scispace - formally typeset
Search or ask a question
JournalISSN: 2287-5255

IEIE Transactions on Smart Processing and Computing 

The Institute of Electronics Engineers of Korea
About: IEIE Transactions on Smart Processing and Computing is an academic journal published by The Institute of Electronics Engineers of Korea. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 2287-5255. Over the lifetime, 402 publications have been published receiving 1198 citations. The journal is also known as: IEEK Transactions on smart processing and computing.

Papers published on a yearly basis

Papers
More filters
Journal ArticleDOI
TL;DR: This review paper is focusing on techniques directly related to DCNNs, especially those needed to understand the architecture and techniques employed in GoogLeNet network.
Abstract: Over the past couple of years, tremendous progress has been made in applying deep learning (DL) techniques to computer vision. Especially, deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance on standard recognition datasets and tasks such as ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). Among them, GoogLeNet network which is a radically redesigned DCNN based on the Hebbian principle and scale invariance set the new state of the art for classification and detection in the ILSVRC 2014. Since there exist various deep learning techniques, this review paper is focusing on techniques directly related to DCNNs, especially those needed to understand the architecture and techniques employed in GoogLeNet network.

99 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive review of recent approaches to human action recognition based on depth maps, skeleton joints, and other hybrid approaches and focuses on the advantages and limitations of the existing approaches and on future directions.
Abstract: Human action recognition from a video scene has remained a challenging problem in the area of computer vision and pattern recognition. The development of the low-cost RGB depth camera (RGB-D) allows new opportunities to solve the problem of human action recognition. In this paper, we present a comprehensive review of recent approaches to human action recognition based on depth maps, skeleton joints, and other hybrid approaches. In particular, we focus on the advantages and limitations of the existing approaches and on future directions.

30 citations

Journal ArticleDOI
TL;DR: An algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking and the experimental results showed that the proposed algorithm is quite efficient for image matching.
Abstract: Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

29 citations

Journal Article
TL;DR: This paper presents a system used toautomatically recognize the road traffic control gestures of police officers, and utilizes Support Vector Machines (SVMs) to perform the gesture recognition.
Abstract: This paper presents a systemused toautomatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured inthe form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongstthe joints of the constructed human skeleton. We utilizeSupport Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for realtime application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

19 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202320
202238
20213
202020
201927
201836