International Conference on Communication and Signal Processing 

About: International Conference on Communication and Signal Processing is an academic conference. The conference publishes majorly in the area(s): Feature extraction & Wireless sensor network. Over the lifetime, 2119 publications have been published by the conference receiving 13359 citations.

A review on deep convolutional neural networks

Abstract: The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process But Convolutional Neural Networks (CNN) have provided an alternative for automatically learning the domain specific features Now every problem in the broader domain of computer vision is re-examined from the perspective of this new methodology Therefore it is essential to figure-out the type of network specific to a problem In this work, we have done a thorough literature survey of Convolutional Neural Networks which is the widely used framework of deep learning With AlexNet as the base CNN model, we have reviewed all the variations emerged over time to suit various applications and a small discussion on the available frameworks for the implementation of the same We hope this piece of article will really serve as a guide for any neophyte in the area

Tomato plant disease classification in digital images using classification tree

Abstract: The applications based on image processing for plant disease recognition and classification is the wide area of research these days. These applications are useful for timely recognition of plant disease. The disease like fungal, bacterial and virus are the destructive disease for any plant. In the study, five types of tomato diseases i.e. tomato late blight, Septoria spot, bacterial spot, bacterial canker, tomato leaf curl and healthy tomato plant leaf and stem images are classified. The classification conducted by extracting color, shape and texture features from healthy and unhealthy tomato plant image. The feature extraction process is done after the segmentation process. Extracted features from segmented images fed to classification tree. Finally, the disease classification was based on these six different types of classes. The classification of six types of tomato images yielded overall 97.3% of classification accuracy.

Transfer Learning with ResNet-50 for Malaria Cell-Image Classification

Abstract: Malaria is an infectious disease caused by single-celled parasite of plasmodium group. The disease is more often spread by an Infected Female Anopheles mosquito. In 2017 alone 219 million cases and nearly 435,000 deaths were reported, with more than 40% of global population at risk. In spite of many advanced evaluation techniques for identifying the infection, microscopists at resource constrained regions face challenge in improving the diagnostic accuracy. Deep learning based classification of cell images prevent the wrong diagnostic decisions. This paper focuses on the implementation of Transfer learning based classification of malarial infected cells to improve the diagnostic accuracy. The experimental results show that transfer learning performs well on microscopic cell-images

Object Detection Algorithms for Video Surveillance Applications

Abstract: Object Detection algorithms find application in various fields such as defence, security, and healthcare. In this paper various Object Detection Algorithms such as face detection, skin detection, colour detection, shape detection, target detection are simulated and implemented using MATLAB 2017b to detect various types of objects for video surveillance applications with improved accuracy. Further, various challenges and applications of Object Detection methods are elaborated.

Human Skin Detection Using RGB, HSV and YCbCr Color Models

Abstract: Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.