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We report on our experience with model-based testing using SpecExplorer within the Flat X-Ray Detection (FXD) Department of Philips Healthcare. Our initial experiments showed a practical obstacle in combining traditional functional testing techniques with model-based testing using SpecExplorer. We overcome this obstacle by specifying the constraints on our data domain in a spreadsheet and interfacing...
A hierarchical model for collision detection of convex polyhedral is presented. This model is based on double representation: the exterior is composed by the minimum outer boxes that envelope the polyhedral; it is an Axis-Aligned Bounding Boxes (AABB), and the interior is composed by the maximum inner boxes that is included inside the polyhedral. Inner boxes allow faster testing of overlapping detection...
In order to be deployed in real-world driving environments, autonomous vehicles must be able to recognize and respond to exceptional road conditions, such as highway workzones, because such unusual events can alter previously known traffic rules and road geometry. In this paper, we present a set of computer vision methods which recognize the bounds of a highway workzone and temporary changes in highway...
Java objects are required to honor an equality contract in order to participate in standard collection data structures such as List, Set, and Map. In practice, the implementation of equality can be error prone, resulting in subtle bugs. We present a checker called EQ that is designed to automatically detect such equality implementation bugs. The key to EQ is the automated extraction of a logical model...
This paper investigates the reliable detection of information embedded with the least significant bits (LSB) matching scheme. It is aimed to design a test with analytically predictable error probabilities. To this end, the problem of hidden information detection is cast in the framework of hypothesis testing theory. In order to deal with nuisance parameters a rejection approach is used with a statistical...
This paper proposes a novel speed-limit sign detection and recognition method by using only gray-level information. This method has a real-time processing ability to remind drivers about the speed limit when they are driving on roads, and it contains four main processing modules: speed-limit sign detection, speed-limit sign segmentation, speed-limit sign recognition and system integration. For detecting...
Vehicle detection in traffic scenes is a fundamental task for intelligent transportation system and has many practical applications as diverse as traffic monitoring, intelligent scheduling and autonomous navigation. In recent years, the number of detection approaches in monocular images has grown rapidly. However, most of them focus on detecting other objects (such as face, pedestrian, cat, dog, etc...
We consider a decentralized detection problem in a power-constrained wireless sensor networks (WSNs), in which a number of sensor nodes collaborate to detect the presence of a deterministic vector signal. The signal to be detected is assumed known a priori. Each sensor conducts a local linear processing to convert its observations into one or multiple messages. The messages are conveyed to the fusion...
The ambiguity function is an important tool to study the performance of radar detectors. In this paper, motivated by Neyman-Pearson testing principles, we propose an alternative definition of the ambiguity function that directly associates with each pair of true and assumed target parameters the probability that the radar will declare a target present. We show that the original ambiguity function...
The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling...
Instruction Detection System can automatically detect and analyze all kinds of data for finding instruction behavior. This essay firstly proposes a new design idea that combining the Snort with NTOP, and then introduces the implementation of the system, lastly it is verified by experiment. The result proves that the instruction behavior can be detected effectively by this system.
View classification for cardiac MR images is a new topic in medical image analysis, and can support efficient content-based filtering, browsing, and retrieval. The major difficulty lies in large variability in image appearance caused by various acquisition protocols, heart phases and disease conditions. We propose a collaborative learning approach that exploits statistical dependencies at three levels:...
Semiparametric detection consists of combining the statistical optimality of a parametric test to the robustness regarding the data of a nonparametric test. This approach is specially interesting in presence of statistical hypotheses depending on unknown probability distributions. The proposed semiparametric approach consists of splitting the measurement vector into two parts such that the first part...
Error pattern detection is very helpful in Computer-Aided Pronunciation Training (CAPT). This paper reports the work of modeling and detecting Error Patterns defined by language teachers based on their linguist knowledge and pedagogical experiences. We develop a model generation framework to create the Error Pattern models from existing phoneme models. We also propose a serial structure for integrating...
For video annotation refinement, a reasonable concept correlation representation is crucial. In this paper, we present a data-specific concept correlation estimation procedure for this task, where the resulting correlation with respect to each data encodes both its visual and high-level characteristics. Specifically, this procedure comprises two major modules: concept correlation basis estimation...
In object detection, the offline trained detector's performance may be degraded in a particular deployed environment, because of the large variation of different environments. In this work, we propose a data level object detector adaptation method to new environments. By recording a small amount of offline data, it's fully compatible with offline training method and easy to implement. We re-derive...
In this paper, normal factor graph (NFG) based probabilistic inference approach for the cooperative spectrum sensing in cognitive radio (CR) is presented. Spectrum sensing problem is modeled as binary hypothesis testing problem. We have formulated the joint probability function with all latent and manifest variables which describe the system. Then decompose the joint distribution function into simpler...
In some sport training application, it is necessary to search the key frames of training video for carefully analysis. In this paper, we take the key frame searching issue as a pose estimation problem. First, a set of various pose detectors are collected trough the twice SVM training process, each of which can be interpreted as a learned pose-specific HOG weight classifier. Then we run each linear...
Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate. Then, the system should self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome...
A simple and efficient skin detector facilitates automatic and robust human detection and tracking. In this paper, we propose a new skin detection method via linear regression tree, which decomposes the problem of discriminating different skin and nonskin colors into several simple problems. Experimental results on the MCG skin database demonstrated its better generalization ability and discriminability...
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