Thousands of fragments of ceramics (called sherds for short) are found at archaeological excavation sites. One of these excavations sites is Tel Dor in Israel. The excavators in Dor use hand drawings and a profilograp...
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Thousands of fragments of ceramics (called sherds for short) are found at archaeological excavation sites. One of these excavations sites is Tel Dor in Israel. The excavators in Dor use hand drawings and a profilograph for documentation of sherds. Both techniques acquire a cross-section of the sherd, the so called profile line, which is used for classification and statistical analysis about the ancient population of Dor. As proposed in previous work we are developing a fully automated system for documentation of sherds by 3D-acquisition based on structured light and extraction of the profile line. Consequently we joined the field trip to Tel Dor in July, 2004 to compare in-situ the accuracy and performance of the traditional hand drawings, the profilograph and our system. We therefore alos measured the time for each step of documentation in-situ to find bottle-necks in documented sherds per hour. Based on these results we could propose an improvement to increase the throughput of our system by a factor of 5. The results of the comparison of all three techniques of documentation of sherds, the improvement for our system and a methodological experiment for future work are shown in this report.
This paper describes a tracking with appearance modelling system for pedestrians. A cascade of boosted classifiers and Haar-like rectangular features [6, 12] are used for the pedestrian detection. Statistical modellin...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imageprocessing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
The efficiency of an image compression technique relies on the capability of finding sparse M-terms for best approximation with reduced visually significant quality loss. By "visually significant" it is mean...
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This paper gives a robust motion detection and tracking solution for a video surveillance application on an airport's apron. As an outdoor application, the system must be capable of adapting to a wide range of wea...
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This paper gives a robust motion detection and tracking solution for a video surveillance application on an airport's apron. As an outdoor application, the system must be capable of adapting to a wide range of weather conditions and illumination changes. Furthermore, the achromaticity of the scene and the presence of occlusions in the tracking process are issues considered in the selection of the motion detector and tracking system respectively. We propose an adapted mixture of Gaussians model with RGB colour normalisation to detect mobile objects in the scene and a region tracking method based on significant mobile object features to track individuals and vehicles on the selected airport's apron. The performance of the proposed motion detector is evaluated using pixel-based performance metrics and compared with other existing methods. The capability of the application to handle partial occlusions is tested on the region tracker.
Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representin...
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ISBN:
(纸本)1901725294
Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representing an individual sub-part are derived from a minimum description length criterion. Thereby the resulting clique of models is more compact and obtains a better generalization behavior than a single model. The proposed AAM clique generation deals with non-linearities in the data in a generic information theoretic manner reducing the necessity of user interaction during training.
This paper presents a visual surveillance system for the automatic scene interpretation of airport aprons. The system comprises two modules - Scene Tracking and Scene Understanding. The Scene Tracking module, comprisi...
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image segmentation is a key technology to image analysis and processing. How to segment portrait fast and robust from the background is a difficult problem. With revising histogram segmentation by threshold in blue to...
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ISBN:
(纸本)9781932415643
image segmentation is a key technology to image analysis and processing. How to segment portrait fast and robust from the background is a difficult problem. With revising histogram segmentation by threshold in blue tone space, we present an adaptable quantum evolution threshold-searching algorithm to segment portrait fast and robust. Experiments and detail comparison analysis are provided to demonstrate effects.
This paper presents a novel approach to compute DCT-I, DCT-III, and DCT-IV. By using a modular mapping and truncating, DCTs are approximated by linear sums of discrete moments computed fast only through additions. Thi...
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Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from ...
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Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from that of optical satellite imagery, whose imaging is a slanting imaging of electromagnetic microwave reflection. The different characters between radar image and optical satellite images are very distinct, such as the layover distortion of ground-truth and speckle noise, which degrades the image to such an extent that the features are very unclear and difficult to be extracted. So the factors such as the hypsography, ground truth, sensor altitude and imaging time should be taken into account for radar image and optical image matching. In this paper, we develop an image match algorithm based on reference map multi-area selection using fuzzy sets. image matching is generally a procedure that calculates the similarity measurement between sensed image and the corresponding intercepted image in reference map and it searches the maximum position in the correlation map. Our method adopts a converse matching strategy which selects multi-areas in optical reference map using fuzzy sets as model images, then match them on the sensed image respectively by normalized cross correlation matching algorithm and fuse the match results to get the optimum registered position. Multi-areas selection mainly considers two influence factors such as ground-truth texture features and the hypsography (DEM) of imaging region, which will suppress the influence of great variance imaging region. Experiment results show the method is effective in registering performance and reducing the calculation.
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