A New Real Time Object Segmentation and Tracking Algorithm and its Parallel Hardware Architecture

Meribout, Mahmoud; Nakanishi, Mahito

doi:10.1007/s11265-005-4843-8

Cited by 3 publications

(3 citation statements)

References 17 publications

(33 reference statements)

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“…Nowadays different types of parallel architectures are used for the real-time image segmentation: digital signal processors (DSP) with field programmable gate arrays (FPGA) and GPUs [15,20,21]. Using of DSPs and FPGAs makes it possible to achieve real-time processing [15] but requires far more development time than in the case of GPUs with CUDA. Furthermore, software developed for FPGAs is highly dependent on the used chip type and as a consequence has a limited portability while CUDA applications run on a wide range of GPUs without any problems.…”

Section: Execution Timementioning

confidence: 99%

“…Since the real-time aspect is getting more and more important in image processing and especially in image segmentation, parallel hardware architectures and programming models for multicore computing have been developed to achieve acceleration [15]. In this paper, we investigate opportunities for achieving efficient performance of superparamagnetic clustering using the Metropolis algorithm with annealing [14], and propose a real-time implementation on graphics processing units (GPU).…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Image Segmentation on a GPU

Abramov

Kulvičius

Wörgötter

et al. 2010

Lecture Notes in Computer Science

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Abstract. Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algorithm finds the equilibrium states of a Potts model in the superparamagnetic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 × 320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time 1 .

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Section: Execution Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Image Segmentation on a GPU

Abramov

Kulvičius

Wörgötter

et al. 2010

Lecture Notes in Computer Science

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show abstract

“…For cognitive vision systems used by robots interacting with the environment, the real-time computations are of particular importance, since only real-time algorithms can be employed in the perception-action loop. Image segmentation is usually used only as a pre-processing step and hence it needs to run in real-time leaving enough time for subsequent high-level computations (Meribout and Nakanishi, 2005).…”

Section: Special Hardware For Accelerationmentioning

confidence: 99%

Compression of visual data into symbol-like descriptors in terms of a cognitive real-time vision system

Abramov¹

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Humans have five main senses: sight, hearing, touch, smell, and taste. Most of them combine several aspects. For example vision addresses at least three perceptual modalities: motion, color, and luminance. Extraction of these modalities begins in the human eye in the retinal network and the preprocessed signals enter the brain as streams of spatio-temporal patterns. As vision is our main sense, particularly for the perception of the three dimensional structure of the world around us, major efforts have been made to understand and simulate the visual system based on the knowledge collected to date.The research done over the last decades in fields of image processing and computer vision coupled with a tremendous step forward in hardware for parallel computing opened the door to building of so-called cognitive vision systems and for their incorporation into robots. The goal of any cognitive vision system is to transform visual input information into more descriptive representations than just color, motion, or luminance. Furthermore, in most robotic systems "live" interactions of robots with the environment are required, greatly increasing demands on the system. In such systems all pre-computations of the visual data need to be performed in real-time in order to be able to use the output data in the perception-action loop. Thus, a central goal of this thesis is to provide techniques which are strictly compatible with real-time computation.In the first part of this thesis we investigate possibilities for the powerful compression of the initial visual input data into symbol-like descriptors, upon which abstract logic or learning schemes can be applied. We introduce a new real-time video segmentation framework performing automatic decomposition of monocular and stereo video streams without use of prior knowledge on data and considering only preceding information. All entities in the scene, representing objects or their parts, are uniquely identified.In the second part of the thesis we make additional use of stereoscopic visual information and address the problem of establishing correspondences between two views of the scene solved with apparent ease in the human visual system (for images acquired with left and right eye). We exploit these correspondences in the stereo image pairs for the estimation of depth (distance) by proposing a novel disparity measurement technique based on extracted stereo-segments. This technique approximates shape and computes depth information for all entities found in the scene. The most important and novel achievement of this approach is that it produces reliable depth information for objects with weak texture where performance of traditional stereo techniques is very poor.In the third part of this thesis we employ an active sensor, producing indoors much more precise depth information encoded as range-data than any passive stereo iii iv Abstract technique. We perform fusion of image and range data for video segmentation which results in better results. By this we can now even handle fast mov...

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Real-time, parallel segmentation of high-resolution images on multi-core platforms

Fitzgerald

Wills

2014

J Real-Time Image Proc

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A New Real Time Object Segmentation and Tracking Algorithm and its Parallel Hardware Architecture

Cited by 3 publications

References 17 publications

Real-Time Image Segmentation on a GPU

Real-Time Image Segmentation on a GPU

Compression of visual data into symbol-like descriptors in terms of a cognitive real-time vision system

Real-time, parallel segmentation of high-resolution images on multi-core platforms

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