Assembly process case matching based on a multilevel assembly ontology method

Purpose The purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience. Design/methodology/approach By integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed. Findings By fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components. Originality/value The process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.

A Matching Algorithm for Underwater Acoustic and Optical Images Based on Image Attribute Transfer and Local Features

In the field of underwater vision, image matching between the main two sensors (sonar and optical camera) has always been a challenging problem. The independent imaging mechanism of the two determines the modalities of the image, and the local features of the images under various modalities are significantly different, which makes the general matching method based on the optical image invalid. In order to make full use of underwater acoustic and optical images, and promote the development of multisensor information fusion (MSIF) technology, this letter proposes to apply an image attribute transfer algorithm and advanced local feature descriptor to solve the problem of underwater acousto-optic image matching. We utilize real and simulated underwater images for testing; experimental results show that our proposed method could effectively preprocess these multimodal images to obtain an accurate matching result, thus providing a new solution for the underwater multisensor image matching task.

ISACS

90% of retail sales occur in physical stores. In these physical stores 40% of shoppers leave the store based on the wait time. Autonomous stores can remove customer waiting time by providing a receipt without the need for scanning the items. Prior approaches use computer vision only, combine computer vision with weight sensors, or combine computer vision with sensors and human product recognition. These approaches, in general, suffer from low accuracy, up to hour long delays for receipt generation, or do not scale to store level deployments due to computation requirements and real-world multiple shopper scenarios. We present ISACS, which combines a physical store model (e.g. customers, shelves, and item interactions), multi-human 3D pose estimation, and live inventory monitoring to provide an accurate matching of multiple people to multiple products. ISACS utilizes only shelf weight sensors and does not require visual inventory monitoring which drastically reduces the computational requirements and thus is scalable to a store-level deployment. In addition, ISACS generates an instant receipt by not requiring human intervention during receipt generation. To fully evaluate the ISACS, we deployed and evaluated our approach in an operating convenience store covering 800 square feet with 1653 distinct products, and more than 20,000 items. Over the course of 13 months of operation, ISACS achieved a receipt daily accuracy of up to 96.4%. Which translates to a 3.5x reduction in error compared to self-checkout stations.

A study of edge preserving filters in image matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.

Understanding congruence in person-centred counselling practice: A trainee counsellor’s perspective

Congruence is a condition in therapeutic relationship that refers to accurate matching of a person’s experience with awareness. In person-centred counselling, counsellor’s congruence is believed as one of helpful and significant aspects that facilitates clients’ growth in counselling. However, this term is quite difficult to understand as a single condition as it interrelated to the other conditions such as empathy and unconditional positive regard. Understanding congruence theoretically and practically is intriguing as well as challenging especially for a trainee counsellor that is still learning to apply theory on practice. This paper aims to critically explore congruence from person-centred counselling theory and to demonstrate some evidences of the development of congruence in practice based on the author’s counselling practice as a trainee counsellor. There are three main discussions in this paper that is to explain congruence from the theoretical point of view, to understand the relation between congruence and acceptance and to explore congruence in person-centred practice. The exploration found that trainee counsellor’s cultural background including condition of worth and language barrier is one of challenges in experiencing congruence within person-centred counselling practice.Keywords: congruence, person-centred counselling, unconditional positive regard, ontological standpoint

Wireless localization with diffusion maps

In the Wireless Localization Matching Problem (WLMP) the challenge is to match pieces of equipment with a set of candidate locations based on wireless signal measurements taken by the pieces of equipment. This challenge is complicated by the noise that is inherent in wireless signal measurements. Here we propose the use of diffusion maps, a manifold learning technique, to obtain an embedding of positions and equipment coordinates in a space that enables coordinate comparison and reliable evaluation of assignment quality at very low computational cost. We show that the mapping is robust to noise and using diffusion maps allows for accurate matching in a realistic setting. This suggests that the diffusion-map-based approach could significantly increase the accuracy of wireless localization in applications.

An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land

The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring.

SR-SYBA: A Scale and Rotation Invariant Synthetic Basis Feature Descriptor with Low Memory Usage

Feature description has an important role in image matching and is widely used for a variety of computer vision applications. As an efficient synthetic basis feature descriptor, SYnthetic BAsis (SYBA) requires low computational complexity and provides accurate matching results. However, the number of matched feature points generated by SYBA suffers from large image scaling and rotation variations. In this paper, we improve SYBA’s scale and rotation invariance by adding an efficient pre-processing operation. The proposed algorithm, SR-SYBA, represents the scale of the feature region with the location of maximum gradient response along the radial direction in Log-polar coordinate system. Based on this scale representation, it normalizes all feature regions to the same reference scale to provide scale invariance. The orientation of the feature region is represented as the orientation of the vector from the center of the feature region to its intensity centroid. Based on this orientation representation, all feature regions are rotated to the same reference orientation to provide rotation invariance. The original SYBA descriptor is then applied to the scale and orientation normalized feature regions for description and matching. Experiment results show that SR-SYBA greatly improves SYBA for image matching applications with scaling and rotation variations. SR-SYBA obtains comparable or better performance in terms of matching rate compared to the mainstream algorithms while still maintains its advantages of using much less storage and simpler computations. SR-SYBA is applied to a vision-based measurement application to demonstrate its performance for image matching.

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

Three-dimensional (3D) reconstruction is extensively used in microscopic applications. Reducing excessive error points and achieving accurate matching of weak texture regions have been the classical challenges for 3D microscopic vision. A Multi-ST algorithm was proposed to improve matching accuracy. The process is performed in two main stages: scaled microscopic images and regularized cost aggregation. First, microscopic image pairs with different scales were extracted according to the Gaussian pyramid criterion. Second, a novel cost aggregation approach based on the regularized multi-scale model was implemented into all scales to obtain the final cost. To evaluate the performances of the proposed Multi-ST algorithm and compare different algorithms, seven groups of images from the Middlebury dataset and four groups of experimental images obtained by a binocular microscopic system were analyzed. Disparity maps and reconstruction maps generated by the proposed approach contained more information and fewer outliers or artifacts. Furthermore, 3D reconstruction of the plug gauges using the Multi-ST algorithm showed that the error was less than 0.025 mm.