Michel Bilodeau

Road segmentation and obstacle detection by a fast watershed transformation

This paper presents the work, performed at CMM as part of the European PROMETHEUS project, concerning road/lane segmentation and obstacle detection in a dynamic scene. Road/lane segmentation is used to select ROI where obstacle detection algorithms are applied. This segmentation is made up of a temporal filter, an edge detector and a watershed transformation. It produces a marker of the current traffic lane. This marker is then used for calculating a road model which is continuously updated. The obstacle detection uses various criteria: existence of a darker region marking a vehicle, size and contrast of obstacles and symmetry. These two processes use the watershed transformation which is usually the slowest part of the segmentation. To speed it up, a very efficient enhancement of the algorithm, based on anamorphosis, is presented.

Road tracking, lane segmentation and obstacle recognition by mathematical morphology

Presents the work performed at the CMM as part of the European PROMETHEUS project. The research deals with the road edge detection, lane segmentation and obstacle recognition in a dynamic scene acquired by a monochrome monocular camera. The image processing is based on morphological segmentation tools. The experiments on over a thousand images show that the approach works well on difficult cases such as dense traffic, and roads without land markers.<<ETX>>

An Efficient Hardware Architecture without Line Memories for Morphological Image Processing

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 Lemonniers architecture in terms of ASIC gates area and shows the interest of our solution by dividing the circuit area by an average of 10.

Space, structure and randomness

Space, structure and randomness , Space, structure and randomness , کتابخانه دیجیتال جندی شاپور اهواز

Real-time implementation of morphological filters with polygonal structuring elements

In mathematical morphology, circular structuring elements (SE) are used whenever one needs angular isotropy. The circles—difficult to implement efficiently—are often approximated by convex, symmetric polygons that decompose under the Minkowski addition to 1-D inclined segments. In this paper, we show how to perform this decomposition efficiently, in stream with almost optimal latency to compute gray-scale erosion and dilation by flat regular polygons. We further increase its performance by introducing a spatial parallelism while maintaining sequential access to data. We implement these principles in a dedicated hardware block. Several of these blocks can be concatenated to efficiently compute sequential filters, or granulometries in one scan. With a configurable image size and programmable SE size, this architecture is usable in high-end, real-time industrial applications. We show on an example that it conforms to real-time requirements of the 100Hz 1080p FullHD TV standard, even for serial morphological filters using large hexagons or octagons.

Fast streaming algorithm for 1-D morphological opening and closing on 2-D support

This paper presents a new streaming algorithm for 1-D morphological opening and closing transformations on 2-D support. Thanks to a recursive computation technique, the algorithm processes an image in constant time irrespective of the Structuring Element (SE) size, with a minimal latency and very low memory requirements, supporting various input data types. It reads and writes data strictly sequentially in the same (horizontal) scan order for both the horizontal and vertical SE. Aforementioned properties allow an efficient implementation in embedded hardware opening a new opportunity of a parallel computation.

One-scan algorithm for arbitrarily oriented 1-D morphological opening and slope pattern spectrum

This paper presents a fast, one-scan algorithm for 1-D morphological opening on 2-D support. The algorithm is further extended to compute the pattern spectrum during a single image scan. The structuring element (SE) can be oriented under arbitrary angle that makes it possible to perform different orientation-involved image analysis, such as the local angle extraction, directional granulometry, etc. The algorithm processes an image in constant time regardless the SE orientation and size in one scan, with minimal latency and very low memory requirements. For pattern spectra, the C-implementation yields an experimental speed-up of 27× compared to other suitable solutions. Aforementioned properties allow for efficient implementation on hardware platforms such as GPU or FPGA that opens a new opportunity of parallel computation, and consequently, further speed-up.

Preprocessing and analysis of microarray images from integrated lensless bio-photonic sensors

A new integrated lensless bio-photonic sensors is being developed. It replaces the ordinary slide supporting the DNA spots, and the complex, large and expensive hybridisation and the scanner reading system, by a sandwich of well defined chemical and optical layers grafted onto a CCD sensor. The upper layer of the new biochip performs the biological function. Due to the architecture of the biochip leading to a lensless imaging of the spots directly on the sensor pixels, the images produced will have novel characteristics beyond the analysis capacity of reading software packages of microarray analysis. In this framework, specific image processing and statistical data analysis algorithms have been developed in order to assess and to quantify these images.