ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. These SoCs (Sy...
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ISBN:
(纸本)9781479989379
ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy imageprocessing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. These SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of imageprocessingalgorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.
Traditional computers data processing is limited by computer data input, output, storage, display. Further computing needs repeated binary-decimal conversions. With the expansion of data intensive computing needs of d...
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ISBN:
(纸本)9781509045570
Traditional computers data processing is limited by computer data input, output, storage, display. Further computing needs repeated binary-decimal conversions. With the expansion of data intensive computing needs of distributed computing, decimal computing of mass data is widely applied in banking, financial, signal processing, bio-medical, astronomy, geography, data acquisition and image compression and other fields. Independent decimal floating point unit is becoming important in these areas. A floating point unit is a part of a computer system specially designed to carry out operations on floating point numbers. Floating point unit have been implemented as a coprocessor rather than as an integrated unit in various systems. Today's floating point arithmetic operations are very important in the design of Digital Signal processing and application-specific systems. As Fixed-Point arithmetic logics are faster and more area efficient, but sometimes it is desirable to implement calculation using Floating-Point numbers. In most of the digital signal processing applications addition and multiplication is done frequently. This paper presents a review of the Floating Point unit for a signal processing applications, which has faster rate of operations.
In this paper, we describe the developed hovering-type AUV called "Cyclops" and discuss characteristics of imaging sonar DIDSON as an tool for AUV application. The Cyclops was designed to perform an advanced...
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ISBN:
(纸本)9788993215090
In this paper, we describe the developed hovering-type AUV called "Cyclops" and discuss characteristics of imaging sonar DIDSON as an tool for AUV application. The Cyclops was designed to perform an advanced mission like object recognition, and its symmetric design enable to maximize the mobility of the vehicle. This hardware structure makes the maintenance of the vehicle fast and convenient. We introduce sonar image process algorithms from simple to advanced ones for AUV application. The algorithm includes segmentation for the extraction of reverberation shapes in sonar images, speckle reduction after segmentation, edge detection, and shape matching analysis.
Numerous practical applications for automated event recognition in video rely on analysis of the objects and their associated motion, i.e., the kinematics of the scene. The ability to recognize events in practice depe...
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ISBN:
(纸本)9781628415766
Numerous practical applications for automated event recognition in video rely on analysis of the objects and their associated motion, i.e., the kinematics of the scene. The ability to recognize events in practice depends on accurate tracking objects of interest in the video data and accurate recognition of changes relative to the background. Numerous factors can degrade the performance of automated algorithms. Our object detection and tracking algorithms estimate the object position and attributes within the context of a dynamic assessment of video quality, to provide more reliable event recognition under challenging conditions. We present an approach to robustly modeling the image quality which informs tuning parameters to use for a given video stream. The video quality model rests on a suite of image metrics computed in real-time from the video. We will describe the formulation of the image quality model. Results from a recent experiment will quantify the empirical performance for recognition of events of interest.
We propose information processing techniques for CCTV based surveillance systems employed in (a) work environments and (b) public places and transport, for automated identification of scenes of inter-personal crime. A...
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ISBN:
(纸本)9781467385497
We propose information processing techniques for CCTV based surveillance systems employed in (a) work environments and (b) public places and transport, for automated identification of scenes of inter-personal crime. Although both the scenarios presented in this work employ similar signal processing and learning algorithms, the objective involved are significantly different. In (a) we aim to preserve confidentiality and privacy of official meetings and discussions, while ensuring detection of un-becoming behavior, like: bullying, harassment and assault. In the proposed method we identify such critical conditions using a combination of image and speech processing and ensue conditional video recording and saving. In (b), the target is to identify the occurrence of interpersonal crime using video and voice processing, in order to raise alert at the local surveillance station, which may be receiving numerous CCTV videos from neighboring areas. This can be an assistance to the security personnel, responsible to monitor large number of screens. The proposed methods can be useful curbing inter-personal violence, and crime against women, in the form of eve teasing, and harassment.
It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Henc...
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It is very important to scan the skin lesions in general population, and to forward suspicious cases to the dermatologists. Typically, the number of dermatologists is not enough for such a wide scanning campaign. Hence, an automated system and algorithms are required that can detect suspicious skin lesions. These systems should be highly accurate and sensitive. In this work, an automated system is developed to detect suspicious skin lesions from digital images using shape and color features. This system is tested on images in the PH2 database. The developed system has 91.5% accuracy, 92.5% sensitivity and 87.5% specificity.
This paper presents a robust hand detection algorithm using the facial information. The proposed algorithm consists of four steps: (i) detection of a face, (ii) generation of regions of interest (ROI) to detect hands,...
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This paper presents a robust hand detection algorithm using the facial information. The proposed algorithm consists of four steps: (i) detection of a face, (ii) generation of regions of interest (ROI) to detect hands, (iii) skin color extraction from the face region, and (iv) detection of hands using the face skin color in the ROI. The proposed algorithm can reduce false detection caused by a similar skin color, and provides a successful detection rate up to 92 percent.
Lane detection algorithm using a vision sensor or a camera would be more effective for self-driving vehicles to keep in lane, if it is possible to derive a distance ratio between a vehicle and left-right lanes. Howeve...
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ISBN:
(纸本)9781509032204
Lane detection algorithm using a vision sensor or a camera would be more effective for self-driving vehicles to keep in lane, if it is possible to derive a distance ratio between a vehicle and left-right lanes. However, a dangerous situation may occur if the performance of the camera (e.g., frame/sec.) and the real-time speed of the vehicle are not considered properly because of the huge distance difference among frames for a fast moving vehicle with a low-speed camera. In this study, we propose a simple method to anticipate the relative position of the vehicle in the following frame from the current frame image. The expected ratio between a vehicle and the left-right lanes can be obtained by using of the speed of a vehicle and the frame speed of a camera. Experiment results show that less than 5.28% error occurs by the proposed algorithm for various cars and cameras.
Network traffic associated with video increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications related with video, such as video sear...
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ISBN:
(纸本)9781509053827
Network traffic associated with video increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications related with video, such as video search, video fast browsing, video index and storage are in great demand. Behind these technologies and applications, a core problem is how to quickly browse massive video data and obtain the main content of the video. To solve this problem, different key frame extraction algorithms have been proposed. Due to the diversity of video content, different video have different characteristics. So the design of general video key frame extraction algorithm to solve the problem is not the reality. The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself. In this article, we mainly focus on videos with edited boundaries and shot conversions. Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF. In this algorithm, we use Gist feature to construct the global scene information of frames, and the SURF keypoint detection algorithm to extract local keypoints as local feature for each frame. Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots. After that, we select the most representative frames in each video shot as key frames. Finally we evaluate the result of the algorithm from the subjective and objective perspective. Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video.
The algorithms for dense correspondences in stereo images are an extensively researched topic, since it is an essential step in a large number of applications. Despite the fact that the first stereo matching algorithm...
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ISBN:
(纸本)9781509018185
The algorithms for dense correspondences in stereo images are an extensively researched topic, since it is an essential step in a large number of applications. Despite the fact that the first stereo matching algorithms were proposed some decades ago, novel approaches regarding typical, but also cutting-edge applications, are always in demand. Stereo matching is an inverse, ill-posed problem, which usually depends on the application and the scenario. In this contribution, a hybrid approach for stereo matching is proposed, which is based on graph-cuts optimization (global) and cross-based aggregation (local) under a hierarchical scheme. It is shown that the combined effect of a global method in a coarse layer and a local method in finer layers improves the matching results. This hybrid approach exploits the strengths and ameliorates the weaknesses of the individual global and local algorithms. The resulted disparity map is robust without outliers even in untextured areas and at the same time high fidelity details are accurately represented. This hybrid scheme is evaluated on challenging indoor datasets. It is also computationally efficient for applying it on low-processing power applications.
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