The effects of destination image and perceived risk on revisit intention: a study in the south eastern coast of Sabah, Malaysia

This study investigated the effects of destination image and perceived risk on revisit intention in the South Eastern Coast of Sabah, Malaysia. A total of 171 questionnaires were collected from international tourists through a self-administered questionnaire. The result of this study identified that three dimensions of destination image (travel environment, natural attraction, entertainment, and events) had significant effects on revisit intention. However, perceived risk was not important to the tourists’ revisit intention. The findings have implications on the tourism industry, especially for key players such as the tourism board and travel companies. It also serves as a reference to destinations with a similar risk background

Accelerated hardware video object segmentation: From foreground detection to connected components labelling

This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency

Analyzing energy savings in an FPGA video processing system using dynamic partial reconfiguration

Dynamic Partial Reconfiguration (DPR) can be a useful tool for maximizing FPGA performance while minimizing power consumption and FPGA size requirements. This work explores the application of the DPR technique in a computer vision application that implements two different edge detection algorithms (FASTX and Sobel). This technique could allow for a similar computer vision system to be realized on a smaller, low-power chipset. Different algorithms can have unique characteristics that yield better performance in certain scenarios; the best algorithm for the current scenario may change during runtime. However, implementing all available algorithms in hardware increases the space and power requirements of the FPGA. We analyze a system that can load an individual edge detection algorithm into the computer vision processing pipeline with negligible interruptions to data processing by using DPR. This application targets the Xilinx UltraScale+ ZCU106 and is able to maintain the same functionality while using an average of 4% less energy when compared to the non-DPR implementation. Additionally, the FPGA utilization for this application is 15% less than that of the traditional implementation that includes both algorithms on the chip at once. These results demonstrate that this technique could allow a similar computer vision system to be realized on a smaller, low-power chipset

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

On the Hardware/Software Design and Implementation of a High Definition Multiview Video Surveillance System

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Real-time multi-camera video acquisition and processing platform for ADAS

The paper presents the design of a real-time and low-cost embedded system for image acquisition and processing in Advanced Driver Assisted Systems (ADAS). The system adopts a multi-camera architecture to provide a panoramic view of the objects surrounding the vehicle. Fish-eye lenses are used to achieve a large Field of View (FOV). Since they introduce radial distortion of the images projected on the sensors, a real-time algorithm for their correction is also implemented in a pre-processor. An FPGA-based hardware implementation, re-using IP macrocells for several ADAS algorithms, allows for real-time processing of input streams from VGA automotive CMOS cameras