We present a working system for real-time tracking of multiple laboratory animals. As it is usually possible to ensure good contrast between the animals and the background, the tracking of a single animal or several p...
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We present a working system for real-time tracking of multiple laboratory animals. As it is usually possible to ensure good contrast between the animals and the background, the tracking of a single animal or several physically separated animals can be obtained by relatively simple algorithms. The main problem arises when we try to track several almost identical, uniformly coloured animals during their contacts. To deal with this problem, we utilize dynamic information extracted by estimating sparse optical flow along the object contours. Optical flow vectors are used for updating the positions of the tracked contours in a sequence of image frames. The local properties of optical flow enable the system to track the objects during their contact, although some parts of the object contours become hidden. The missing dynamic information is reconstructed by using a model of constant optical flow along an object contour. The reconstructed contours are then adjusted to real object boundaries in the current frame by using an active contour model. The robustness of the tracking algorithm is improved by adding a supervision module, which detects tracking failures and reinitialises the contours that lose their targets. The system has been tested on real sequences with laboratory animals during pharmacological experiments and has been shown to be robust and efficient. Future extensions will include expert knowledge of biomedical and pharmacological experts. The major goal is to build a system that will provide an objective and standardised tool for evaluation of animal behaviour during experiments.
Charge-Coupled Device (CCD) cameras are widely used imaging sensors in computer vision systems. Many photometric algorithms, such as shape from shading, color constancy and photometric stereo, implicitly assume that t...
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ISBN:
(纸本)0769511430
Charge-Coupled Device (CCD) cameras are widely used imaging sensors in computer vision systems. Many photometric algorithms, such as shape from shading, color constancy and photometric stereo, implicitly assume that the image intensity is proportional to scene radiance. The actual image measurements deviate significantly from this assumption since the transformation from scene radiance to image intensity is non-linear and is a function of various factors including: noise sources in the CCD sensor, as well as various transformations occurring in the camera including: white balancing, gamma correction and automatic gain control. This paper illustrates how careful modeling of the error sources and the various processing steps enable us to accurately estimate the "response function", the inverse mapping from image measurements to scene radiance for a given camera exposure setting. It is shown that the estimation algorithm outperforms the calibration procedures known to us in terms of reduced bias and variance. Further, we demonstrate how the error modelling helps us to obtain uncertainty estimates of the camera irradiance value. The power of this uncertainty modeling is illustrated by a vision task involving High Dynamic Range image generation followed by change detection. Change can be detected reliably even in situation where the two images (the reference scene image and the current image) are taken several hours apart.
We propose two fast recursive methods for synthesis of two families of basic systems of generalized slant Walsh transforms when the number of slant vectors k > 2. On this basis the fast algorithms having universal ...
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We propose two fast recursive methods for synthesis of two families of basic systems of generalized slant Walsh transforms when the number of slant vectors k > 2. On this basis the fast algorithms having universal adaptive regular structure with adaptation to the basis word length which enables to parallelize imageprocessing and coding are constructed.
This paper describes a study of parallel imageprocessingalgorithms implemented on a one-dimensional DSP array. DSPs are developed for computationally intensive signal processing operations. Recently introduced paral...
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This paper describes a study of parallel imageprocessingalgorithms implemented on a one-dimensional DSP array. DSPs are developed for computationally intensive signal processing operations. Recently introduced parallel DSPs can be used for all levels of imageprocessing operations and they provide easy development of a parallel system. In addition, due to the computing power delivered by these processors, we can employ coarse grain parallelism instead of the traditional fine-grain parallelism. Modularity, expandability and easy programming are other advantages of parallel DSPs. In this paper, parallel implementation of some selected imageprocessingalgorithms is described and performance results are presented. (C) 2000 Elsevier Science B.v. All rights reserved.
systems for automated image analysis are useful for a variety of tasks. Their importance is still growing due to technological advances and increased social acceptance. Especially driver assistance systems have reache...
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systems for automated image analysis are useful for a variety of tasks. Their importance is still growing due to technological advances and increased social acceptance. Especially driver assistance systems have reached a high level of sophistication. Fully or partly autonomously guided vehicles, particularly for road traffic, require highly reliable algorithms due to the conditions imposed by natural environments. At the Institut fur Neuroinformatik, methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a system extracting important information from an image taken by a CCD camera installed at the rear-view mirror in a car. The approach is divided into a sequential and a parallel phase of sensor and information processing. Three main tasks, namely initial segmentation (object detection), object tracking and object classification are realized by integration in the sequential phase and by fusion in the parallel phase. The main advantage of this approach is integrative coupling of different algorithms providing partly redundant information. (C) 2000 Elsevier Science B.v. All rights reserved.
Underwater exploration is becoming more and more important for many applications involving physical, biological, geological, archaeological, and industrial issues. Unfortunately, only a small percentage of potential r...
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Underwater exploration is becoming more and more important for many applications involving physical, biological, geological, archaeological, and industrial issues. Unfortunately, only a small percentage of potential resources has been exploited under the sea. The inherent structureless environment and the difficulties implied by the nature of the propagating medium have placed limitations on the sensing and the understanding of the underwater world. Typically, acoustic imaging systems are widely utilized for both large- and small-scale underwater investigations, as they can more easily achieve short and large visibility ranges, though at the expense of a coarse resolution and a poor visual quality. This paper aims at surveying the up-to-date advances in acoustic acquisition systems and data processing techniques, especially focusing on three-dimensional (3-D) short-range imaging for scene reconstruction and understanding. In fact, the advent of smarter and more efficient imaging systems has allowed the generation of good-quality high-resolution images and the related design of proper techniques for underwater scene understanding. The term acoustic vision is introduced to generally describe all data processing (especially imageprocessing) methods devoted to the interpretation of a scene. Since acoustics is also used for medical applications, a short overview of the related systems for biomedical acoustic image formation is provided. The final goal of the paper is to establish the state of the art of the techniques and algorithms for acoustic image generation and processing, providing technical details and results for the most promising techniques, and pointing out the potential capabilities of this technology for underwater scene understanding.
The Ritz method was developed to analyze vector diffraction by microstructures. The effect of focusing a plane wave of wavelength 1.3 μm by a lens of radius 2 μm, aperture 2 μm and focal length 4 μm was examined. ...
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The Ritz method was developed to analyze vector diffraction by microstructures. The effect of focusing a plane wave of wavelength 1.3 μm by a lens of radius 2 μm, aperture 2 μm and focal length 4 μm was examined. The algorithms allow the derivation of the approximate solution of the desired accuracy not at the cost of an increased network step, but through the construction of more accurate approximations of the initial problem.
MPEG-4 audio represents a new kind of audio coding standard. Unlike its predecessors, MPEG-1 and MPEG-2 high-quality audio coding, and unlike the speech coding standards which have been completed by the ITU-T, it desc...
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MPEG-4 audio represents a new kind of audio coding standard. Unlike its predecessors, MPEG-1 and MPEG-2 high-quality audio coding, and unlike the speech coding standards which have been completed by the ITU-T, it describes not a single or small set of highly efficient compression schemes but a complete toolbox to do everything from low bit-rate speech coding to high-quality audio coding or music synthesis. The natural coding part within MPEG-4 audio describes traditional type speech and high-quality audio coding algorithms and their combination to enable new functionalities like scalability (hierarchical coding) across the boundaries of coding algorithms. This paper gives an overview of the basic algorithms and how they can be combined. (C) 2000 Elsevier Science B.v. All rights reserved.
For real-time image-processing applications, a highly parallel system that exploits parallelism is desirable. A content addressable memory (CAM), or an associative processor, that can perform various types of parallel...
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For real-time image-processing applications, a highly parallel system that exploits parallelism is desirable. A content addressable memory (CAM), or an associative processor, that can perform various types of parallel processing with words as the basic unit is a promising component for creating such a system because of its suitability for LSI implementation. Conventional CAM LSI's, however, have neither efficient function nor enough capacity for pixel-parallel processing. This paper describes a fully parallel I-Mb CAM LSI, It has advanced functions for processingvarious pixel-parallel algorithms, such as mathematical morphology and discrete-time cellular neural networks. Moreover, since it has 16-K words, or processing elements (PE's), which can process 128 x 128 pixels in parallel, a board-sized pixel-parallel image-processing system can be implemented using several chips. A chip capable of operating at 56 MHz and 2.5 v was fabricated using 0.25-mu m full-custom CMOS technology with five aluminum layers. A total of 15.5 million transistors have been integrated into a 16.1 x 17.0 mm chip, Typical power dissipation is 0.25 W, processing performance of various update and data transfer operations is 3-640 GOPS, This CAM LSI will make a significant contribution to the development of compact, high-performance image-processingsystems.
In this paper we discuss a new implementation of a floating point based rainfalling watershed algorithm. First, we analyze and compare our proposed algorithm and its implementation with two implementations based on th...
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In this paper we discuss a new implementation of a floating point based rainfalling watershed algorithm. First, we analyze and compare our proposed algorithm and its implementation with two implementations based on the well-known discrete vincent-Soille flooding watershed algorithms. Next, we show that by carefully designing and optimizing our algorithm a memory (bandwidth) efficient and high speed implementation can be realized. We report on timing and memory usage results for different compiler settings, computer systems and algorithmic parameters. Our optimized implementation turns out to be significantly faster than the two vincent-Soille based implementations with which we compare. Finally, we include some segmentation results to illustrate that visually acceptable and almost identical segmentation results can always be obtained for all algorithms being compared. And, we also explain how, in combination with other pre- or post-processing techniques, the problem of oversegmentation (a typical problem of all raw watershed algorithms) can be (partially) overcome. All these properties make that our proposed implementation is an excellent candidate for use in various practical applications where high speed performance and/or efficient memory usage is needed.
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