Alignment invariant image comparison implemented on the GPU

Abstract: This paper proposes a GPU implemented algorithm to determine the differences between two binary images using Distance Transformations. These differences are invariant to slight rotation and offsets, making the technique ideal for comparisons between images that are not perfectly aligned..

Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions

We present a comprehensive approach to characterizing labyrinthine structures that often emerge as a final steady state in pattern forming systems. We employ machine learning based pattern recognition techniques to identify the types and locations of topological defects of the local stripe ordering to augment conventional Fourier analysis. A pair distribution function analysis of the topological defects reveals subtle differences between labyrinthine structures which are beyond the conventional characterization methods. We utilize our approach to highlight a clear morphological transition between two zero-field labyrinthine structures in single crystal Bi substituted Yttrium Iron Garnet films. An energy landscape picture is proposed to understand the athermal dynamics that governs the observed morphological transition. Our work demonstrates that machine learning based recognition techniques enable novel studies of rich and complex labyrinthine type structures universal to many pattern formation systems

Generation and Detection of Cranial Landmark

Purpose : When a surgeon examines the morphology of skull of patient, locations of craniometric landmarks of 3D computed tomography(CT) volume are one of the most important information for surgical purpose. The locations of craniometric landmarks can be found manually by surgeon from the 3D rendered volume or 2D sagittal, axial, and coronal slices which are taken by CT. Since there are many landmarks on the skull, finding these manually is time-consuming, exhaustive, and occasionally inexact. These inefficiencies raise a demand for a automatic localization technique for craniometric landmark points. So in this paper, we propose a novel method through which we can automatically find these landmark points, which are useful for surgical purpose. Materials and Methods : At first, we align the experimental data (CT volumes) using Frankfurt Horizontal Plane (FHP) and Mid Sagittal Plane(MSP) which are defined by 3 and 2 cranial landmark points each. The target landmark of our experiment is the anterior nasal spine. Prior to constructing a statistical cubic model which would be used for detecting the location of the landmark from a given CT volume, reference points for the anterior nasal spine were manually chosen by a surgeon from several CT volume sets. The statistical cubic model is constructed by calculating weighted intensity means of these CT sets around the reference points. By finding the location where similarity function (squared difference function) has the minimal value with this model, the location of the landmark can be found from any given CT volume. Results : In this paper, we used 5 CT volumes to construct the statistical cubic model. The 20 CT volumes including the volumes, which were used to construct the model, were used for testing. The range of age of subjects is up to 2 years (24 months) old. The found points of each data are almost close to the reference point which were manually chosen by surgeon. Also it has been seen that the similarity function always has the global minimum at the detection point. Conclusion : Through the experiment, we have seen the proposed method shows the outstanding performance in searching the landmark point. This algorithm would make surgeons efficiently work with morphological informations of skull. We also expect the potential of our algorithm for searching the anatomic landmarks not only cranial landmarks.ope

Color-Ciratefi: A color-based RST-invariant template matching algorithm

Abstract-Template matching i

Efficient and robust shape retrieval from deformable templates

Computer Systems, Imagery and Medi

A Deep Learning-Based Tool for Face Mask Detection and Body Temperature Measurement

Due to the COVID-19 pandemic outbreak, wearing a mask and ensuring normal body temperature in overcrowded areas such as workplaces have become obligatory. In this paper, a deep learning-based tool for automatic mask detection and temperature measurement at the entrance of workplaces was developed to save costs of manual supervision and reduce human contact for safety concerns. Using Python, image/video processing techniques related to face and object detection are used to process image input from a webcam. A deep learning algorithm called MobileNetV2 was used to build the face mask detector model. Moreover, a non-contact thermal sensor, the MLX90614, along with Arduino, was employed to measure body temperature. The mask detection and temperature measurements are displayed correctly on a Graphical User Interface (GUI). Besides, an additional function related to the Internet of Things (IoT) was implemented, which sends high-temperature alerts to smartphones. It has been verified that the model can achieve an accuracy of about 98%. The developed system experiences a limitation when other objects are used to cover the mouth and nose in that they may still be classified as masks. However, compared to the mask detection systems available commercially, it can provide correct detection results when using the hand to pretend to be wearing a mask

Object Detection Based on Template Matching through Use of Best-So-Far ABC

Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution

Application of computer vision techniques for laser-based global localization of a mobile robot in a known, static environment

El problema de la localización global, es decir, la localización de un robot en un entorno conocido sin información sobre sus estados previos, ha sido ampliamente estudiado mediante diferentes métodos en la literatura. Un gran número de algoritmos han sido presentados y probados considerando varias condiciones. El objetivo de este trabajo es comparar diferentes métodos de representación de ocupación de celdillas construidos a partir de datos provistos por un láser utilizando técnicas de visión por computador. El enfoque seguido usa comparación visual de un mapa global con uno local, creado gracias al sensor láser. Esta elección permite una fácil integración con sistemas robóticos existentes al mismo tiempo que evita problemas típicos de soluciones puramente visuales, como la influencia de cambios de iluminación. Los dos algoritmos comparados—basados en área y en puntos característicos, respectivamente—han sido evaluados en un entorno interior simulado. Se ha asumido una situación estática, sin presencia de obstáculos móviles. El trabajo ha sido realizado durante un programa de movilidad en el extranjero, por lo que el documento se adapta en fondo y forma a las especificaciones de la Universidad de destino