An FPGA sliding window-based architecture harris corner detector

Amăricăi, Alexandru; Gavriliu, Constantina-Elena; Boncalo, Oana

doi:10.1109/fpl.2014.6927402

Cited by 33 publications

(18 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming n pairs of matching points are got through feature point matching and the coordinate values of the ith pair in I 1 and I 2 are (x i1 ,y i1 ) and (x i2 ,y i2 ) respectively, then calculate the coordinate offsets between them: (8) Calculate the number of the matched point pairs with the same Δx and Δy. The Δx and Δy of which the number is the largest are considered as the offsets in direction x and direction y of the two images.…”

Section: Determination Of the Best Matching Pointsmentioning

confidence: 99%

“…Add the values with the same coefficients in the image window and filter the eight values. Make lookup tables with the size of 2 8 and store the adding results of the products of the ith place of the eight values and their responding Gaussian template coefficients. Make an eight-bit address of lookup table with the ith place of each figure, then output the ith place of the figure having been processed by Gaussian filter.…”

Section: Significant Feature Point Extractionmentioning

confidence: 99%

“…Harris corner detection, which has good parallelism, can perform real-time detection on FPGA and accurately extract corners when images rotate or grayscale changes [8]. The point feature describing method used in SIFT algorithm can provide the matching with highly multi-faceted information of feature points and improve matching precision.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Chen

Han

Zhang

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.To solve the problems such as low matching precision, high algorithm complexity and poor real-time in real-time panoramic image mosaic, this paper makes full use of the significance and parallelism of Harris corners as well as the invariance of feature describing methods in light intensity changes, translation and rotation, and proposes a real-time panoramic image mosaic algorithm which uses Harris corner detection and is logically implemented on FPGA. According to the basic features of classical algorithms and the properties of FPGA, several modules like feature point extraction, description and matching are optimized based on the logical implementation of FPGA. The optimized system realizes the high-precision matching in real time. The new algorithm collects images of 256 × 256 pixels by CCD camera on Spartan-6 hardware platform of Xilinx. After going through the algorithm, the mosaic images will finally be output on HD display in the form of DVI. The results show that the new algorithm based on FPGA has high precision, good real-time and robustness.

show abstract

Section: Determination Of the Best Matching Pointsmentioning

confidence: 99%

Section: Significant Feature Point Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Chen

Han

Zhang

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…There are several computer vision applications that used these corner detection algorithms successfully [39,49,40], and several smart cameras included them in their self-contained algorithms [6,2]. Unfortunately, in several applications the corner detection algorithms are not compatible with high textured regions [22] or can not perform real-time processing on full HD images [24,33,4]. We can mention the three most important limitations affecting the current corner detection algorithms:…”

Section: Performance Of Corner Detection Algorithmsmentioning

confidence: 99%

“…Furthermore, most accurate algorithms like Shi & Tomasi/Harris & Stephens, have relatively complex mathematical formulation (quotients, roots, etc. ), that require high hardware resources [24,33,4], hardware requirements are ×4 more than corner detection algorithms with straightforward FPGA implementation. On the other hand, corner detection algorithms with straightforward FPGA implementation like FAST [35] have low hardware demand but accuracy and robustness is low.…”

Section: Performance Of Corner Detection Algorithmsmentioning

confidence: 99%

Robust feature extraction algorithm suitable for real-time embedded applications

Aguilar-González

Arias-Estrada

Berry

2017

J Real-Time Image Proc

View full text Add to dashboard Cite

International audienceSmart cameras integrate processing close to the image sensor, so they can deliver high-level information to a host computer or high-level decision process. One of the most common processing is the visual features extraction since many vision-based use-cases are based on such algorithm. Unfortunately, in most of cases, features detection algorithms are not robust or do not reach real-time processing. Based on these limitations, a feature detection algorithm that is robust enough to deliver robust features under any type of indoor / outdoor scenarios is proposed. This was achieved by applying a non-textured corner filter combined to a subpixel refinement. Furthermore, an FPGA architecture is proposed. This architecture allows compact system design, real-time processing for Full HD images (it can process up to 44 frames/91.238.400 pixels per second for Full HD images), and high efficiency for smart camera implementations (similar hardware resources than previous formulations without subpixel refinement and without non-textured corner filter). For accuracy/robustness, experimental results for several real world scenes are encouraging and show the feasibility of our algorithmic approach

show abstract

Geometric Transformations

2023

Design for Embedded Image Processing on FPGAs

View full text Add to dashboard Cite

An FPGA sliding window-based architecture harris corner detector

Cited by 33 publications

References 5 publications

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA

Robust feature extraction algorithm suitable for real-time embedded applications

Geometric Transformations

Contact Info

Product

Resources

About