In this paper, we present a efficient algorithm for real-time ellipse detection. Unlike Hough transform algorithm that is computationally intense and requires a higher dimensional parameter space, our proposed method reduces the computational complexity significantly, and accurately detects ellipses in realtime. We present a new method of detecting arc-segments from the image, based on the properties of ellipse. We then group the arc-segments into elliptical arcs in order to estimate the parameters of the ellipse, which are calculated using the leastsquare method. Our method has been tested and implemented on synthetic and real-world images containing both complete and incomplete ellipses. The performance is compared to existing ellipse detection algorithms, demonstrating the robustness, accuracy and effectiveness of our algorithm.
We have developed a fully integrated, miniaturized embedded stereo vision system (MESVS-I) which fits into a tiny package of 5x5cm and consumes very low power (700mA@3.3V). The system consists of two small profile CMOS cameras, and a power efficient, dual-core embedded media processor, running at 600MHz per core. The stereo-matching engine performs sub-sampling, rectification, pre-processing using rank transform, correlation-based matching using three levels of recursion, L/R consistency check and post-processing. We have proposed a novel and efficient post-processing algorithm that removes outliers due to low-texture regions and depthdiscontinuities by combining the contributions from the variance map of the rectified image, disparity map, and the variance map of the disparity map. To further enhance the performance of the system, we have implemented a two staged pipelined-processing scheme that takes advantage of the dual-core architecture of the embedded processor, thereby achieving a processing speed of around 10fps for disparity maps.
Standard gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. However, their main drawback is that they are computationally expensive to implement, and require large numbers of parameters. Based on these considerations, we propose an extension of the standard GMM for image segmentation, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM. The proposed model is easy to implement and compared with MRF models, requires lesser number of parameters. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood, based on the gradient method. Experimental results obtained on noisy synthetic and real world grayscale images demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation, as compared to other methods based on standard GMM and MRF models.
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