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Babu, Arun
- Comparative Advertising and the Consumer-Changing Dynamics
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PDF Views:93
Authors
Affiliations
1 IIT Kharagpur, Kharagpur, West Bengal-721302, IN
1 IIT Kharagpur, Kharagpur, West Bengal-721302, IN
Source
Journal of Intellectual Property Rights, Vol 22, No 3 (2017), Pagination: 113-120Abstract
Advertisements are designed to introduce products and services to prospective consumers. Every company wants to leave most impact on a consumer in the short duration of an advertisement and hence, advertising wars between market players dealing in similar products/services is not new. In common parlance, this is known as “comparative advertising”. These comparisons are sometimes veiled and sometimes blatant. This paper discus the law related to comparative advertising in India. It discus issues involved in comparative advertising and looks at more recent evolution of case law wherein courts have factored consumer interest in deciding cases of comparative advertisements. The paper notes legal position in other jurisdictions and highlights various competing interests involved in cases related to comparative advertisements.Keywords
Comparative Advertising, Trademark, Trademark Infringement, Product Disparagement, Unfair Competition, Injunction, Trade Marks Act, 1999, Monopolies and Restrictive Trade Practices (MRTP) Act, 1969, Competition Act, 2002, Advertising Standards Council of India, Consumer Complaints Council.Full Text
- CNN Transfer Learning for Detection, Counting and Segmentation of Coconut Palms from Satellite Images
Abstract Views :173 |
PDF Views:1
Authors
Affiliations
1 Indian Institute of Remote Sensing, Indian Space Research Organisation - Dehradun, IN
1 Indian Institute of Remote Sensing, Indian Space Research Organisation - Dehradun, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2475-2482Abstract
Several Free and Open Source (FOSS) tools use Neural Networks for detection of objects from images and videos captured from hand-held imaging devices. Satellite based Remote Sensing images offer wide area coverage and hold potential for detecting, counting and mapping manmade objects, trees, etc., but, have embedded geospatial information and often have more than three bands. Hence, the existing FOSS tools are not able to directly process Remote Sensing images for Computer Vision (CV) applications. This research aims to devise a methodology to adapt a FOSS CV tool, namely the TensorFlow Object Detection (TFOD) API, for detection, counting and segmentation of coconut palms from satellite images and ascertain if the technique can facilitate automated census of coconut palms. Dataset of coconut palm crowns was custom-created using multi-band images from World View-3 satellite. The images were pan-sharpened, cropped and labelled. SSDLite MobileNet V2 CNN, which was pre-trained on COCO dataset, was subjected to transfer learning using coconut data on Tesla K80x GPU. This re-trained CNN could successfully detect and count coconut palms with F-1 score more than 96 %. Histogram thresholds were used to segment and delineate each detected coconut palm crown with 87 % accuracy. Assessment of relative health status of coconut palms was mapped using the Normalised Difference Red-Edge Index derived from satellite images. This study demonstrated that TFOD API can indeed be adapted for object detection and segmentation from Remote Sensing images, albeit with some limitations.Keywords
Computer Vision, Object Detection, Segmentation, TensorFlow Object Detection API, Satellite Image.
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