Color Image Sequences Research Articles

Dicom Color Medical Image Compression using 3D-SPIHT for Pacs Application

The proposed algorithm presents an application of 3D-SPIHT algorithm to color volumetric dicom medical images using 3D wavelet decomposition and a 3D spatial dependence tree. The wavelet decomposition is accomplished with biorthogonal 9/7 filters. 3D-SPIHT is the modern-day benchmark for three dimensional image compressions. The three-dimensional coding is based on the observation that the sequences of images are contiguous in the temporal axis and there is no motion between slices. Therefore, the 3D discrete wavelet transform can fully exploit the inter-slices correlations. The set partitioning techniques involve a progressive coding of the wavelet coefficients. The 3D-SPIHT is implemented and the Rate-distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances are presented for volumetric medical datasets by using biorthogonal 9/7. The results are compared with the previous results of JPEG 2000 standards. Results show that 3D-SPIHT method exploits the color space relationships as well as maintaining the full embeddedness required by color image sequences compression and gives better performance in terms of the PSNR and compression ratio than the JPEG 2000. The results suggest an effective practical implementation for PACS applications.

Joint source and channel coding for 3D video with depth image - based rendering

Following recent commercial availability of autostereoscopic 3D displays that allow 3D visual data to be viewed without the use of special headgear or glasses, it is anticipated that the applications of 3D video will increase rapidly in near future. In this paper we propose a joint source channel coding scheme for depth image-based rendering based 3D video coding. We considered different source and channel coding rates to find the optimum coding performance under a given channel bit rate for a WiMAX based communication channel. When the optimum bit allocation combination for color and depth image sequences are found, different protection levels have been considered for coding both image sequences. Finally, an optimum protection levels are proposed for the best video quality.

ENCODING VIDEO SEQUENCES IN FRACTAL-BASED COMPRESSION

With the rapid increase in the use of computers and the Internet, the demand for higher transmission and better storage is increasing as well. This paper describes the different techniques for data (image-video) compression in general and, in particular, the new compression technique called fractal image compression. Fractal image compression is based on self-similarity, where one part of an image is similar to the other part of the same image. Low bit rate color image sequence coding is very important for video transmission and storage applications. The most significant aspect of this work is the development of color images using fractal-based color image compression, since little work has been done previously in this area. The results obtained show that the fractal-based compression works for the color images works as well as for the gray-scale images. Nevertheless, the encoding of the color images takes more time than the gray-scale images. Color images are usually compressed in a luminance-chrominance coordinate space, with the compression performed independently for each coordinate by applying the monochrome image processing techniques. For image sequence compression, the design of an accurate and efficient algorithm for computing motion to exploit the temporal redundancy has been one of the most active research areas in computer vision and image compression. Pixel-based motion estimation algorithms address pixel correspondence directly by identifying a set of local features and computing a match between these features across the frames. These direct techniques share the common pitfall of high computation complexity resulting from the dense vector fields produced. For block matching motion estimation algorithms, the quad-tree data structure is frequently used in image coding to recursively decompose an image plane into four non-overlapping rectangular blocks.

Towards Rate-Distortion Tradeoff in Real-Time Color Video Coding

In this paper, we address the key problem in real-time video coding, the rate-distortion (R-D) tradeoff. As most video coding applications employ color images, we analyze the R-D modeling problem of the color image sequence. We investigate that the R-D characteristics of color video signal in traditional transform-based video coding systems should be modeled for the luminance and chrominance components separately. So we propose separable R-D models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach to avoid buffer overflow and underflow. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel R-D tradeoff controller is designed. In the proposed R-D tradeoff framework, both the quality variation and buffer safety are considered. Extensive simulation results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow

Fusion of color and infrared video for moving human detection

We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.

An Omnidirectional Stereo Vision-Based Smart Wheelchair

To support safe self-movement of the disabled and the aged, we developed an electric wheelchair that realizes the functions of detecting both the potential hazards in a moving environment and the postures and gestures of a user by equipping an electric wheelchair with the stereo omnidirectional system (SOS), which is capable of acquiring omnidirectional color image sequences and range data simultaneously in real time. The first half of this paper introduces the SOS and the basic technology behind it. To use the multicamera system SOS on an electric wheelchair, we developed an image synthesizing method of high speed and high quality and the method of recovering SOS attitude changes by using attitude sensors is also introduced. This method allows the SOS to be used without being affected by the mounting attitude of the SOS. The second half of this paper introduces the prototype electric wheelchair actually manufactured and experiments conducted using the prototype. The usability of the electric wheelchair is also discussed.

Mining motion patterns using color motion map clustering

Automatically extracting previously unknown behavior patterns from videos that track animals with various physical conditions can accelerate our understanding of animal behaviors and their influential factors, resulting in major medical and economic benefits. Unfortunately, extracting behavior patterns from videos recordings remains as a very challenging task due to their extensive duration and the unstructured natures. This task is further complicated in a completely darken animal cage with inconsistent infrared lighting, moving reflections, or other cage debris such as the cage bedding. In this research, we propose a new motion model that enables us to measure the similarities among different animal movements in high precision so a clustering method can correctly separate recurring movements from infrequent random movements. More specifically, our model first transforms the spatial and temporal features of animal movements into a sequence of color images, referred to as color motion maps (CMMs). The task of mining recurring behavior patterns is then reduced to clustering similar color images in a database. We will use a real infrared video to demonstrate the capability of our model in capturing distinguished but brief animal movements that are embedded within a sequence of other animal movements.

Cooperative Multitarget Tracking With Efficient Split and Merge Handling

For applications such as behavior recognition it is important to maintain the identity of multiple targets, while tracking them in the presence of splits and merges, or occlusion of the targets by background obstacles. Here we propose an algorithm to handle multiple splits and merges of objects based on dynamic programming and a new geometric shape matching measure. We then cooperatively combine Kalman filter-based motion and shape tracking with the efficient and novel geometric shape matching algorithm. The system is fully automatic and requires no manual input of any kind for initialization of tracking. The target track initialization problem is formulated as computation of shortest paths in a directed and attributed graph using Dijkstra's shortest path algorithm. This scheme correctly initializes multiple target tracks for tracking even in the presence of clutter and segmentation errors which may occur in detecting a target. We present results on a large number of real world image sequences, where upto 17 objects have been tracked simultaneously in real-time, despite clutter, splits, and merges in measurements of objects. The complete tracking system including segmentation of moving objects works at 25 Hz on 352times288 pixel color image sequences on a 2.8-GHz Pentium-4 workstation

Detection of Moving Cast Shadows for Traffic Monitoring System

Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

Simultaneous tracking of multiple body parts of interacting persons

This paper presents a framework to simultaneously segment and track multiple body parts of interacting humans in the presence of mutual occlusion and shadow. The framework uses multiple free-form blobs and a coarse model of the human body. The color image sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train and classify individual pixel based on color. Relaxation labeling in an attribute relational graph (ARG) is used at the blob level to merge the pixels into coherent blobs and to represent inter-blob relations. A twofold tracking scheme is used that consists of blob-to-blob matching in consecutive frames and blob-to-body-part association within a frame. The tracking scheme resembles multi-target, multi-association tracking (MMT). A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures. The result is ‘ARG–MMT’: ‘attribute relational graph based multi-target, multi-association tracker.’ The tracking results are demonstrated for various sequences including ‘punching,’ ‘hand-shaking,’ ‘pushing,’ and ‘hugging’ interactions between two people. This ARG–MMT system may be used as a segmentation and tracking unit for a recognition system for human interactions.

Vectorial Computation of the Optical Flow in Color Image Sequences

Vectorial Computation of the Optical Flow in Color Image Sequences

Silhouette and stereo fusion for 3D object modeling

In this paper, we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multi-stereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multi-grid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the contour generators of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.

Bi-directional segmentation from the color image sequence for in-vivo visualization

Bi-directional segmentation from the color image sequence for in-vivo visualization

Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients?

We consider the problem of segmenting an image through the minimization of an energy criterion involving region and boundary functionals. We show that one can go from one class to the otherby solving Poisson's orHelmholtz's equation with well-chosen boundar y conditions. Using this equivalence, we study the case of a large class of region functionals by standard methods of the calculus of variations and derive the corresponding Euler-Lagrange equations. We revisit this problem using the notion of a shape derivative and show that the same equations can be elegantly derived without going through the unnatural step of converting the region integrals into boundary integrals. We also define a larger class of region functionals based on the estimation and comparison to a prototype of the probability density distribution of image features and show how the shape derivative tool allows us to easily compute the corresponding Gateaux derivatives and Euler-Lagrange equations. Finally we apply this new functional to the problem of regions segmentation in sequences of color images. We briefly describe our numerical scheme and show some experimental results.

Human Face Detection and Tracking using Skin Color Modeling and Connected Component Operators

Face Recognition (FR) systems are increasingly gaining more importance. Face detection and tracking in a complex scene forms the first step in building a practical FR system. In this paper, a method to detect and track human faces in color image sequences is described. Skin color classification and morphological segmentation is used to detect face(s) in the first frame. These detected faces are tracked over subsequent frames by using the position of the faces in the first frame as the marker and detecting for skin in the localized region. Specific advantages of this approach are that skin color analysis method is simple and powerful, and the system can be used to detect/track multiple faces.

Fast method for face location and tracking by distributed behaviour-based agents

The authors present a novel approach for fast human face location and tracking in colour image sequences. First, a number of colour-sensitive behaviour-based agents are uniformly distributed in the 2-D image environment to search the skin-like pixels and mark the face-like regions by activating their evolutionary behaviour. Then the shape information of each face candidate region is examined and the face regions are finally determined by fuzzy shape feature analysis. Experimental results show that the tracking speed is about 30 frames per second for a 160 /spl times/ 120 video size and 12 fps for a 320 /spl times/ 240 video size.

Extraction of Text Region From Color Image Sequence of A Book Cover Containing Gilt Characters

Extraction of Text Region From Color Image Sequence of A Book Cover Containing Gilt Characters

Image sequence segmentation using 3-D structure tensor and curve evolution

We describe a novel approach for image sequence segmentation. It contains three parts: global motion compensation, robust frame differencing, and curve evolution. It is computationally efficient, does not require dense-field motion estimation, and is insensitive to noise and global/background motion. It works for black-and-white and color image sequences. The efficacy of this approach is demonstrated on both TV and surveillance image sequences.

Resolution enhancement of monochrome and color video using motion compensation

We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).

Joint audio-video object localization and tracking

There has been a tremendous amount of research on object localization either involving microphone arrays or video cameras. Considerable less attention has been paid, however, to object localization and tracking based on joint audio-video processing thus far. This may be related to the lack of suitable algorithms for object localization simultaneously using multimicrophone outputs and color image sequences. In this article, we propose a solution to this problem. Before elaborating on joint audio-video processing, we review some previous work the areas of audio and video object localization. Then a recursive sensor fusion method based on decentralized Kalman filtering is introduced. Unfortunately, the decentralized Kalman filter cannot be directly used for joint audio-video object localization due to specific properties of the audio sensor. By properly adjusting the local audio position estimator, however, we manage to keep the overall architecture. We stress the general methodology.