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The purpose of this study is to develop a motion sensor (delivering optical flow estimations) using a platform that includes the sensor itself, focal plane processing resources, and co-processing resources on a general purpose embedded processor. All this is implemented on a single device as a SoC (System-on-a-Chip). Optical flow is the 2-D projection into the camera plane of the 3-D motion information presented at the world scenario. This motion representation is widespread well-known and applied in the science community to solve a wide variety of problems. Most applications based on motion estimation require work in real-time; hence, this restriction must be taken into account. In this paper, we show an efficient approach to estimate the motion velocity vectors with an architecture based on a focal plane processor combined on-chip with a 32 bits NIOS II processor. Our approach relies on the simplification of the original optical flow model and its efficient implementation in a platform that combines an analog (focal-plane) and digital (NIOS II) processor. The system is fully functional and is organized in different stages where the early processing (focal plane) stage is mainly focus to pre-process the input image stream to reduce the computational cost in the post-processing (NIOS II) stage. We present the employed co-design techniques and analyze this novel architecture. We evaluate the system’s performance and accuracy with respect to the different proposed approaches described in the literature. We also discuss the advantages of the proposed approach as well as the degree of efficiency which can be obtained from the focal plane processing capabilities of the system. The final outcome is a low cost smart sensor for optical flow computation with real-time performance and reduced power consumption that can be used for very diverse application domains.

Segmentation in ultrasound (US) images is a challenge in computer vision, due to the high signal noise, artifacts that produce discontinuities in the boundaries and shadows that hide part of the received signal. In this paper, a solution based on ellipse fitting motivated by natural artery geometry will be proposed. To optimize the parameters that define such an ellipse, a strategy based on an evolutionary algorithm was adopted. The paper will also demonstrate that the method can be solved in a reasonable amount of time, making intensive GPGPU (general graphics processing unit, GPU, processing) where excellent computing performance gain is obtained (up to 54 times faster than the parallel CPU implementation). The proposed approach is compared with other artery segmentation methods in US images, obtaining very promising results. Furthermore, the proposed approach is parameter free and does not require any initialization estimation close to the final solution.

Correspondence techniques start from the assumption, based on the Lambertian reflection model, that the apparent brightness of a surface is independent of the observer's angle of view. From this, a grey value constancy assumption is derived, which states that a change in brightness of a particular image pixel is proportional to a change in its position. This constancy assumption can be extended directly for vector valued images, such as RGB. It is clear that the grey value constancy assumption does not hold for surfaces with a non-Lambertian behaviour and, therefore, the underlying image representation is crucial when using real image sequences under varying lighting conditions and noise from the imaging device. In order for the correspondence methods to produce good, temporally coherent results, properties such as robustness to noise, illumination invariance, and stability with respect to small geometrical deformations are all desired properties of the representation. In this article, we study how different image representation spaces complement each other and how the chosen representations benefit from the combination in terms of both robustness and accuracy. The model used for establishing the correspondences, based on the calculus of variations, is itself considered robust. However, we show that considerable improvements are possible, especially in the case of real image sequences, by using an appropriate image representation. We also show how optimum (or near optimum) parameters, related to each representation space, can be efficiently found.

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