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International Journal of Innovative and Emerging Research in Engineering
Android Application for Object RecognitionThe International journal of Multimedia & Its Applications
Development of an Android Application for Object Detection Based on Color, Shape or Local Features2017 •
2016 •
Object recognition has always been an area of interest for various researchers since decades. In this paper an attempt has been made to give a comparison between various techniques of object recognition mainly feature based approaches. In this paper an overview of the Famous and impressive technique by David Lowe, which is Scale Invariant Feature Transform (SIFT) has been given. Another very important technique called Speeded-Up Robust Feature Transform (SURF) has been used to conclude with certain interesting results. FAST is the third technique which has also been discussed in this paper. SIFT, SURF and FAST algorithms has been implemented on COIL dataset and a comparative analysis of these techniques has been given. The algorithms has been evaluated on two parameters i.e., number of features extracted and the time of execution. It has been seen that SIFT has detected more number of features as compared to SIFT and FAST. But the times of execution taken by SURF is comparatively l...
With the advancement of modern technologies areas related to robotics and computer vision, real time image processing has become a major technology under consideration. So I tried a novel approach for capturing images from the computer web cam in real time environment and process them as we are required. By using open source computer vision library (OpenCV for short), an image can be captured on the bases of its hue, saturation and color value (HSV) range. The basic library functions for image handling and processing are used. Basic library functions are used for loading an image, creating windows to hold image at run time, saving images, and to differentiate images based on their color values. I have also applied function to threshold the output image in order to decrease the distortion in it. While processing, the images are converted from their basic scheme Red, Green, and Blue (RGB) to a more suitable one that is HSV. 1. Introduction The research purpose of computer vision aims to simulate the manner of human eyes directly by using computer. Computer vision is such kind of research field which tries to percept and represent the 3D information for world objects. Its essence is to reconstruct the visual aspects of 3D object by analyzing the 2D information extracted accordingly [1]. Real life 3D objects are represented by 2D images. The process of object detection analysis the input image and to determine the number, location, size, position of the objects. Object detection is the base for object tracking and object recognition, whose results directly affect the process and accuracy of object recognition. The common object detection method is: color-based approach, detecting objects based on their color values. The method is strong adaptability and robustness, however, the detection speed needs to be improved, because it requires test all possible windows by exhaustive search and has high computational complexity.
Due to the importance of image registration and development of image acquisition technology to obtain higher quality images, besides the importance of smart phones, also for the wide spread of smart phones that run on the Android system and the lack of application to make the process of registering images under this environment, this paper aims to propose a system to register images under Android environment, by using Java language and depending on the features based methods that do not need user intervention to construct actual and practical product, with the possibility of implementing it on the captured images directly (online).The proposed system algorithm is registering two images (reference and sensed) in five stages. The images adapted in this paper with extensions (GIF, BMP, PNG, JPEG) color and gray, representing views of scenes in an internal and external environment taken from different devices and multi-view and multi-temporal. It has been shown that it is not affected by the kind of the used images, and this strengthens the system to apply it on other operating system, whereas the Speeded Up Robust Features (SURF) algorithm which used in proposed algorithm gave good experimental results comparing with the Scale Invariant Feature Transform (SIFT) algorithm, which reflect on the produced images effectively. Whereas the average value of (RMSE) for applied images was equal to 7.0 while the average value of (PSNR) was equal to 31.3dB. Besides, the high speed in registration.
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
Instant segmentation and feature extraction for recognition of simple objects on mobile phones2010 •
Keeping in mind the challenges faced by the visually impaired people we had come with an Android application. In this paper we present an Android application dedicated to the aid of visually impaired or blind users. The main aim of this application is to reduce object detection procedure and give more useful and reliable function in single application. The software modules are designed for Android operating system. The goal of this project is creating virtual guide application which provides services like Object detection, Scene detection, Motion detection in which result generated will be in voice format with the help of speech synthesizer enable in smart phones.
Feature detection and matching are used in image registration, object tracking, object retrieval etc. There are number of approaches used to detect and matching of features as SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), FAST, ORB etc. SIFT and SURF are most useful approaches to detect and matching of features because of it is invariant to scale, rotate, translation, illumination, and blur. In this paper, there is comparison between SIFT and SURF approaches are discussed. SURF is better than SIFT in rotation invariant, blur and warp transform. SIFT is better than SURF in different scale images. SURF is 3 times faster than SIFT because using of integral image and box filter. SIFT and SURF are good in illumination changes images. Keywords-SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Feature), invariant, integral image, box filter
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