Adaptive image pre-processing for quality control in production lines

Flexible and self-adaptive behaviours in automated quality control systems are features that may significantly enhance the robustness, efficiency and flexibility of the industrial production processes. However, most current approaches on automated quality control are based on rigid inspection methods and are not capable of accommodating to disturbances affecting the image acquisition quality, fact that hast direct consequences on the system´s reliability and performance. In an effort to address the problem, this paper presents the development of a self-adaptive software system designed for the pre-processing (quality enhancement) of digital images captured in industrial production lines. The approach introduces the use of scene recognition as a key-feature to allow the execution of customized image pre-processing strategies, increase the system’s flexibility and enable self-adapting conducts. Real images captured in a washing machines production line are presented to test and validate the system performance. Experimental results demonstrate significant image quality enhancements and a valuable reliability improvement of the automated quality control procedures

Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

An approach to a pseudo real-time image processing engine for hyperspectral imaging

Hyperspectral imaging provides an alternative way of increasing the accuracy by adding another dimension: the wavelength. Recently, hyperspectral imaging is also finding its way into many more applications, ranging from medical imaging in endoscopy for cancer detection to quality control in the sorting of fruit and vegetables. But effective use of hyperspectral imaging requires an understanding of the nature and limitations of the data and of various strategies for processing and interpreting it. Also, the breakthrough of this technology is limited by its cost, speed and complicated image interpretation. We have therefore initiated work on designing real-time hyperspectral image processing to tackle these problems by using a combination of smart system design, and pseudo-real time image processing software. The main focus of this paper is the development of a camera-based hyperspectral imaging system for stationary remote sensing applications. The system consists of a high performance digital CCD camera, an intelligent processing unit, an imaging spectrograph, an optional focal plane scanner and a laptop computer equipped with a frame grabbing card. In addition, special software has been developed to synchronize between the frame grabber (video capture card), and the digital camera with different image processing techniques for both digital and hyperspectral data

Improved Microrobotic Control through Image Processing and Automated Hardware Interfacing

Untethered submilliliter-sized robots (microrobots) are showing potential use in different industrial, manufacturing and medical applications. A particular type of these microrobots, magnetic robots, have shown improved performance in power and control capabilities compared to the other thermal and electrostatic based robots. However, the magnetic robot designs have not been assessed in a robust manner to understand the degree of control in different environments and their application feasibility. This research project seeks to develop a custom control software interface to provide a holistic tool for researchers to evaluate the microrobotic performance through advance control features. The software deliverable involved two main aspects: 1) Real-time microrobot detection and tracking through image processing, achieved through testing with different combinations of built-in tracking algorithms in OpenCV package, and 2) hardware interfacing with a microcontroller based coil control system through serial port communication for direct control of the magnetic coils. The robotic motion control was studied using error mode correction strategies to provide a robust, accurate and time efficient image stream based robotic controls. The user interface developed conducts change in brightness and rotation invariant tracking with an efficient speed of 12 frames per second and performs real-time calculation of robot’s position and orientation. It provides robust automatic control of directing microrobotic motion along the specific path waypoints entered on the images, through recursive serial bus communication. The project showcases the advanced importance and the powerful tool of image processing and microcontroller based communication in conducting the performance analysis of promising microrobotic designs

Fixational Eye Movements in the Earliest Stage of Metazoan Evolution

All known photoreceptor cells adapt to constant light stimuli, fading the retinal image when exposed to an immobile visual scene. Counter strategies are therefore necessary to prevent blindness, and in mammals this is accomplished by fixational eye movements. Cubomedusae occupy a key position for understanding the evolution of complex visual systems and their eyes are assumedly subject to the same adaptive problems as the vertebrate eye, but lack motor control of their visual system. The morphology of the visual system of cubomedusae ensures a constant orientation of the eyes and a clear division of the visual field, but thereby also a constant retinal image when exposed to stationary visual scenes. Here we show that bell contractions used for swimming in the medusae refresh the retinal image in the upper lens eye of Tripedalia cystophora. This strongly suggests that strategies comparable to fixational eye movements have evolved at the earliest metazoan stage to compensate for the intrinsic property of the photoreceptors. Since the timing and amplitude of the rhopalial movements concur with the spatial and temporal resolution of the eye it circumvents the need for post processing in the central nervous system to remove image blur

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs

Real time visualization and analysis of sensory hair arrays using fast image processing and proper orthogonal decomposition

This paper presents an approach both to receiving multiple sensor data from a flow in real time and to analyzing these data in order to characterize the flow condition and, if necessary, control the flow. In order to obtain the data, an optical micro-pillar array acting as distributed wall-shear sensor was developed and interrogated optically with an LDM (long distance microscope). Together, the micro-pillar array and the LDM form a channeling optics, which allows magnified imaging of larger numbers of individual pillars simultaneously. The sensor was tested in a turbulent wall shear stress field under varying conditions (Reynolds number). A frame rate of 3000 fps was used since the higher the temporal resolution is, the more specific flow control strategies might be applied later in realistic application. However, the temporal high resolution would lead to a vast amount of data, which is difficult to analyze in real time. Therefore, a fast image processing algorithm is developed, which detects the tip deflections of the pillars and vectorizes the wall-shear stress field online. The extracted data fields are then broken down into equidistant and overlapping windows in order to guarantee fast POD (proper orthogonal decomposition) modes calculation. The POD is applied to each of these windows and the extracted modes are compared, summarized and collected in a library. Finally, this library is again applied to the flow but under different conditions in order to identify the state of the current flow in real time