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Edge detection is often regarded as a basic step in range image processing by virtue of its crucial effect. The majority of existing edge detection methods cannot satisfy the requirement of efficiency in many industrial applications due to huge computational costs. In this paper, a novel instantaneous method, named RIDED-2D is proposed for denoising and edge detection for 2D scan line in range images. In the method, silhouettes of 2D scan line are classified into eight types by defining a few new coefficients. Several discriminant criteria on large noise filtering and edge detection are stipulated based on qualitative feature analysis on each type. Selecting some feature point candidates, a practical parameter learning method is provided to determine the threshold set, along with the implementation of an integrated algorithm by merging calculation steps. Because all the coefficients are established based on distances among the points or their ratio, RIDED-2D is inherently invariant to translation and rotation transformations. Furthermore, a forbidden region approach is proposed to eliminate interference of the mixed pixels. Key performances of RIDED-2D are evaluated in detail by including computational complexity, time expenditure, accuracy and stability. The results indicate that RIDED-2D can detect edge points accurately from several real range images, in which large noises and systematic noises are involved, and the total processing time is less than 0.1 millisecond on an ordinary PC platform using the integrated algorithm. Comparing with other state-of-the-art edge detection methods qualitatively, RIDED-2D exhibits a prominent advantage on computational efficiency. Thus, the proposed method qualifies for real-time processing in stringent industrial applications. Besides, another contribution of this paper is to introduce CPU clock counting technique to evaluate the performance of the proposed algorithm, and suggest a convenient and objective way to estimate the algorithm's time expenditure in other platforms.

Signal and image processing applications require high computational power with the ability to experiment different algorithms involving matrix transforms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed to obtain high performance at an economical price. However, the users must program FPGAs at a very low level and must have a detailed knowledge of the architecture of the device being used. In trying to reconcile the dual requirements of high performance and the ease of development, this paper reports the design and realization of the Fast Fourier Transforms (FFTs) using a FPGA-based environment, which enables system designer to meet different system requirements (i.e., chip area, speed, memory, etc.) for a range of signal processing and imaging applications. The use of the proposed environment has been proven by the developing a high-level FPGA-based parametrizable image processing system for frequency-domain filtering application. The system achieves real-time image filtering performance exceeding those of currently available solutions by an order of magnitude in frame rate and input image size.

The aim of this study is to implement an algorithm for face recognition, based on fast fourier transform (FFT), on the field programmable gate array (FPGA) chip. Implemented program included the initialization process of two single-IP-core ROM blocks, each with an image of a human face, which are sent to the real components of two-channel IP CoreFFT block. The result of classification could be displayed in the form of either a word “yes” or “no” on the seven-segment display or the information about the reference to the folder with the found match face. Due to the lack of memory on the chip, the results are discussed based on the results obtained by the simulation, whilst the implemented part of the system included displaying images on VGA monitor and result of the algorithm shown on seven-segment display or realized as a software solution in Matlab. The results show 79% accuracy and the advantage of presented system lies in the possibility of working with images in real time. The results obtained in this study can be a good starting point in the implementation of complex algorithms for face recognition using all the benefits that FPGAs offer.

Mobile target tracking remains a significant issue in smart cities. Due to complex changes in time and space of targets, real-time tracking remains a challenging problem. As a result, this paper proposes a real-time tracking approach for moving objects by combining the advantages of YOLOv7 and SORT algorithms. First, we use the YOLOv7 algorithm for object detection, which has the characteristics of high accuracy and efficiency. Then, we apply the SORT algorithm to the target tracking stage, which estimates and updates the target state through Kalman filtering. The collaborative function of the two parts is expected to achieve high-quality tracking of moving targets. Besides, this paper also demonstrates experiments and analysis on image datasets. The experimental results show that the proposed algorithm has achieved good performance in real-time tracking of moving targets. Compared with traditional methods, it can more accurately predict the position and trajectory of targets and has better real-time performance. In addition, the proposed algorithm is equally effective for target tracking in complex scenes, such as multi-target tracking and target occlusion. Future research can further optimize the performance of algorithms to cope with more complex scenarios and problems.