In the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs.

Lake et al. proposed a way to build machines that learn as fast as people do. This can be possible only if machines follow the human processes: the perception-action loop. People perceive and act to understand new objects or to promote specific behavior to their partners. In return, the object/person provides information that induces another reaction, and so on.

We present a computational method for pseudo-circular object detection and quantitative characterization in digital images, using the gradient accumulation matrix as a basic tool. This Gradient Accumulation Transform (GAT) was first introduced in 1992 by Kierkegaard and recently used by Kaytanli & Valentine. In the present article, we modify the approach by using the phase coding studied by Cicconet, and by adding a “local contributor list” (LCL) as well as a “used contributor matrix” (UCM), which allow for accurate peak detection and exploitation. These changes help make the GAT algorithm a robust and precise method to automatically detect pseudo-circular objects in a microscopic image. We then present an application of the method to cell counting in microbiological images.

In biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid detection. This optimization also allows complete erasing of the grid without altering the cells, which eases their segmentation.

In Kline's target article, the role of motor behavior in teaching is missing. However, it is so important that we cannot avoid taking into account the movements of another person when performing our own movements. Moreover, the state of mind is embodied. Consequently, teaching should integrate the role of motor behavior to enhance teacher/learner social interactions.

In biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.

Dijkerman & de Haan (D&dH) study perception and action as two independent processes. However, in all daily activities the processes are completely intertwined, so it is difficult to separate one from the other. Humans perceive in order to move and also move in order to perceive. Understanding first how perception and action are coordinated, leads us then to determine how each component works independently.

Although robots can contribute to the understanding of biological behavior, they fail to model the processes by which humans cope with their environment. Both development and learning are characterized by complex relationships that require constant modification. Given present technology, robots can only model behaviors in specific situations and during discrete stages. Robots cannot master the complex relationships that are the hallmark of human behavior.

Based on studies with infants, we expand on Stoffregen & Bardy's explanation of perceptual motor errors, given the global array. Information pick-up from the global array is not sufficient without adequate exploratory movements and learning to support perceptually guided activity.