FPGA-based Smart Camera for 3D wavelet-based image segmentation

Salem, Mohammed A.-M.; Markus, Appel; Winkler, Frank; Meffert, Beate

doi:10.1109/icdsc.2008.4635720

Cited by 15 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, There were few f eatures extracted in the copy-move algorithm from some of the images in the dataset using our 2 feature extraction methods; SIFT and SURF. One proposed solution can be using another feature extraction as block-based methods such as DCT [24] or DWT [3]. Also, our algorithm depends on dividing the images into blocks in the copy-move detection, however some objects can be divided between multiple blocks which can cause negatively affects the matching point step that compares the features of the blocks to one another.…”

Section: Discussionmentioning

confidence: 99%

Robust Image Forgery Detection Using Point Feature Analysis

Youssef

Safwat

Salem

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

Day for day it becomes easier to temper digital images. Thus, people are in need of various forgery image detection. In this paper, we present forgery image detection techniques for two of the most common image tampering techniques; copy-move and splicing. We use match points technique after feature extraction process using SIFT and SURF. For splicing detection, we extracted the edges of the integral images of Y , C b , and Cr image components. GLCM is applied for each edge integral image and the feature vector is formed. The feature vector is then fed to a SVM classifier. For the copy-move, the results show that SURF feature extraction can be more efficient than SIFT, where we achieved 80% accuracy of detecting tempered images. On the other hand, processing the image in Y C b Cr color model is found to give promising results in splicing image detection. We have achieved 99% true positive rate for detecting splicing images.

show abstract

Section: Discussionmentioning

confidence: 99%

Robust Image Forgery Detection Using Point Feature Analysis

Youssef

Safwat

Salem

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Temiz et al [6] used the rectification of the video frames to eliminate the perspective effect and then they used the optical flow [8] to estimate the vehicle speed, which is close to [24]. In [13] vehicle path estimated using accumulated ROI based on 3D wavelet transform. Yik [7] proposed a system that is based on Capturing information regarding the object of interest, this required the author to filter the right scene from the captured data frame sets and then applying the correct corresponding filters which are median filters.…”

Section: Literature Reviewmentioning

confidence: 99%

Moving Object Tracking using Inverse Perspective Mapping and Optical Flow

Almutairi¹

2020

IJATCSE

View full text Add to dashboard Cite

As objects moving at a pace create difficulty in identification so is the problem persisting in the moving vehicle recognition and tracking using passive vision sensor. This proposed tracking system is based on a camera mounted in a moving vehicle used to capture images to track another moving one. It considers tracking vehicles in open roads and highways. Inverse perspective mapping is adopted to extract a bird eye view of the road to subtract the standpoint results from the image. The image is then cropped and processed to extract the vehicle segment. The cropped image is returned to the camera view where the optical flow is utilized to estimate the vehicle direction and relative velocity based on the object's centroid displacement. The algorithm is tested on standard datasets as well as a new dataset created in Egypt's open roads. The results show robust tracking even with curved roads and fast computation which allow real-time processing even on embedded systems with low-computing resources.

show abstract

“…Despite its complexity, there has been an interest in 3-D DWT implementation on various platforms. Existing survey exhibits that the research can be classified into three categories: architecture development [63][64][65] , architecture with FPGA implementation [9], [10], [66][67][68], and finally architecture that has been implemented on other silicon platforms [69]. Since the aim and contribution of this work are on the reconfigurable architecture, the reviews and discussions will be focused on FPGA-based implementation only.…”

Section: Transform (Dct) and 3-d Discrete Fourier Transform (Dft) Exmentioning

confidence: 99%

“…Furthermore, the design consumes 63% of slice utilisation, and the maximum operating frequency is 100 MHz. Table 2.3 summarises the existing FPGA implementations of 3-D DWT [9], [66][67][68], in terms of FPGA devices, filter that has been used, as well as the design parameters of area and maximum frequency. …”

Section: Reconfigurable Architectures 39mentioning

confidence: 99%

Efficient reconfigurable architectures for 3D medical image compression

Ahmad

Amira

2009

2009 International Conference on Field-Programmable Technology

View full text Add to dashboard Cite

A AbstractRecently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.

show abstract

FPGA-based Smart Camera for 3D wavelet-based image segmentation

Cited by 15 publications

References 3 publications

Robust Image Forgery Detection Using Point Feature Analysis

Robust Image Forgery Detection Using Point Feature Analysis

Moving Object Tracking using Inverse Perspective Mapping and Optical Flow

Efficient reconfigurable architectures for 3D medical image compression

Contact Info

Product

Resources

About