Shot boundary detection is a fundamental step of video indexing. One crucial issue of this step is the discrimination of abrupt shot change from flashlight, because flashlight often induces a false shot boundary. Support vector machine (SVM) is a supervised learning technique for data classification. In this paper, we propose a SVM-based technique to detect flashlights in video. Our approach to flashlight detection is based on the facts that the duration of flashlight is short and the video contents before and after a flashlight should be similar. Therefore, we design a sliding window in temporal domain to monitor the instantaneous video variation and extract color and edge features to compare the visual contents between two video segments. Then, a SVM is employed to classify the luminance variation into flashlight or shot cut. Experimental results indicate that the proposed approach is effective and outperforms some existing techniques.

In this paper, we propose an integrated approach for human detection in surveillance video. In our approach, the moving object is extracted by background subtraction; and the background model is updated by the first-order recurrence filter. Then, two complementary features are extracted for moving object classification. They are contour-based description: Fourier descriptor and region-based description: histogram of oriented gradient. As the binary classifier (support vector machine) is able to provide the posterior probability, we effectively integrate two types of features to achieve better performance. Experimental results show that the proposed approach is effective and outperforms some existing technique.

The usefulness of an image database depends on whether the image of interest can be easily located. Feature extraction is a crucial step of image retrieval. The well-known SIFT descriptor is a keypoint-based image feature. It can be used to robustly find the same objects in different images, i.e. achieve the object recognition task. However, it is not effective on the image retrieval task, i.e. finding images with similar content. To improve the SIFT algorithm, we propose a robust image retrieval algorithm based on the integration of keypoints and edges information. Our approach is robust to translation, rotation and partial occlusion of the object. Experimental results indicate that the proposed algorithm is effective and outperforms the SIFT algorithm.

As violence in movies has harmful influence on children, in this paper, we propose a vision-based algorithm to detect violent scenes in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed only the shot level of video structure, our approach works on a more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to be fed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective.

The usefulness of a video database depends on whether the video of interest can be easily located. In this paper, we propose a video retrieval algorithm based on the integration of several visual cues. In contrast to key-frame based representation of shot, our approach analyzes all frames within a shot to construct a compact representation of video shot. In the video matching step, by integrating the color and motion features, a similarity measure is defined to locate the occurrence of similar video clips in the database. Therefore, our approach is able to fully exploit the spatio-temporal information contained in video. Experimental results indicate that the proposed approach is effective and outperforms some existing technique.

Gradual shot change detection is one of the most important research issues in the field of video indexing/retrieval. Among the numerous types of gradual transitions, the dissolve-type gradual transition is considered the most common one, but it is also the most difficult one to detect. In most of the existing dissolve detection algorithms, the false/miss detection problem caused by motion is very serious. In this paper, we present a novel dissolve-type transition detection algorithm that can correctly distinguish dissolves from disturbance caused by motion. We carefully model a dissolve based on its nature and then use the model to filter out possible confusion caused by the effect of motion. Experimental results show that the proposed algorithm is indeed powerful.

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

Object segmentation and tracking are problems within the scope of MPEG-4 standardization activities. This paper proposes a novel algorithm to extract and track the moving objects in the video sequences. A model-based tracking method is developed to localize the object in each frame of a video sequence. One distinctive property of this method is that the model of an object is acquired dynamically from the video sequence, rather than being provided a priori. Our method also allows the object to have large movement between two frames and to be partially occluded in several successive frames. To extract contiguous object boundaries, an active contour model is then applied to the binary image representation of the object model. Compared with the related work of Neri et al. (1998), the proposed algorithm can achieve a more accurate location of object boundaries.

An audio protection and authentication scheme is proposed. By quantizing a host audio's FFT-coefficients as masking threshold units (MTUs), two complementary watermarks are designed and embedded using our cocktail watermarking method. For audio protection, high robustness can be achieved; whereas for audio authentication, tampered regions can be detected. Both of the above mentioned goals are accomplished in an oblivious manner. Experimental results indicate that our multipurpose audio watermarking scheme is remarkably effective.

This paper presents a complete procedure for the extraction and recognition of hand-printed numeral strings on maps. The extraction algorithm can extract individual characters from a map even if the characters touch each other or touch with graphical line. The feature-based recognition algorithm can recognize numeral characters of any size, position and orientation. Our features for discrimination are simple and easily detectable. Experimental results on utility and cadastral maps have shown that the proposed technique is effective in the automatic data capture of geographic information systems.

We present a map interpretation system for automatic extraction of high level information from the scanned images of Chinese land register maps. Our map interpretation system consists of three main components: text/graphics separation, parcel extraction, and rotated character recognition. Our approach to text/graphics separation is based on a simple yet effective rule: the feature points of characters are more compact than those of graphics. In the parcel extraction process, the proposed algorithm traces the branches between feature points to extract polygon structure from line drawings. Our character recognition method is based on the matching of extracted strokes using a neural network. The techniques of text/graphics separation and character recognition are robust to the rotation and writing style of characters. Another advantage of our separation algorithm is that it can successfully extract a character connected to a graphical line. Experimental results have shown that the proposed system is effective for the data capture of geographic information systems.

The paper presents a complete procedure for the extraction and recognition of handprinted numeral strings on maps. The character extraction algorithm can even segment slant and touching characters into individual components. The feature based recognition algorithm can recognize numeral characters of any size, position, and orientation. Our features for discrimination are simple and easily detectable. Experimental results on utility and cadastral maps have shown that the proposed technique is effective in the automatic data capture of geographic information systems.

This paper presents a map interpretation system for automatic extraction of high level information (such as parcels and their attributes) from the scanned images of Chinese cadastral maps. Our map interpretation system consists of three main components: text/graphics separation, parcel extraction, and rotated character recognition. The techniques of text/graphics separation and character recognition are robust to the rotation and writing style of character. Another advantage of our separation algorithm is that it can successfully extract character connected to graphical line.

In this paper, we propose a robust algorithm to separate Chinese characters from line drawings. This approach is based on the clustering of all feature points in the images. Using our algorithm, all Chinese characters can be completely separated from graphics without regard to the size, orientation and location of Chinese characters even if the characters touching or overlapping line problem occurs. Experiments show that our algorithm can be applied to both geographical information systems and forms processing.

An algorithm for extracting and segmenting surface descriptions from stereo images is presented. Compared with the advance of range image segmentation, the progress of surface segmentation from stereo images is slow. The algorithm integrates the boundary-based and region-based approaches to achieve more satisfactory segmentation results. The algorithm yields such a rich description of a scene in terms of global surface patches and closed surface boundaries. These shape primitives are useful for subsequent high-level processing.>

Addresses the problem of segmenting objects into parts using stereo images. There are three components in the part segmentation process: surface segmentation, region grouping, and volumetric models recovery/segmentation. The surface segmentation process segments the image into a set of regions such that each region represents a surface smooth in depth. The region grouping process merges the segmented regions into meaningful parts. Finally the process of volumetric model recovery/segmentation phase recovers the part model and segments that part into smaller parts if necessary. Since multi-shape models have been used to drive the part segmentation process, one can capture more geometric properties of the object and the application domain of the approach is broader than that of the previous approaches.>

The authors present two algorithms for stereo matching that make use of simultaneous matching and surface reconstruction. By integrating matching and reconstruction, which are traditionally separated temporally, the algorithms can make use of the current surface approximation to help disambiguate the remaining matches. The result is a consistent smoothness assumption in both matching and surface reconstruction. The two methods differ in the surfaces reconstructed; one uses world surfaces and the other disparity surfaces. The authors present an initial experimental analysis of each algorithm and discuss the limitations of the approach and future work.>

This paper presents a new algorithm for stereo matching. The algo- rithm combines what are generally three processes, feature matching, surface reconstruction, and segmentation of world surfaces, in a consis- tent and synergistic way. By integrating these phases, which are usually sequential, the algorithm can make use of the current surface approxi- mation to disambiguate potential matches. This results in higher data densities, a consistency of interpretation, and greater system flexibility. Examples of the algorithm are presented on real and synthetic images, including a scene with a transparent surface.