face detector and gender classifier. To enhance...
This paper proposes a novel machine-learning framework for facial-expression recognition, which is capable of processing images fast and accurately even without having to rely on a large-scale dataset. The framework is derived from Support Vector Machines (SVMs) but distinguishes itself in three key ways. First, the measure of the samples normalization is based on the Perturbed Subspace Method (PSM),...
Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept...
Automatic detection of cells which are in outage has been identified as one of the key use cases for Self Organizing Networks (SON) for emerging and future generations of cellular systems. A special case of cell outage, referred to as Sleeping Cell (SC) remains particularly challenging to detect in state of the art SON because in this case cell goes into outage or may perform poorly without triggering...
In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual...
Although eye detection has been studied for a long time in academic and industrial communities, it is still a changeling problem if facial images are with varying head poses, facial expressions, illuminations and resolution changes etc., which tend to happen in uncontrolled conditions. In this work, we propose to learn deep features that could capture the appearance variations of eyes for eye detection...
Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training...
In this work we present a system that enables automatic estimation of Pain from image sequences with frontal views of faces. The system uses facial characteristic points to characterize different Action Units (AU) of pain and is able to operate in cluttered and dynamic scenes. Geometric features are computed using 22 facial characteristic points. We use k-NN classifier for classifying AU. Only action...
In this study we evaluate the potential of local binary descriptors for automatic sorting in an industrial context. This problem is different from that of retrieval for human handling as we need to identify the one correct class, rather than finding all the similar classes. We have looked at classes of objects that need to be identified by their cover or label, rather than their shape. Challenges...
Face is a highly utilized biometric, and 3D modality is preferred due to better handling of variations such as pose and illumination. However, occlusions covering the face alter the 3D surface and degrade the recognition performance. To improve recognition rates, the occluded parts should be detected prior to any surface comparison. In this paper, we consider two different occlusion detection approaches:...
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