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A real-time hardware architecture based on scale-invariant feature transform algorithm (SIFT) feature extraction with parallel technology has been introduced in this paper. The proposed parallel hardware architecture could be able to extract feature via a Field-Programmable Gate Array (FPGA) chip efficiently, which provided the real-time performance and the similar accuracy with software implementation...
As more and more human-machine interactive applications call for higher frame rate and lower delay to get a better experience, there is an inevitable need for high frame rate and ultra-low delay image processing system. Current existing works based on vision chip target on video with simple patterns or simple shapes in order to get a higher speed, which is reasonable in the first trial of this new...
In this paper, a real-time vehicle detection system is designed and implemented on an FPGA (Field Programmable Gate Array). The system is composed of an infrared camera and an image acquisition and processing board developed by our research team. An FPGA chip and a DSP chip are embedded in the image board as the major calculation units, which make realtime computation possible. First, edge features...
Traffic signal detection has long been an important function in an advanced driver assistance system (ADAS). This paper presents a complete system design based on the techniques of blob detection, histogram of oriented gradients (HOG) and support vector machine (SVM). Blob detection is applied to detect potential candidates, and then HOG and SVM is for feature classification. A novel hardware/software...
Texture descriptors are a powerful tool for 2D scene features extraction. They can be computed for whole image or for regions of interests obtained from various object detection methods. In case when foreground objects mask obtained from background subtraction or optical-flow thresholding is to be used, the connected components analysis is needed first. The texture descriptor is then computed for...
In recent years the use of real-time face detection and face recognition for surveillance, human-machine interfaces and other applications has increased and thus the need for high power, low cost implementations has been posed. In embedded implementations, the computing power needed for face detection system calls for a custom designed processor. In this paper we will discuss implementations of alternatives...
The domain of vision and navigation often includes applications for feature tracking as well as simultaneous localization and mapping (SLAM). As these problems require computationally demanding solutions, it is challenging to achieve high performance without sacrificing the fidelity of results or otherwise consuming excessive amounts of energy. Our goal then is to accelerate the applications in this...
Real-time face recognition by computer systems is required in many commercial and security applications since it is the only way to protect privacy and security. On the other hand, face recognition generates huge amounts of data in real-time. Filtering out meaningful data from this raw data with high accuracy is a complex task. Most of the existing techniques primarily focus on the accuracy aspect...
SIFT is regarded as one of the most powerful feature point detection algorithms in the world. The Orientation Calculation Part, defining major orientation of feature points, enables selected image features to be invariant to rotation changes. In this paper, we propose an FPGA-implementable hardware accelerator for this part. By introducing LUT-Based Square Root Computation and Shifting-Based Orientation...
Many fatal accidents have happened due to drivers failing to stop at stop signs. A stop sign recognition system could be used to reduce the risk of accidents by warning the driver when a vehicle approaches a stop sign at an unexpected speed. In this paper, we describe the implementation of a real-time vision-based stop sign recognition system on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA)...
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