Paridhi Swaroop

Bio: Paridhi Swaroop is an academic researcher. The author has contributed to research in topics: Template matching & Image processing. The author has an hindex of 1, co-authored 1 publications receiving 32 citations.

An Overview of Various Template Matching Methodologies in Image Processing

Abstract: The recognition and classification of objects in images is a emerging trend within the discipline of computer vision community. A general image processing problem is to decide the vicinity of an object by means of a template once the scale and rotation of the true target are unknown. Template is primarily a sub-part of an object that‟s to be matched amongst entirely different objects. The techniques of template matching are flexible and generally easy to make use of, that makes it one amongst the most famous strategies of object localization. Template matching is carried out in versatile fields like image processing,signal processing, video compression and pattern recognition.This paper gives brief description of applications and methods where template matching methods were used. General Terms Template matching,computer vision,image processing,object recognition.

A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots.

Abstract: In this paper we tackle the problem of indoor robot localization by using a vision-based approach. Specifically, we propose a visual odometer able to give back the relative pose of an omnidirectional automatic guided vehicle (AGV) that moves inside an indoor industrial environment. A monocular downward-looking camera having the optical axis nearly perpendicular to the ground floor, is used for collecting floor images. After a preliminary analysis of images aimed at detecting robust point features (keypoints) takes place, specific descriptors associated to the keypoints enable to match the detected points to their consecutive frames. A robust correspondence feature filter based on statistical and geometrical information is devised for rejecting those incorrect matchings, thus delivering better pose estimations. A camera pose compensation is further introduced for ensuring better positioning accuracy. The effectiveness of proposed methodology has been proven through several experiments, in laboratory as well as in an industrial setting. Both quantitative and qualitative evaluations have been made. Outcomes have shown that the method provides a final positioning percentage error of 0.21% on an average distance of 17.2 m. A longer run in an industrial context has provided comparable results (a percentage error of 0.94% after about 80 m). The average relative positioning error is about 3%, which is still in good agreement with current state of the art.

RoScript: a visual script driven truly non-intrusive robotic testing system for touch screen applications

Abstract: Existing intrusive test automation techniques for touch screen applications (e.g., Appium and Sikuli) are difficult to work on many closed or uncommon systems, such as a GoPro. Being non-intrusive can largely extend the application scope of the test automation techniques. To this end, this paper presents RoScript, a truly non- intrusive test-script-driven robotic testing system for test automation of touch screen applications. RoScript leverages visual test scripts to express GUI actions on a touch screen application and uses a physical robot to drive automated test execution. To reduce the test script creation cost, a non-intrusive computer vision based technique is also introduced in RoScript to automatically record touch screen actions into test scripts from videos of human actions on the device under test. RoScript is applicable to touch screen applications running on almost arbitrary platforms, whatever the underlying operating systems or GUI frameworks are. We conducted experiments applying it to automate the testing of 21 touch screen applications on 6 different devices. The results show that RoScript is highly usable. In the experiments, it successfully automated 104 test scenarios containing over 650 different GUI actions on the subject applications. RoScript accurately performed GUI actions on over 90% of the test script executions and accurately recorded about 85% of human screen click actions into test code.

Fast and Precise Positioning in PCBs Using Deep Neural Network Regression

Abstract: Precision positioning is a very important task for automatic assembly and inspection in the manufacturing process. The conventional image processing for image alignment has relied on template matching, which is computationally intensive for objects in arbitrary locations and orientations. In this article, we propose the deep neural network regressors for fast and accurate image alignment. They are especially applied to the positioning of printed circuit boards (PCBs). The simple multilayer perceptron (MLP), the convolutional neural network (CNN), and the CNN models incorporated with support vector regression (SVR) are proposed and evaluated for the PCB positioning task. The proposed deep neural networks require only one single reference sample with a manually marked template window. All training images and the ground-truth geometric parameters are automatically generated for the model training. The effect of illumination changes and the strategies to cope with lighting variations are analyzed and proposed for robust positioning. Experimental results indicate that the proposed regressors can achieve a subpixel accuracy in translation and yield a rotation error less than 1° with 1-ms evaluation time for PCB positioning.