Development of An Android Application for Object Detection Based on Color, Shape, or Local Features

2017-03-10 · Lamiaa A. Elrefaei, Mona Omar Al-musawa, Norah Abdullah Al-gohany

Object detection and recognition is an important task in many computer vision applications. In this paper an Android application was developed using Eclipse IDE and OpenCV3 Library. This application is able to detect objects in an image that is loaded from the mobile gallery, based on its color, shape, or local features. The image is processed in the HSV color domain for better color detection. Circular shapes are detected using Circular Hough Transform and other shapes are detected using Douglas-Peucker algorithm. BRISK (binary robust invariant scalable keypoints) local features were applied in the developed Android application for matching an object image in another scene image. The steps of the proposed detection algorithms are described, and the interfaces of the application are illustrated. The application is ported and tested on Galaxy S3, S6, and Note1 Smartphones. Based on the experimental results, the application is capable of detecting eleven different colors, detecting two dimensional geometrical shapes including circles, rectangles, triangles, and squares, and correctly match local features of object and scene images for different conditions. The application could be used as a standalone application, or as a part of another application such as Robot systems, traffic systems, e-learning applications, information retrieval and many others.

📄 PDF Abstract BibTeX arXiv:1703.03848

Code (0)

등록된 구현이 없습니다.

Tasks

Information RetrievalObjectobject-detection Object Detection Retrieval

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

2017-05-12 · TonTon Hsien-De Huang, Hung-Yu Kao

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits we…

Android Malware Detection Feature Engineering Malware Detection

Adversarial Samples on Android Malware Detection Systems for IoT Systems

2019-02-12 · Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu 외

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gr…

Android Malware Detection Malware Detection

Low Cost Embedded Vision System For Location And Tracking Of A Color Object

2022-07-28 · Diego Ayala, Danilo Chavez, Leopoldo Altamirano Robles

This paper describes the development of an embedded vision system for detection, location, and tracking of a color object; it makes use of a single 32-bit microprocessor to acquire image data, process, and perform action…

Object

Android Malware Category and Family Detection and Identification using Machine Learning

2021-07-05 · Ahmed Hashem El Fiky, Ayman El Shenawy, Mohamed Ashraf Madkour

Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applicati…

BIG-bench Machine Learning Malware Family Detection

Object Localization and Size Estimation from RGB-D Images

2018-08-02 · ShreeRanjani SrirangamSridharan, Oytun Ulutan, Shehzad Noor Taus Priyo, Swati Rallapalli 외

Depth sensing cameras (e.g., Kinect sensor, Tango phone) can acquire color and depth images that are registered to a common viewpoint. This opens the possibility of developing algorithms that exploit the advantages of bo…

Object Object Localization