Vision-Based Mobile Robot Navigation Using Image Processing and Cell Decomposition

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

Robot path obstacle locator using webcam and laser emitter

This paper presents an analysis of three current color perception models CIECAM97, CIECAM02 and the Belyaeva-Matveev model and then compares their visual component. The definition of the Belyaeva-Matveev, CIECAM02 and CIECAM97 models is presented, the mathematical aspect of the Belyaeva-Matveev model is explained and the main points of the construction of an algorithm for its realization are described. The algorithm is written in Matlab programming language, which includes a plug-in for synthetic image processing - Image Processing Toolbox. Implementation of the algorithms of the Belyaeva-Matveev, CIECAM02 and CIECAM97 models involves the transition to new observation conditions: from the given initial conditions of creating a synthetic image with the primary values of brightness and color of the object and the background to viewing the image on the monitor screen. In the comparison one and the same initial image passes three algorithms of the investigated models of color perception and, as a result, we can compare the obtained images under the same observation conditions. According to these results we can judge which of the models has the smallest deviation in the accuracy of color and sensation transfer.

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.

Image binarization is one of the fundamental methods in image processing and it is mainly used as a preprocessing for other methods in image processing. We present an image binarization method with the primary purpose to find markers such as those used in mobile 3D scanning systems. Handling a mobile 3D scanning system often includes bad conditions such as light reflection and non-uniform illumination. As the basic part of the scanning process, the proposed binarization method successfully overcomes the above problems and does it successfully. Due to the trend of increasing image size and real-time image processing we were able to achieve the required small algorithmic complexity. The paper outlines a comparison with several other methods with a focus on objects with markers including the calibration system plane of the 3D scanning system. Although it is obvious that no binarization algorithm is best for all types of images, we also give the results of the proposed method applied to historical documents.

Elevators ensure transportation of people inside buildings and increase their life quality. High-rise buildings whose number is increasingly going up today has one or more elevator cabs to provide vertical transportation. A great number of people use elevators in many buildings such as business centres, hotels, hospitals and shopping centres daily. It is highly essential for the elevators used by many people daily to operate constantly. In the event of sudden failure of elevators during operation, people inside them face with a tough situation. Also, people have difficulty during the maintenance-repair period of elevators. Elevator system has counterweight system in order to balance the weight of elevator cab. A guide-rail system has been developed to limit the movements of elevator cab and counterweights on horizontal axis. When an elevator system is operational, cab and counterweight system move reversely. The common failures in elevators are usually seen in the components such as elevator guide-rail system, ropes and motors. In this study, a system based on image processing has been developed in order to prevent wear on guide-rail surface in elevators. In the proposed method, real-time condition monitoring is performed by cameras using built-in system. The images of elevator guide-rail surface are captured via four digital cameras fixed onto elevator cab. The image-processing methods are applied on the images captured by cameras and hence the wears on the surface of guide-rails are detected. The surface of guide-rail is firstly detected in the proposed method. Then, image segmentation and mathematical morphology are applied on the image of guide-rail surface and the wears on the surface of rail are detected. The failure extent of the wear failures detected are calculated. By processing the images captured by four cameras during movement of elevator, the results for surface of guide-rails are obtained. Using these results, reporting is performed. An elevator prototype has been created in order to carry out tests for development of the proposed method. The tests have been conducted by fixing the built-in system and cameras onto this elevator prototype. It is considerably advantageous to detect the failures on elevator guide-rails through image-processing methods. Following a literature review, it is seen that the proposed method is a new approach.

The last few years have been strongly marked by the presence of multimedia data (images and videos) in our everyday lives. These data are characterized by a fast frequency of creation and sharing since images and videos can come from different devices such as cameras, smartphones or drones. The latter are generally used to illustrate objects in different situations (airports, hospitals, public areas, sport games, etc.). As result, image and video processing algorithms have got increasing importance for several computer vision applications such as motion tracking, event detection and recognition, multimedia indexation and medical computer-aided diagnosis methods. In this paper, we propose a real time cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can be run in real time and in a secure way. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different algorithms without the need to download, install and configure software or hardware. Moreover, the platform offers the access to the integrated applications from multiple users thanks to the use of Docker (Merkel, 2014) containers and images. Experimentations were conducted within three kinds of algorithms: 1. image processing toolbox. 2. Video processing toolbox. 3. 3D medical methods such as computer-aided diagnosis for scoliosis and osteoporosis.  These experimentations demonstrated the interest of our platform for sharing our scientific contributions related to computer vision domain. The scientific researchers could be able to develop and share easily their applications fastly and in a safe way.

The objective of this study is to quantify the in vivo kinematics of the superficial femoral artery (SFA) caused by hip and knee flexion by utilizing vascular and skeletal magnetic resonance (MR) imaging and image processing methods. Seven male healthy volunteers (56 ± 5 years old) were imaged using contrast-enhanced MR angiography in the supine and bent-leg positions using a GE Signa Excite 1.5-T scanner. Coregistered SFA coordinates from these two body positions provide the relative motion of the SFA with respect to the femur as a result of hip and knee flexion. With 86 ± 6° hip and 39 ± 6° knee flexion angles, the proximal portion of the SFA moved significantly inferiorly while the distal portion of the SFA stayed immobile in relation to the femur due to hip and knee flexion. From the supine position to the bent-leg position, the top of the SFA moved 23.5 ± 7.0 mm closer to the bottom of the SFA (p < 0.05), resulting in shortening and buckling of the SFA. The differential translations of points along the SFA indicate shortening and buckling along the length of the vessel. They also indicate that the SFA is more tethered to the bony structures at the knee compared to the thigh. The in vivo SFA kinematics obtained in this study provides insight into the interaction between the SFA and the surrounding musculoskeletal structures.

Signal and imaging investigations are currently a basic step of the diagnostic, prognostic and follow-up processes of heart diseases. Besides, the need of a more efficient, cost-effective and personalized care has lead nowadays to a renaissance of clinical decision support systems (CDSS). The purpose of this paper is to present an effective way to achieve a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. Among several heart diseases, we treat heart failure, that for its complexity highlights best the benefits of this integration. Architectural details of the related components of the CDSS are provided with special attention to their seamless integration in the general IT infrastructure. In particular, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features that, in collaboration with the CDSS, can facilitate decisional problems in the heart failure domain. Furthermore, additional signal and image processing tools enrich the model baseof the CDSS.

This paper presents an approach for real-time detection of three types of eye blinks: eye blink (blinking both eyes simultaneously), left and right winks. The process of blink detection has been divided into four parts viz. face localization in facial images acquired through a video camera, eye pair localization, pixels’ motion analysis using optical flow technique, and classification of eye blinks. Blink detection has been performed using a video camera and MATLAB software with image processing and computer vision toolbox. The algorithm takes about 60 ms time for processing a frame and 250 ms time for confirmation and classification of the detected blink. An experiment was conducted to evaluate the performance of the proposed approach in which 10 users voluntarily participated. The performance of the proposed method has been tested under two lighting conditions: natural lighting conditions and controlled lighting conditions. Also, the performance has been tested by varying the distance of the user from the camera. Here, it is observed that the system gives best performance when used under controlled lighting conditions and the user sitting at a distance of about 0.5 m. Accuracy of the proposed approach has been found to be 96, 92 and 88% for detection of eye blink, left wink and right wink, respectively. The proposed method has also been tested on ZJU dataset where it has given precision, detection accuracy and false alarm rate of values 94.11, 91.2 and 1.54%, respectively. The proposed system has been used and evaluated for performing various mouse analogous functions using eye blinks and winks. It has given an accuracy of 90, 80 and 90% in performing left click, double click, and right click operations, respectively.

For 3D surface reconstruction problems with noisy and incomplete range data measured from complex scenes with arbitrary topologies, a low-level representation, such as level set surfaces, is used. Such surface reconstruction is typically accomplished by minimizing a weighted sum of datamodel discrepancy and model smoothness terms. We introduce a new nonlinear model smoothness term for surface reconstruction based on variations of the surface normals. A direct solution requires solving a fourth-order partial differential equation (PDE), which is very difficult with; conventional numerical techniques. Our solution is based on processing the normals separately from the surface, which allows us to separate the problem into two second-order PDEs. The proposed method can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion.

In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches

Determination of seal coat deterioration using image processing methods

The general image processing technique only processes the image itself, but in the field of geo-mapping reconnaissance, geographic data also need to be dealt with. For example, scouts often need to know the location of a target. In this paper, an image and geographic data integration approach is proposed. This method takes the geographic data as multi-channel floating-point matrixes participating in the image processing algorithm to handle. To verify the effectiveness of the proposed algorithm, a geographic data verification method for the image and geographic data integration is also introduced. The experimental results show that the proposed method can effectively process the image with geographic data and show the application for image stitching.

Existing methods and means for measurements and identification of the vegetable matter (VM) in wool fibre assemblies are time consuming, labour-intensive and therefore expensive. This paper presents the measurements of VM content, sorting and counting (identification) of VM particles using digital image processing and analysis. A measuring system has been constructed, and the compound light mode has been used for the measurements. A formula has been derived to translate the VM content by area into the VM content by mass. MATLAB and its image processing toolbox have been used in the image processing and analysis. The area, perimeter and shape factor of each VM particle were measured, and using the values of the boundary limits of the shape factor for each type of VM, sorting and counting of four types of VM in wool have been carried out.