Selected Papers from IEEE ICASI 2019

The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale

Colour depth-from-defocus incorporating experimental point spread function measurements

Depth-From-Defocus (DFD) is a monocular computer vision technique for creating depth maps from two images taken on the same optical axis with different intrinsic camera parameters. A pre-processing stage for optimally converting colour images to monochrome using a linear combination of the colour planes has been shown to improve the accuracy of the depth map. It was found that the first component formed using Principal Component Analysis (PCA) and a technique to maximise the signal-to-noise ratio (SNR) performed better than using an equal weighting of the colour planes with an additive noise model. When the noise is non-isotropic the Mean Square Error (MSE) of the depth map by maximising the SNR was improved by 7.8 times compared to an equal weighting and 1.9 compared to PCA. The fractal dimension (FD) of a monochrome image gives a measure of its roughness and an algorithm was devised to maximise its FD through colour mixing. The formulation using a fractional Brownian motion (mm) model reduced the SNR and thus produced depth maps that were less accurate than using PCA or an equal weighting. An active DFD algorithm to reduce the image overlap problem has been developed, called Localisation through Colour Mixing (LCM), that uses a projected colour pattern. Simulation results showed that LCM produces a MSE 9.4 times lower than equal weighting and 2.2 times lower than PCA. The Point Spread Function (PSF) of a camera system models how a point source of light is imaged. For depth maps to be accurately created using DFD a high-precision PSF must be known. Improvements to a sub-sampled, knife-edge based technique are presented that account for non-uniform illumination of the light box and this reduced the MSE by 25%. The Generalised Gaussian is presented as a model of the PSF and shown to be up to 16 times better than the conventional models of the Gaussian and pillbox

A Convolutional Neural Network (CNN) based Pill Image Retrieval System

Several works have been done in the area of image retrieval systems, and many are still trying to provide improvements for a better model for retrieving said images. Image segmentation using clustering techniques is one of the most used approaches. There are various clustering methods available, but the non-linear k-means clustering technique is the most used method. In the following research, a model of retrieving images using a non-linear classifier aided with a convolutional neural network is proposed. Both algorithms were exploited and paired in terms of feature extraction and classification. Comprehensive evaluations over a dataset containing over 7,000 pill images of 1,000 pill types obtained from the National Library of Medicine database demonstrate significant success during the data classification using the proposed model

Nuclei segmentation of histology images based on deep learning and color quantization and analysis of real world pill images

Medical image analysis has paved a way for research in the field of medical and biological image analysis through the applications of image processing. This study has special emphasis on nuclei segmentation from digitized histology images and pill segmentation. Cervical cancer is one of the most common malignant cancers affecting women. This can be cured if detected early. Histology image feature analysis is required to classify the squamous epithelium into Normal, CIN1, CIN2 and CIN3 grades of cervical intraepithelial neoplasia (CIN). The nuclei in the epithelium region provide the majority of information regarding the severity of the cancer. Segmentation of nuclei is therefore crucial. This paper provides two methods for nuclei segmentation. The first approach is clustering approach by quantization of the color content in the histology images uses k-means++ clustering. The second approach is deep-learning based nuclei segmentation method works by gathering localized information through the generation of superpixels and training convolutional neural network. The other part of the study covers segmentation of consumer-quality pill images. Misidentified and unidentified pills constitute a safety hazard for both patients and health professionals. An automatic pill identification technique is essential to address this challenge. This paper concentrates on segmenting the pill image, which is crucial step to identify a pill. A color image segmentation algorithm is proposed by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. The algorithm manages to supersede the challenges due to various backgrounds and lighting conditions of consumer-quality pill images --Abstract, page iii

A Novel Efficient Algorithm for Locating and Tracking Object Parts in Low Resolution Videos

This is the published version. Copyright De GruyterIn this paper, a novel efficient algorithm is presented for locating and tracking object parts in low resolution videos using Lowe's SIFT keypoints with a nearest neighbor object detection approach. Our interest lies in using this information as one step in the process of automatically programming service, household, or personal robots to perform the skills that are being taught in easily obtainable instructional videos. In the reported experiments, the system looked for 14 parts of inanimate and animate objects in 40 natural outdoor scenes. The scenes were frames from a low-resolution instructional video on cleaning golf clubs containing 2,405 frames of 180 by 240 pixels. The system was trained using 39 frames that were half-way between the test frames. Despite the low resolution quality of the instructional video and occluded training samples, the system achieved a recall of 49 % with a precision of 71 % and an Fl of 0.58, which is better than that achieved by less demanding applications. In order to verify that the reported results were not dependent on the specific video, the proposed technique was applied to another video and the results are reported

Sistema de reconocimiento de iris implementado en un procesador digital de señales

Los sistemas de seguridad para el control de acceso a lugares restringidos se han convertido en estos últimos años en un tema de bastante importancia, no solo para las grandes empresas, sino también para la gente del común, que quiere mantener a salvo y bajo su protección sus bienes más preciados de manos inescrupulosas; problema que se ha visto durante toda la historia de la humanidad. Debido a lo anterior, en este proyecto se decidió desarrollar en MatLab® e implementar en el ADSP-BF533 EZ-Kit Lite® de Analog Devices un sistema de reconocimiento de iris que hace uso de la Transformada Wavelet de Haar 2D y las herramientas del procesamiento digital de imágenes como los filtros espaciales, detector de bordes y análisis de envolvente convexa, para segmentar, normalizar, codificar y validar la imagen del iris de un sujeto de prueba tomado de la base datos CASIA Iris Databa V4.0 contra el sujeto previamente registrado en el sistema. Los algoritmos creados para MatLab® están desarrollados utilizando solo funciones lógicas y matemáticas básicas, es decir, sin el uso de toolbox especializados en el procesamiento digital de imágenes; esto, con el objetivo de poder desarrollar primeramente en MatLab® y posteriormente implementar los mismos algoritmos en el DSP. Igualmente esto se realizó con el objetivo de finalmente evaluar el rendimiento del sistema en ambas plataformas y obtener un punto de comparación entre ambos sistemas. Posteriormente se procedió a realizar un conjunto de pruebas tanto en MatLab® como en el ADSP-BF533 EZ-Kit Lite®, utilizando el método de validación cruzada Leave One Out para determinar el porcentaje de aceptabilidad y confiabilidad del sistema de reconocimiento de iris; las cuales arrojaron un mejor resultado en ambas pruebas para el sistema desarrollado en MatLab®; sin embargo el sistema desarrollado en el DSP ofrece ventajas significativas de implementación real al ser este un sistema embebido especialmente diseñado para el procesamiento digital de imágenes