Food Intake Vision-Based Recognition System via Histogram of Oriented Gradients and Support Vector Machine for Persons With Alzheimer’s Disease

Due to cognitive decline, individuals with Alzheimer’s often suffer from malnutrition, forgetting to eat, even if food is presented. Therefore, assistance with feeding is needed. In this paper a vision-based system for monitoring of eating patterns is presented. Upper Body Region (UBR) is detected using Viola-Jones method, a histogram of oriented gradients (HOG) is generated for feature extraction, and a support vector machine (SVM) is used to distinguish eating versus non-eating. To reduce false positive results, Haar-like features are used to detect hands while moving between served food and mouth within the identified upper body region (UBR). A combined template image (CTI) method is proposed in this work to eliminate false positive hand detections where 30 hand eating posture images have been selected and combined into one template image. Matching implemented using CTI is 2.86 times faster than matching the subject to the 30 images separately. Experimental simulation used 33 videos of 163840 frames indicates that the proposed method achieves a high accuracy of 90.65%.

Research on track fastener positioning method based on local unidirectional template matching

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

Automatic assembly of micro-miniature parts based on coaxial alignment and ORB feature matching

Aiming at the problems of low manual assembly process efficiency and low yield of micro-miniature parts. A vision system based on ORB feature matching for fast coaxial alignment of micro-miniature parts automated assembly is proposed. The coaxial alignment module is the main hardware, the assembly part and the base part can be imaged in the industrial camera; the ORB features are extracted from the known part template image and the part image obtained by the vision imaging system, and the RANSAC algorithm is adopted to match the ORB features of the template image and the actual part image, the pose of parts is calculated by feature matching results. At the same time, the image coordinate system and the motion axis coordinate system are calibrated, and the motion control system is driven by the transformation matrix of the two coordinate systems to complete the assembly between the assembly part and the base part. The assembly experiment shows the system can complete the automated assembly of micro-miniature parts.

Rail Fastener Positioning Based on Double Template Matching

For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

Defect Detection of Printed Fabric Based on RGBAAM and Image Pyramid

To solve the problem of defect detection in printed fabrics caused by abundant colors and varied patterns, a defect detection method based on RGB accumulative average method (RGBAAM) and image pyramid matching is proposed. First, the minimum period of the printed fabric is calculated by the RGBAAM. Second, a Gaussian pyramid is constructed for the template image and the detected image by using the minimum period as a template. Third, the similarity measurement method is used to match the template image and the detected image. Finally, the position of the printed fabric defect is marked in the image to be detected by using the Laplacian pyramid restoration. The experimental results show that the method can accurately segment the printed fabric periodic unit and locate the defect position. The calculation cost is low for the method proposed in this article.

Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction

Groupwise image (GW) registration is customarily used for subsequent processing in medical imaging. However, it is computationally expensive due to repeated calculation of transformations and gradients. In this paper, we propose a deep learning (DL) architecture that achieves GW elastic registration of a 2D dynamic sequence on an affordable average GPU. Our solution, referred to as dGW, is a simplified version of the well-known U-net. In our GW solution, the image that the other images are registered to, referred to in the paper as template image, is iteratively obtained together with the registered images. Design and evaluation have been carried out using 2D cine cardiac MR slices from 2 databases respectively consisting of 89 and 41 subjects. The first database was used for training and validation with 66.6–33.3% split. The second one was used for validation (50%) and testing (50%). Additional network hyperparameters, which are—in essence—those that control the transformation smoothness degree, are obtained by means of a forward selection procedure. Our results show a 9-fold runtime reduction with respect to an optimization-based implementation; in addition, making use of the well-known structural similarity (SSIM) index we have obtained significative differences with dGW with respect to an alternative DL solution based on Voxelmorph.

Automatic Recognition of Dendritic Solidification Structures: DenMap

Dendrites are the predominant solidification structures in directionally solidified alloys and control the maximum length scale for segregation. The conventional industrial method for identification of dendrite cores and primary dendrite spacing is performed by time-consuming laborious manual measurement. In this work we developed a novel DenMap image processing and pattern recognition algorithm to identify dendritic cores. Systematic row scan with a specially selected template image over an image of interest is applied via a normalised cross-correlation algorithm. The DenMap algorithm locates the exact dendritic core position with a 98% accuracy for a batch of SEM images of typical as-cast CMSX-4® microstructures in under 90 s per image. Such accuracy is achieved due to a sequence of specially selected image pre-processing methods. Coupled with statistical analysis the model has the potential to gather large quantities of structural data accurately and rapidly, allowing for optimisation and quality control of industrial processes to improve mechanical and creep performance of materials.

Repeat pattern segmentation of print fabric based on adaptive template matching

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

A comparison of shape-based matching with deep-learning-based object detection

Matching, i. e. determining the exact 2D pose (e. g., position and orientation) of objects, is still one of the key tasks in machine vision applications like robot navigation, measuring, or grasping an object. There are many classic approaches for matching, based on edges or on the pure gray values of the template. In recent years, deep learning has been utilized mainly for more difficult tasks where the objects of interest are from many different categories with high intra-class variations and classic algorithms are failing. In this work, we compare one of the latest deep-learning-based object detectors with classic shape-based matching. We evaluate the methods both on a matching dataset as well as an object detection dataset that contains rigid objects and is thus also suitable for shape-based matching. We show that for datasets of this type, where rigid objects appear with rigid transformations, shape-based matching still outperforms recent object detectors regarding runtime, robustness, and precision if only a single template image per object is used. On the other hand, we show that for the application of object detection, the deep-learning-based approach outperforms the classic approach if annotated data is used for training. Ultimately, the choice of the best suited approach depends on the conditions and requirements of the application.

Anomaly Detector for Manufacturing Industries using Lab View

In assembling enterprises the extra parts can arrive in a wide scope of various sizes and shapes, yet the essential creation process is large and continues with its different stages. It begins by manufacturing steel wire into the correct shape, trailed by warmth treatment to enhance the quality and surface treatment to enhance strength, before the packaging procedure. Splits or anomaly on the extra parts like bolts are one of the serious issues in the assembling enterprises which lead to parcel of issues when utilized in any machine. By manual investigation it is hard to discover the breaks. As a solution for this problem we have designed, anomaly detector for manufacturing industries using LabVIEW to detect the defected bolts which may cause serious issues in running machines like electromagnetic interference and unnecessary vibrations. In this proposed system, the shapes are detected using geometric matching and the defects are identified by varying the threshold levels. Also, the colour matching is used to find the erosion. The proposed system. The image is converted into gray scale to compare with template image using color plane extraction and the defects are identified comparing the two images i.e., the template and the acquired image using match pattern where the patterns are matched for both the images. The image is taken in real time and compared with template image using web cam and my Rio.