Real-Time Imaging最新文献

In this paper, a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so-called peer group concept. Compared to the vector median filter and other commonly used multichannel filters, the proposed technique consistently yields very good results in suppressing both the random and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of color images in real-time applications.

MPEG-4 introduces the concept of video object to support content-based functionalities. Video object segmentation is a crucial step for object-based coding and manipulation. In this paper, a robust semi- automatic video object segmentation scheme is proposed. To efficiently and accurately define the initial object contour, modified intelligent scissors is proposed on the basis of original intelligent scissors. It can improve about 6–8 times the processing speed with only slight sacrifice of accuracy, which just meets the requirements of initial object extraction for semi-automatic approach. To avoid errors accumulating and propagating during object tracking, an adaptive frame skipping scheme is proposed to decompose video sequence into video clips. For rigid and non-rigid video objects, two different image segmentation algorithms are utilized, and then region-based backward projection technique is adopted to interpolate the video object plane (VOPs) of other frames within every video clip. The proposed approach can cope with occlusion/disocclusion problem to most extent. Experimental results demonstrate the effectiveness and robustness of the method.

Computer-assisted vision plays an important role in our society, in various fields such as personal and goods safety, industrial production, telecommunications, robotics, etc. However, technical developments are still rare and slowed down by various factors linked to sensor cost, lack of system flexibility, difficulty of rapidly developing complex and robust applications, and lack of interaction among these systems themselves, or with their environment. This paper describes our proposal for a smart camera with real-time video processing capabilities. A CMOS sensor, processor and, reconfigurable unit associated in the same chip will allow scalability, flexibility, and high performance.

The 3-D visual tracking of human limbs is fundamental to a wide array of computer vision applications including gesture recognition, interactive entertainment, biomechanical analysis, vehicle driver monitoring, and electronic surveillance. The problem of limb tracking is complicated by issues of occlusion, depth ambiguities, rotational ambiguities, and high levels of noise caused by loose fitting clothing. We attempt to solve the 3-D limb tracking problem using only monocular imagery (a single 2-D video source) in largely unconstrained environments. The approach presented is a movement towards full real-time operating capabilities. The described system presents a complete visual tracking system which incorporates target detection, target model acquisition/initialization, and target tracking components into a single, cohesive, probabilistic framework. The presence of a target is detected, using visual cues alone, by recognition of an individual performing a simple pre-defined initialization cue. The physical dimensions of the limb are then learned probabilistically until a statistically stable model estimate has been found. The appearance of the limb is learned in a joint spatial-chromatic domain which incorporates normalized color data with spatial constraints in order to model complex target appearances. The target tracking is performed within a Monte Carlo particle filtering framework which is capable of maintaining multiple state-space hypotheses and propagating ambiguity until less ambiguous data is observed. Multiple image cues are combined within this framework in a principled Bayesian manner. The target detection and model acquisition components are able to perform at near real-time frame rates and are shown to accurately recognize the presence of a target and initialize a target model specific to that user. The target tracking component has demonstrated exceptional resilience to occlusion and temporary target disappearance and contains a natural mechanism for the trade-off between accuracy and speed. At this point, the target tracking component performs at sub real-time frame rates, although several methods to increase the effective operating speed are proposed.

In the textile industry, scoured wool contains different types of foreign materials (contaminants) that need to be separated out before it goes into further processing, so that the textile machines are protected from damage and the quality of the final woollen products is ensured. This paper presents an automated visual inspection (AVI) system for detecting and sorting contaminants from wool in real time. The techniques were first developed in the lab and subsequently applied to a large-scale factory system. The combinative use of image processing algorithms in RGB and HSV colour spaces can segment 96% of contaminant types (minimum size around 4 cm long and 5 mm in diameter) in real-time on the lab test rig. One of the most important aspects of the system is to use the non-linear colour space transformation and merge the threshold algorithm in HSV colour space into the image processing algorithms in RGB colour space to enhance the contaminant identification in real time. The real-time capability of the system is also analysed in detail. The experimental results demonstrate that the factory AVI system could identify and remove the contaminants at a camera speed of around 800 lines/s and the conveyor speed of 20 m/min in real time.

Recently, Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95] presented an efficient adaptive vector quantization (AVQ) algorithm and their proposed AVQ algorithm has a better peak signal-to-noise ratio (PSNR) than that of the previous benchmark AVQ algorithm. This paper presents an improved AVQ algorithm based on the proposed hybrid codebook data structure which consists of three codebooks—the locality codebook, the static codebook, and the history codebook. Due to easy maintenance advantage, the proposed AVQ algorithm leads to a considerable computation-saving effect while preserving the similar PSNR performance as in the previous AVQ algorithm by Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95]. Experimental results show that the proposed AVQ algorithm over the previous AVQ algorithm has about 75% encoding time improvement ratio while both algorithms have the similar PSNR performance.

For robust real-time transmission of scalable image and video data over packet-loss networks, a commonly used approach is FEC-based multiple description coding, which protects a scalable bitstream with a fixed number of packets of equal length. In this paper, by considering the problem of applying this approach of multicasting a source to a collection of clients with heterogeneous bandwidths, we propose a novel technique that changes packet length but fixes packet number. In one scenario where different clients access the server via separate links, we study the sensitivity of an optimal solution to the change of packet length, and propose a local search procedure which refines the already computed optimal solutions for other bandwidths. Compared to the scheme that computes an optimal solution individually for each bandwidth, this procedure can achieve comparable performance, however with quite lower time complexity, thus can be used in real-time applications. In another scenario where many clients share a bottleneck link, we present an embedded packetization framework for layered multiple description coding, in which even simple methods with low complexities can achieve good performance. We also propose a local search algorithm to optimize the weighted average performance in case of two layers, and a fast heuristic algorithm which can achieve very good performance tradeoff among all clients in case of more than two layers.

Moving object segmentation in compressed domain plays an important role in many real-time applications, e.g. video indexing, video transcoding, video surveillance, etc. Because H.264/AVC is the up-to-date video-coding standard, few literatures have been reported in the area of video analysis on H.264/AVC compressed video. Compared with the former MPEG standard, H.264/AVC employs several new coding tools and provides a different video format. As a consequence, moving object segmentation on H.264/AVC compressed video is a new task and challenging work. In this paper, a robust approach to extract moving objects on H.264/AVC compressed video is proposed. Our algorithm employs a block-based Markov Random Field (MRF) model to segment moving objects from the sparse motion vector field obtained directly from the bitstream. In the proposed method, object tracking is integrated in the uniform MRF model and exploits the object temporal consistency simultaneously. Experiments show that our approach provides the remarkable performance and can extract moving objects efficiently and robustly. The prominent applications of the proposed algorithm are object-based transcoding, fast moving object detection, video analysis on compressed video, etc.