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H.264/AVC for DMB is the most advanced video compression standard which provides a high compression rate and a video quality by adopting new technologies. However, these new technologies prevent us from applying some conventional algorithms directly to the H.264/AVC compression domain. For example, we need to study new image resizing schemes in the compressed bit-stream. So, in this paper, we propose a new thumbnail extraction method which can be applied to H.264. The proposed method shows that it can extract a 1/16 size thumbnail video, saving the operation time up to 50% to 70%.

Kinect v2, a new version of Kinect sensor, provides RGB, IR (Infrared) and depth images like its predecessor Kinect v1. However, the depth measurement mechanism and the image resolutions of the Kinect v2 are different from those of Kinect v1, which requires a new transformation matrix for the camera calibration of Kinect v2. In this paper, we correct the radial distortion of the RGB camera and find the transformation matrix for the correspondence between the RGB and depth image of the Kinect v2. Experimental results show that our method yields accurate correspondence between the RGB and depth images.

Different similarity measures between the descriptors of the key-points certainly yield different performances in image matching. In this paper we introduce an effective similarity measurement, which considers the distances of each key-point in a query image and its matched key-point with the smallest distance in the test image. Therefore, the distances of all key-points in the query image to the corresponding matched key-points in the test image contribute to the final similarity measurement. On the other hand, the previous method considers only the distances less than a threshold value of all possible key-point pairs, which may ignore a significant part of the key-points in the query image. Our experiments show that the proposed measure yields better performance for image similarity matching and retrieval.

In this paper, a new quality metric for depth images is proposed. Unlike the conventional depth metrics which require the additional information such as the ground truth depth image or a stereo image pair, the proposed quality metric demands only a single camera image and its corresponding depth image. In this work, we first empirically observe that the depth distortion is closely related to the local image characteristics. Based on the observation, we introduce a method to assess the local depth distortion for the edge and non-edge regions. Then, the local distortion is adaptively weighted by the Gabor filter and added up to the quality metric for the depth image.

Image retrieval based on color histograms requires quantization of a color space. Uniform scalar quantization of each color channel is a popular method for the reduction of histogram dimensionality. With this method, however, no spatial information among pixels is considered in constructing the histograms. Vector quantization (VQ) provides a simple and effective means for exploiting spatial information by clustering groups of pixels. We propose the use of Gauss mixture vector quantization (GMVQ) as a quantization method for color histogram generation. GMVQ is known to be robust for quantizer mismatch, which motivates its use in making color histograms for both the query image and the images in the database. Results show that the histograms made by GMVQ with a penalized log-likelihood (LL) distortion yield better retrieval performance for color images than the conventional methods of uniform quantization and VQ with squared error distortion.

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

Edge histogram descriptor (EHD) consists of histogram bins of local edges in an image. Instead of the conventional bin-to-bin matching for the similarity measure, this paper presents a binary string descriptor for the EHD to achieve a faster binary bit-to-bit matching.

representative frame in GoF (Group of frames) of a video is formed by taking spatial and temporal gradients sequentially for image frames and by selecting the pixel of the largest spatial-temporal gradient (STG) for all co-located pixels in the GoF. As a result, the boundary of the moving object in the video is highlighted by the STG operation. Therefore, an optimal bounding box for a moving object can be determined by choosing the maximum spatial density of the STG for various sizes of the bounding box. The bounding box includes the boundary trace of the MOOI (moving object of interest) in the GoF and is used to differentiate the MOOI from the non-MOOI. That is, the pixels outside the bounding box are the non-MOOI and they are the main target for the size reduction of the video frames for a pre-processing of video compression.

Moving objects of interest (MOOIs) in surveillance videos are detected and encapsulated by bounding boxes. Since moving objects are defined by temporal activities through the consecutive video frames, it is necessary to examine a group of frames (GoF) to detect the moving objects. To do that, the traces of moving objects in the GoF are quantified by forming a spatiotemporal gradient map (STGM) through the GoF. Each pixel value in the STGM corresponds to the maximum temporal gradient of the spatial gradients at the same pixel location for all frames in the GoF. Therefore, the STGM highlights boundaries of the MOOI in the GoF and the optimal bounding box encapsulating the MOOI can be determined as the local areas with the peak average STGM energy. Once an MOOI and its bounding box are identified, the inside and outside of it can be treated differently for object-aware size reduction. Our optimal encapsulation method for the MOOI in the surveillance videos makes it possible to recognize the moving objects even after the low bitrate video compressions.

Remote vehicle operator must quickly decide on the motion and path. Thus, rapid and intuitive feedback of the real environment is vital for effective control. This paper presents a real-time traversable ground surface segmentation and intuitive representation system for remote operation of mobile robot. Firstly, a terrain model using voxel-based flag map is proposed for incrementally registering large-scale point clouds in real time. Subsequently, a ground segmentation method with Gibbs-Markov random field (Gibbs-MRF) model is applied to detect ground data in the reconstructed terrain. Finally, we generate a texture mesh for ground surface representation by mapping the triangles in the terrain mesh onto the captured video images. To speed up the computation, we program a graphics processing unit (GPU) to implement the proposed system for large-scale datasets in parallel. Our proposed methods were tested in an outdoor environment. The results show that ground data is segmented effectively and the ground surface is represented intuitively.

A texture description is useful in many applications including similarity based image search and browsing. We present here three texture descriptors that are being considered for the final committee draft of the ISO/MPEG-7 standard. A comprehensive overview of the syntax and semantics of these texture descriptors is provided. The Homogeneous Texture Descriptor (HTD) and the Edge Histogram Descriptor (EHD) are useful in similarity search. The HTD characterizes homogeneous texture regions and is also useful in texture classification and recognition. The EHD is applicable when the underlying texture is not homogeneous and can also be used in sketch based retrieval. In addition, a compact descriptor that facilitates browsing applications is also defined. These descriptors are selected after a highly competitive test and evaluation phase within the MPEG group and we briefly summarize the evaluation criteria, the datasets used and the experimental results.

An image fidelity assessment using the edge histogram descriptor (EHD) of MPEG-7 is presented. Neither additional data nor fragile watermarking is needed, and there is no need to access the original image as a reference. Only the EHDs of the original image and the received image are required. The peak signal-to-noise ratio (PSNR) obtained by comparing the EHD extracted from the received image and that of the original image is used to assess the noise level of the received image. Experimental results show that the PSNRs calculated from the conventional pixel-to-pixel gray level and from the proposed bin-to-bin EHD maintain a proportional relationship. This implies that the EHD can be used instead of image data for the image fidelity assessments.

Query-by-example (QBE) is a well-known method for image retrieval. In reality, however, an example image to be used for the query is rarely available. Therefore, it is often necessary to find a good example image to be used for the query before applying the QBE method. Query-by-layout (QBL) is our proposal for that purpose. In particular, we make use of the visual descriptors such as the edge histogram descriptor (EHD) and the color layout descriptor (CLD) in MPEG-7. Since image features of the CLD and the EHD can be localized in terms of a 4 4 sub-image, we can specify image features such as color and edge distribution on each sub-image separately for image retrieval without a query image. Experimental results show that the proposed query method can be used to retrieve a good image as a starting point for further QBE-based image retrieval.

Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

Terrain classification allows a mobile robot to create an annotated map of its local environment from the three-dimensional (3D) and two-dimensional (2D) datasets collected by its array of sensors, including a GPS receiver, gyroscope, video camera, and range sensor. However, parts of objects that are outside the measurement range of the range sensor will not be detected. To overcome this problem, this paper describes an edge estimation method for complete scene recovery and complete terrain reconstruction. Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds. Further, a masking method is proposed to classify buildings and trees in a terrain mesh.

Depth maps taken from depth cameras are often available at a lower resolution compared to the corresponding color images. In this paper, we propose a selective interpolation method to expand depth image to the size of the corresponding color image. To achieve our goal we selectively adopt either the bilinear smoothing filter or an edge-preserving filter to expand the low resolution depth map. The edge-preserving filters we considered in this paper are joint bilateral up-sampling (JBU) filter and the New Edge-Directed Interpolation (NEDI) filter. Our method not only maintains the visual quality of the interpolated depth image at edge regions, but it also reduces the computational complexity for real time execution.

This paper extends our previous framework for digital photo annotation by adding noble approach of indoor/mixed/outdoor image classification. We propose the best feature vectors for a support vector machine based indoor/mixed/ outdoor image classification. While previous research classifies photographs into indoor and outdoor, this study extends into three types, including indoor, mixed, and outdoor classes. This three-class method improves the performance of outdoor classification. This classification scheme showed 5--10% higher performance than previous research. This method is one of the components for digital image annotation. A digital camera or an annotation server connected to a ubiquitous computing network can automatically annotate captured photos using the proposed method.

In this paper, two considerations were presented for designing pico-projector based-on LCoS panel and LED. To have a clear picture, LED duty cycle has to be less than 50% of whole possible LED illumination times. And, a constant current circuit is needed to maintain a white balance of picture.

Stereoscopic 3D video is decimated by frame-compatible top-bottom packing structure. At the receiver the discarded horizontal lines are reconstructed by interpolation functions of parallax-compensated data as well as remained neighboring lines.

Differentiating region-of-interest (ROI) from non-ROI in an image in terms of relative size as well as fidelity becomes an important functionality for future visual communication environment with a variety of display devices. In this paper, we propose a scalable image representation with the ROI functionality in the spatial domain, which allows us to generate a hierarchy of images with arbitrary sizes. The ROI functionality of our scalable representation is a result of a nonuniform grid transformation in the spatial domain, where only the center of ROI and an expansion parameter are to be known. Our grid transformation guarantees no loss of information within the area of ROI.

This paper proposes a general unsupervised segmentation algorithm which estimates all model parameters, including the number of regions, as part of the segmentation. The general image model is a hierarchical one, consisting of Markov random fields as components of the model. The MAP criterion is adopted, in principle, for the simultaneous image segmentation and parameter estimation procedures. Due to the difficulty of implementing the MAP segmentation with a large number of unknown parameters, a novel modification of the MAP criterion is proposed. For the model parameters having closed-form ML estimates, these estimates are substituted back into the objective function to reduce the difficulty of the maximization. The remaining maximization is implemented by a recursive segmentation-parameter estimation algorithm, which yields a partial optimal solution (POS) to the maximization problem. In the special case where all model parameters have closed-form ML estimates, the proposed algorithm is equivalent to implementing the MAP criterion. The number of regions in the image is determined through a model fitting criterion tagged on to the segmentation algorithm. Special forms of the general unsupervised segmentation algorithm are developed for the segmentation of noisy and textured images. For noisy images, the image is assumed to consist of uniform graylevel regions modeled by a class of Gibbs random fields and corrupted by additive, white, region-dependent, Gaussian noise. For textured images, the image is assumed to consist of regions, modeled by a class of Gibbs random fields, which are filled with textures, modeled by Gaussian Markov random fields. The algorithms for both classes of images are applied to a wide range of images—generated according to the model, hand-drawn, natural and Brodatz textures, their combinations, and outdoor images—with notable success. Despite the large number of unknown parameters (as many as 14 for some noisy images and 36 for some textured images), the algorithms yield good segmentations, accurate estimates for the parameters, and the correct number of regions.

The authors propose two decoding strategies for Reed-Solomon product codes, and analyze their performance on a random error channel. The performance measures of interest are the probability of undetected error at the inner decoder output and that of decoding failure at the outer decoder output. It has been shown that the proposed decoding strategies give a significant performance improvement over the conventional decoder. >

To reduce the semantic gap, image retrieval systems based on users' relevance feedback have been adopted. However, since this structure needs human intervention during the retrieval process, it cannot be applied to fully automated systems. To avoid this problem, we propose a feed-forward framework instead of the feed-back retrieval system, which adds a classifier to the traditional system for giving feed-forward information to maximize the average precision. That is, given a database, our proposed system improves the overall precision by selecting the best mode based on known statistics (average precision vs. recall for each category). Lloyd-clustered Gauss mixtures are used in the classifier to provide the feed-forward category information and in the quantization of color images for histogram generation.

Object segmentation and extraction play an important role in computer vision and recognition problems. Unfortunately, with current computing technologies, fully automatic object segmentation is not possible, but human intervention is needed for outlining the rough boundary of the object to be segmented. The goal of this paper is to make the object extraction automatic after the first semi-automatic segmentation. That is, once the semantically meaningful object such as a house or a human body is extracted from the image under human's guidance, an image manipulation technique is applied. There is no noticeable difference between the original and the manipulated images. However, the embedded signature by the image manipulation can be detected automatically to be used to differentiate the object from the background. The manipulated images, which is called automatic-object-extractible images, can be used to provide training images with the same object but various background images.

An important issue in embedding watermark bits in compressed video stream is to keep the bit-rate unchanged after the watermarking. This is a very difficult problem for high efficient compression methods such as H.264/AVC, because just one bit alteration in highly compressed bit-stream may widely affect the video content. In this paper we solve this problem by embedding watermark bit to the sign bit of the Trailing Ones in Context Adaptive Variable Length Coding (CAVLC) of H.264/AVC. The algorithm yields no bit-rate change after the data hiding. Also, we can easily balance between the capacity of the watermark bits and the fidelity of the video. The simplicity of the proposed algorithm is an added bonus for the real-time applications. Our experiments show that the PSNRs of the video sequences after the data hiding are higher than 43dB.

A novel image quality assessment using the edge histogram descriptor (EHD) of MPEG-7 is presented. Neither additional data nor fragile watermarking is needed for the quality assessment and the image content authentication. Also, the original image is not needed for our method, no need to access the original image as a reference. Only the EHD metadata of the original image and the received (noisy or altered) one are required. The PSNR (Peak to Signal-to-Noise Ratio) or the mean-square error (MSE) is obtained by comparing the EHD extracted from the received image and that of the original image attached as the meta-data. Then, it is used to assess the level of the image degradation and any illicit modification of the image. Experimental results show that the PSNRs calculated from the two EHDs are similar to those calculated from the pixel-to-pixel comparisons of original and received images. This implies that one can use the EHD, instead of the image data, to calculate the PSNR for the image assessment. Also, since the EHD extracted from the received image is prone to be changed according to the alterations of the image content, one can also use the proposed method as the image authentication purpose.

In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

The main difficulty in segmenting a cell image occurs when there are red blood cells touching the leukocyte. Similar brightness of the touched red blood cells with the leukocytes make the separation of the cytoplasm from the red blood cells quite difficult. Conventional approaches were based on the search of the concavities created by contact of two round boundaries as two points to be connected for the separation. Here, we exploit the fact that the boundary of the leukocytes normally has a round shape and a small portion of it is disconnected due to the touching red blood cells. Specifically, at an initial central point of the nucleus in the leukocyte, we can generate the largest possible circle that covers a circular portion of the composite of nucleus and cytoplasm areas. Then, by perturbing the initial central points and selecting only those central points that do not cross the boundary, we can cover most of interior regions in the nucleus and the cytoplasm, separating the leukocyte from the touching red blood cells.

Regions of interest in photos are important clues for the content-based image retrieval. However, segmenting semantically meaningful objects in the photo automatically for the query and similarity matching is known to be an unsolved problem. As an alternative, in this paper, we propose a scheme to form a query region in the image space in terms of 4× 4 sub-images. The set of query sub-images, which include the region of interest in the image space, is used for the basic unit for the similarity matching. Specifically, the edge histogram descriptor and the color layout descriptor in MPEG-7 are used to extract image features in the chosen sub-images and are compared to those extracted from the test images in the database. Experimental results show that the proposed method can retrieve images with similar regions in the images, even if the background regions look quite different from each other.

This paper presents a novel scene change detection method in H.264/AVC compression domain. We note that, comparing to previous MPEG compression methods, some unique compression schemes such as intra prediction, CAVLC etc. are adopted for H.264/AVC. Therefore, previous scene change detection methods for MPEG-1 or MPEG-2 are not directly applicable to the compressed bit stream of H.264/AVC. To solve this problem, we propose a novel scene change detection algorithm for H.264/AVC, which exploits intra prediction modes of macro block. Since our method requires a simple decoding of the prediction mode, it is quite fast. Also, the precision and recall for various video sequences and bit-rates turn out to be promising.

Spatial domain watermarking techniques do not fully consider the frequency characteristics of an image, so they are vulnerable to frequency domain attacks like JPEG compression. On the other hand, DCT domain watermarking techniques are subject to geometric attacks such as rotation and resizing. To overcome these problems we propose a dual watermark detection scheme, detecting watermarks both in spatial and in DCT domains. Our method embeds a watermark in the DCT domain. For the spatial domain detection, we use a synchronization technique based on block-based template matching to reverse the disordered alignment caused by t,wo important geometric attacks! rotation and resizing. An equivalent pattern of the watermark is extracted in the spatial domain. The extracted watermark is then correlated for detection with the watermark obtained by taking inverse DCT to the original watermark. Reszllts show that the proposed dual detection is robust to both geometric attacks and other signal processing attacks.

A general framework for image interpolation in a uniformly spaced image grid is presented. The proposed formulation is suitable for representing fractional (or sub-pixel) pixels as well as integer pixel interpolations. Also, by fitting an interpolation kernel to the grid formulation, finite impulse response filter coefficients can be readily determined for a given sampling interval and filter length. As an example, a four-tap lowpass filter is derived by fitting the Lagrange interpolation kernel to a row (or column) expansion by 2 on integer pixel locations.

The multiscale Markov models introduced in Chapter 4 can be used to extract large scale image features by parameterizing inter scale and intra scale interactions in an image. Since there are multiple scales with different parameter values for different scales, the parameter estimation process in the multi-scale approach becomes a major computational burden. One way to alleviate this complexity is to adopt a block-wise image modeling paradigm. That is, dividing the image space into non-overlapping blocks, a random variable is assigned for each image block to represent the class label. Then, a representative feature for each image block is extracted and is treated as the observed image data. The collection of all block features constitute the realization of the random field Y. Also, the class label assigned for each image block is a realization of the unobservable random field X. Since the representative feature values for image blocks normally have spatial continuity, the random field for the block class labels can be modeled as an MRF.

A neighborhood system provides a convenient means for representing non-causal dependence of a pixel in a 2-D image lattice. A neighborhood system η is a set of neighborhoods η s ⊂ Ω which satisfies the following conditions.

The pixel-wise Markov image models introduced in the previous chapters have a limited ability in dealing with texture images. In order to describe large scale behavior in texture images, it is necessary to increase the size of the neighborhood. However, this dramatically increases the number of parameters and makes the computation for the parameter estimation quite difficult. Considering that the scales of the texture elements can be diverse, one possible way of dealing with various scales of texture elements is to express image data hierarchically using a multiscale transform. Laplacian pyramids [27] and wavelet multiresolution representations [124] are techniques that can decompose an image into multiple scales. Successive filtering and decimation operations in multiscale transforms provide a pyramid structure for image data. As a result, we have a sequence of image data for various scales and frequency bands. Specifically, coarse image components with smaller image resolution are located at the higher level and fine image components capturing detail can be found at the lower levels of the image pyramid. This multiscale image structure can provide useful information for the analysis and representation of the image data, especially for texture images. Processing successively from coarse to fine levels of the pyramid, image features at different scales and frequency bands are captured and class labels are assigned accordingly to large regions with low frequency and then refined to small image blocks and eventually to pixels with higher frequency information. This multiscale processing also allows us to save computations. That is, coarsening the image data for a multiscale image representation may smooth out local minima and result in faster convergence with a reduced sensitivity to an initial class label. Then, the optimal class labels at the current level of the pyramid to the next finer level can be obtained by searching in the vicinity of the class labels obtained at the previous coarser level. Since there exist strong inter scale and intra scale dependencies among vertically and horizontally connected neighboring data in the image pyramid, the multiscale images can be described more efficiently via Markov modeling. Adopting Markov models for the multiscale image data, the following issues arise: (i) how to describe various inter scale and intra scale interactions in terms of the Markov models, (ii) how to formulate the optimization problem for the class labels in the image pyramid, (iii) how to obtain the optimal class labels defined in (ii). These issues are treated in this chapter.

Traditionally two groups have developed extensions of 1-D Markov processes to 2-D image data. People in the first group adopt most of their ideas and tools from statistical mechanics and express the Markov nature of a random field in a noncausal way. The MRFs described in Chapter 2 are such models. The primary goal of the second group is to extend 1-D hidden Markov models (HMMs) to 2-D causal MRF models. The chief obstacle to this extension is the lack of a natural ordering for a two dimensional grid and hence the lack of a natural notion of causality in the spatial image data. As a result, an artificial ordering for image data must be assumed, which sometimes yields directional artifacts in the processed images. On the other hand, an advantage of a causal MRF model approach is the possibility of reduced complexity on-line processing for 2-D image data. That is, in the case of sequential image transmission, received image data can be processed immediately without waiting the arrival of other data in the image space. A more important advantage of the causal MRF model approach is the availability of a variety of useful tools developed for 1-D Markov chain problems such as speech recognition [9, 10, 151]. For example, the recursive paradigm and its computationally useful algorithms can be employed for the solutions of various image processing problems by means of causal MRF modeling.

Automatic segmentation of digital cell images into four regions, namely nucleus, cytoplasm, red blood cell (rbc), and background, is an important step for pathological measurements. Using an adaptive thresholding of the histogram, the cell image can be roughly segmented into three regions: nucleus, a mixture of cytoplasm and rbc’s, and background. This segmentation is served as an initial segmentation for our iterative image segmentation algorithm. Specifically, MAP (maximum a posteriori) criterion formulated by the Bayesian framework with the original image data and local variance image field (LVIF) is used to update the class labels iteratively by a deterministic relaxation algorithm. Finally, we draw a line to separate the touching rbc from the cytoplasm.

Histogram-based image retrieval requires some form of quantization since the raw color images result in large dimensionality in the histogram representation. Simple uniform quantization disregards the spatial information among pixels in making histograms. Since traditional vector quantization (VQ) with squared-error distortion employs only the first moment, it neglects the relationship among vectors. We propose Gauss mixture vector quantization (GMVQ) as the quantization method for a histogram-based image retrieval to capture the spatial information in the image via the Gaussian covariance structure. Two common histogram distance measures are used to evaluate the similarity of histograms resulting from GMVQ. Our result shows that GMVQ with a quadratic discriminant analysis (QDA) distortion outperforms the two typical quantization methods in the histogram- based image retrieval.

The paper describes an automatic object segmentation algorithm for images with low depth of field (DOF). The low DOF images are segmented into two regions, namely, focused objects and defocused background. A local variance image field (LVIF) can represent the pixel-wise spatial distribution of the high-frequency components in the image. However, applying a thresholding method to the LVIF for segmentation often yields blob-like errors in both focused and defocused regions. To eliminate these errors, a block-wise MRF (Markov random field) image model is employed for maximum a posteriori (MAP) segmentation. After the block-wise MAP segmentation, the image blocks in the object boundary are divided into smaller blocks. Then, they are reassigned to one of the neighboring objects through the watershed algorithm, which eventually yields a pixel-level segmentation. Experimental results show that the proposed method yields more accurate segmentation than the multiresolution wavelet-based segmentation method.

We propose a texture classification method using multiple Gauss mixture vector quantizers (GMVQ). We designed a separate model codebook or Gauss mixture for each texture using the generalized Lloyd algorithm with a minimum discrimination information (MDI) distortion based on a training data set. The multi-codebook structure of the GMVQ classifier is an extension to images of the isolated utterance speech recognizer of J.E. Shore and D. Burton (see Proc. Int. Conf. Acoust., Speech, and Sig. Processing, IEEE82Ch.1746-7, p.907-10, 1982). We applied the algorithm to the Brodatz texture database and showed it to be competitive in performance in comparison to other texture classifiers. Its low complexity implementation and real-time operation make the approach suitable for content-based image retrieval.

This paper proposes an invertible semi-fragile video watermarking algorithm using a hash function to authenticate the video contents. It can protect the video content from malicious manipulations and detect the modified part of the video. Also the proposed algorithm has the invertible property, which allows us to get the original video data back if they are authentic.

This paper deals with the watermarking problem for irregular shaped objects in video. Extracting the meaningful objects from video, we have implemented the watermarking using the SA-DCT (shape adaptive discrete cosine transform) and conventional rectangular based watermarking using various padding methods. The experimental results show that the SA-DCT method is superior to all other padding methods in terms of the robustness against the image deformations such as lossy compression, additive noise, filtering, and clipping.

We propose to use a 2/spl times/2 DCT for the multiresolution progressive image transmission of images with the spatial scalability. Since the 2/spl times/2 DCT has simple computations and insignificant image quality degradation compared to the other convolution-based wavelet filters, it can be used for real-time progressive transmission of images through the Internet.

We describe a similarity based adult image detection technique (SID) exploiting composed MPEG-7 visual descriptors. The technique with a large set of training adult image database and a smaller set of training non-adult image database is practically useful in detecting adult images with little false negatives. SID achieved 99% correct detections with 23% false positives when experimented with a database containing 1,300 training non-adult images, and 93.5% correct detections with 8.4% false positives when experimented with a database containing 12,000 training non-adult images. 9,900 training adult images are used for both experiments. Given a query, ten most similar images are retrieved. If majority of the retrieved are adult images, then the query is determined to be an adult image. Otherwise, it is determined to be a non-adult image. SID can detect adult Internet content with the aid of text filtering system as described in the later section.

Summary form only given. Taking the average of feature vectors from the center and neighboring blocks to a block being coded is proposed as a method of considering context information in block classification. The algorithm has the advantage of low complexity. Gauss mixture models (GMM) are adopted to extract features from image blocks, including an algorithm to handle singular covariance matrices. Two different distortion measures are used; namely log-likelihood quadratic discrimination analysis (QDA) and a dimension-compensated distortion measure defined by dividing the QDA distortion by the corresponding cell's dimension. Aerial images were used to train and test. Experimental results show that the proposed algorithm not only improves the classification performance, but also provides a solution to the singular covariance problem.

In this paper, a new variable size block segmentation for image compression is proposed. The decision whether the given image block is homogeneous or not is based on the model selection criterion. More specifically, calculating the log-likelihoods for all pre-determined region segmentations with the given image data, we apply a modified AIC criterion to select a best match. If the selected pattern turns out to be a texture or an edge, we further divide the given image block to yield a variable size block segmentation. Since the proposed algorithm takes into account the contextual information as well as the block variance for the classification, it can differentiate a texture from an edge. Moreover, due to the pre-determined block segmentations, we can further differentiate vertical, horizontal, or diagonal edges.