An Efficient Cloud based Image Target Recognition SDK for Mobile Applications

Smart phones have exploded in popularity in recent years, becoming ever more sophisticated and capable specially when these devices tries to access the shared pool of computing resources provided by the cloud, on demand. Mobile services such as image target recognition SDK may enrich their functionality by delegating heavy tasks to the clouds as the remote processing. This paper proposes an image target recognition SDK based on cloud with the main goal of lightweight implementation on mobile devices based on processing performed over cloud. In such circumstances, the focus of the proposed image target recognition SDK needs to be on effectiveness, robustness and simplicity, while still preserving high level of functionality (i.e. good recognition). Application areas involve android library development, pattern recognition and web portal development. The applications mentioned in this paper bring an added value by being success stories for mobile cloud computing domain in genera

Efficient Object Detection and Intelligent Information Display Using YOLOv4-Tiny

This study aims to develop an innovative image recognition and information display approach based on you only look once version 4 (YOLOv4)-tiny framework. The lightweight YOLOv4-tiny model is modified by replacing convolutional modules with Fire modules to further reduce its parameters. Performance reductions are offset by including spatial pyramid pooling, and they also improve the model’s detection ability for objects of various sizes. The pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC) 2012 dataset are used, the proposed modified YOLOv4-tiny architecture achieves a higher mean average precision (mAP) that is 1.59% higher than its unmodified counterpart. This study addresses the need for efficient object detection and recognition on resource-constrained devices by leveraging YOLOv4-tiny, Fire modules, and SPP to achieve accurate image recognition at a low computational cost

Polarization-Tunable Antenna-Coupled Infrared Detector

An antenna-coupled infrared detector with polarization tuning over approximately 90 degrees by application of a bias voltage in the range of a few hundred millivolts. This mechanism of polarization tuning eliminates the need for bulk-optical polarization filters. When integrated into focal plane arrays, these detectors can be used in remote-sensing systems to facilitate enhanced image recognition, feature extraction and image-clutter removal. A preferred version of the antenna has longitudinal metal antenna arms extending outward from an infrared(IR) sensor in a spiral pattern, polarization tuning devices connected between the antenna arms, and a voltage for controlling the polarization tuning devices, wherein the polarization tuning devices enable real-time control of current distribution in the arms. The infrared(IR) sensors can be tunnel diodes, schottky diodes, photovoltaics, photoconductors, and pyroelectrics. Application areas can include earth-resource mapping, pollution monito

Wavelength Tunable Antenna Coupled Infrared Detectors

A tuned antenna-coupled infrared detector is made possible by application of a bias voltage in the range of a few hundred millivolts. The use of first and second antenna arms connected to the detector makes possible polarization tuning which eliminates the need for bulk-optical polarization filters. An alternative tuned detector is one in which the antenna is frequency tuned by a capacitative device to make the detector particularly responsive to 8 um to 12 um infrared radiation. When integrated into focal plane arrays, these detectors can be used in remote-sensing systems to facilitate enhanced image recognition, feature extraction and image-clutter removal. One preferred version of the polarization tuned antenna has longitudinal metal antenna arms extending outward from an infrared(IR) sensor in a spiral pattern, with polarization tuning devices connected between the antenna arms, and a voltage for controlling the polarization tuning devices, wherein the polarization tuning devices

Spectral fuzzy classification system for target recognition

The goal of this paper is to present an algorithm for terrain matching, leveraging an existing fuzzy clustering algorithm, and modifying it to its supervised version, in order to apply the algorithm to georegistration and, later on pattern recognition. Georegistration is the process of adjusting one drawing or image to the geographic location of a "known good" reference drawing, image, surface or map, The georegistration problem can be treated as a pattern recognition problem; and it can be applied to the target detection problem. The terrain matching algorithm will be based on fuzzy set theory as a very accurate method to represent the imprecision of the real world, and presented as a multicriteria decision making problem. The energy emitted and reflected by the Earth's surface has to be recorded by relatively complex remote sensing devices that have spatial, spectral and geometrical resolution constraints. Errors usually slip into the data acquisition process. Therefore, it is necessary to preprocess the remotely sensed data, prior to analyzing it (image restoration, involving the correction of distortion, degradation and noise introduced during the rendering process). In this paper we shall assume that all these problems have been solved, focusing our study on the image classification of a corrected image being close enough, both geometrically and radiometrically, to the radiant energy characteristics of the target scene. In particular, at a first stage we consider each pixel individually; and a class will be assigned to each pixel, taking into account several values measured in separate spectral bands. Then we shall describe an automatic detection system based on a previous algorithm developed in A. Del Amo et al., introducing now the fuzzy partition model proposed by A. Del Amo et al. A first phase will lead to a spectral definition of patterns; and a second phase will lead to classification and recognition. Similarity measures will then allow us to evaluate the degree to which a pixel can be associated to each pattern, or determine if a pattern is similar enough to a subimage of the main image, to establish that a target we are looking for can be found on that image