This paper presents a method for recovering and identifying image regions from an initial oversegmentation using qualitative knowledge of its content. Compared to recent works favoring spatial information and quantitative techniques, our approach focuses on simple a priori qualitative inclusion and photometric relationships such as "region A is included in region B", "the intensity of region A is lower than the one of region B" or "regions A and B depict similar intensities" (photometric uncertainty). The proposed method is based on a two steps" inexact graph matching approach. The first step searches for the best subgraph isomorphism candidate between expected regions and a subset of regions resulting from the initial oversegmentation. Then, remaining segmented regions are progressively merged with appropriate already matched regions, while preserving the coherence with a priori declared relationships. Strengths and weaknesses of the method are studied on various images (grayscale and color), with various intial oversegmentation algorithms (k-means, meanshift, quickshift). Results show the potential of the method to recover, in a reasonable runtime, expected regions, a priori described in a qualitative manner. For further evaluation and comparison purposes, a Python opensource package implementing the method is provided, together with the specifically built experimental database.

The choice of actuation when designing a bipedal robot is an important matter, as it impacts its capacities and its control. In these aspects, electric direct-drive (DD) linear motors are interesting candidates. Indeed, they can reproduce the human muscle system by using monoarticular and bi-articular motors and offer a natural backdrivability for impact mitigation. However, motor efficiency depends on their elongation and their attachment points. The ROBIBIO (RObot humanoïde BI-articulaire BIO-inspiré) project aims to design and build a planar bipedal robot using electric direct-drive linear motors for the actuation. To do so, a simulator was developed to evaluate multiple human-like bipedal robot architectures over a set of motion-captured movements. Furthermore, a numerical optimization was done to find the best motor placement for each architecture.