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Abstract. In this paper, we present a novel hardware architecture to achieve erosion and dilation with a large structuring element. We are proposing a modification of HGW algorithm with a block mirroring scheme to ease the propagation and memory access and to minimize memory consumption. It allows to suppress the needs for backward scanning and gives the possibility for hardware architecture to process very large lines with a low latency. It compares well with the Lemonnier's architecture in terms of ASIC gates area and shows the interest of our solution by dividing the circuit area by an average of 10.

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This paper focuses on the development of a fully programmable morphological coprocessor for embedded devices. It is a well-known fact that the majority of morphological processing operations are composed of a (potentially large) number of sequential elementary operators. At the same time, the industrial context induces a high demand on robustness and decision liability that makes the application even more demanding. Recent stationary platforms (PC, GPU, clusters) no more represent a computational bottleneck in real-time vision or image processing applications. However, in embedded solutions such applications still hit computational limits. The Morphological Co-Processing Unit (MCPU) replies to this demand. It assembles the previously published efficient dilation/erosion units with geodesic units and ALUs to support a larger collection of morphological operations, from a simple dilation to a serial filters involving a geodesic reconstruction step. The coprocessor has been integrated into an FPGA platform running a server, able to respond client's requests over the ethernet. The experimental performance of the MCPU measured on a wide set of operations brings as results in orders of magnitude better than another embedded platform, built around an ARM A9 quad-core processor.

We describe a novel method for propagating disparity values using hierarchical segmentation by waterfall and robust regression models. High confidence disparity values obtained by state of the art stereo matching algorithms are interpolated using a coarse to fine approach. We start from a coarse segmentation of the image and try to fit each region's disparities using robust regression models. If the fit is not satisfying, the process is repeated on a finer region's segmentation. Erroneous values in the initial sparse disparity maps are generally excluded, as we use robust regressions algorithms and left-right consistency checks. Final disparity maps are therefore not only denser but can also be more accurate. The proposed method is general and independent from the sparse disparity map generation: it can therefore be used as a post-processing step for any stereo-matching algorithm.