Low resolution and un-sharp facial images are always captured from surveillance videos because of long human-camera
distance and human movements. Previous works addressed this problem by using an active camera to capture close-up
facial images without considering human movements and mechanical delays of the active camera. In this paper, we
proposed a unified framework to capture facial images in video surveillance systems by using one static and active
camera in a cooperative manner. Human faces are first located by a skin-color based real-time face detection algorithm.
A stereo camera model is also employed to approximate human face location and his/her velocity with respect to the
active camera. Given the mechanical delays of the active camera, the position of a target face with a given delay can be
estimated using a Human-Camera Synchronization Model. By controlling the active camera with corresponding amount
of pan, tilt, and zoom, a clear close-up facial image of a moving human can be captured then. We built the proposed
system in an 8.4-meter indoor corridor. Results show that the proposed stereo camera configuration can locate faces with
average error of 3%. In addition, it is capable of capturing facial images of a walking human clearly in first instance in
90% of the test cases.
The large number of rear end collisions due to driver inattention has been identified as a major automotive safety issue. Even a short advance warning can significantly reduce the number and severity of the collisions. In this paper, we describe an image alignment based vehicle tracking method that employs only a single moving camera mounted on the driver's automobile as input, for use in detecting rear vehicles on highways and city streets. We also present a method to compute the relative distances between the rear vehicles and the driver's car. With the aid of symmetrical function and simplified image alignment tracking methodology, our methodology becomes relatively simple to implement using embedded system technology in the automobile environment with real-time multiple vehicles tracking and successful rate over 97%.
This paper describes an embedded multi-user login system based on fingerprint recognition. The system, built using the Sitsang development board and embedded Linux, implements all fingerprint acquisition, preprocessing, minutia extraction, match, identification, user registration, and template encryption on the board. By careful analysis of the accuracy requirement as well as the arithmetic precision to be used, we optimized the algorithms so that the whole system can work in real-time in the embedded environment based on Intel(R) PXA255 processor. The fingerprint verification, which is the core part of the system, is fully tested on a fingerprint database consists of 1149 fingerprint images. The result shows that we can achieve an accuracy of more than 95%. Field testing of 20 registered users has further proved the reliability of our system. The core part of our system, then embedded fingerprint authentication, can also be applied in many different embedded applications concerning security problems.
This paper presents an experimental study of the implementation of a face recognition system in embedded systems. To investigate the feasibility and practicality of real time face recognition on such systems, a door access control system based on face recognition is built. Due to the limited computation power of embedded device, a semi-automatic scheme for face detection and eye location is proposed to solve these computationally hard problems. It is found that to achieve real time performance, optimization of the core face recognition module is needed. As a result, extensive profiling is done to pinpoint the execution hotspots in the system and optimization are carried out. After careful precision analysis, all slow floating point calculations are replaced with their fixed-point versions. Experimental results show that real time performance can be achieved without significant loss in recognition accuracy.
This paper presents an experimental study of the implementation of a face authentication system for mobile devices. Our system is based on a widely adopted face recognition technique called Principal Component Analysis (PCA). The execution time of the baseline system on a PDA is unacceptably slow -- a typical authentication session takes more than 30 seconds. To make real-time face authentication possible on mobile devices, optimization is needed. In our study, extensive profiling is done to pinpoint the execution hotspots in the system. Based on the profiling results, our optimization strategy focused on replacing the large amount of slow floating point calculations with their fixed-point versions. Range estimation is also carried out to determine the range of floating point values that must be accommodated by the final, fixed-point version of our system. Compared with the baseline system, experimental results indicate that our optimized system runs as much as 47 times faster for PCA projection. Using the optimized system, a complete authentication session takes only 5 seconds. Real time face authentication for mobile device is achieved with no significant loss in recognition accuracy.
The importance of digital maps increases continuously. Unfortunately, it is costly and time consuming to create a digital map out of the void. If we can successfully vectorize scanned paper maps, digital map production will be much easier and previous map resources can be well utilized. The first step of map vectorization is color clustering. Currently there exist a number of fast and efficient algorithms for automatic color clustering. However, they may not work well for maps of enormous noises. Here we introduce two interactive color clustering algorithms: color clustering with pre-calculated index colors (PCIC) and color clustering with pre-calculated color ranges (PCCR). A number of experiments are conducted to investigate into the performance of the two approaches. Finally, we introduce a novel and efficient vectorization algorithm, which can perform the entire vectorization algorithm in one pass of linear time.
The solid state fingerprint sensors are small in size and can be easily installed on mobile devices. However, the small contact area limits the number of collected minutiae, making the fingerprint matching less reliable. Recently, template synthesis of fingerprints is proposed to augment the available minutiae set during registration. However, this approach is not feasible when two fingerprints are severely distorted. In this paper, we propose a novel way of fingerprint template normalization for distortion removal. Instead of performing expensive processing of the fingerprint images, we suggest that the normalization can be applied to the extracted minutiae using the ridge structure gathered during direct gray scale.
The demand for digital maps continues to arise as mobile electronic devices become more popular nowadays. Instead of creating the entire map from void, we may convert a scanned paper map into a digital one. Color clustering is the very first step of the conversion process. Currently, most of the existing clustering algorithms are fully automatic. They are fast and efficient but may not work well in map conversion because of the numerous ambiguous issues associated with printed maps. Here we introduce two interactive approaches for color clustering on the map: color clustering with pre-calculated index colors (PCIC) and color clustering with pre-calculated color ranges (PCCR). We also introduce a memory model that could enhance and integrate different image processing techniques for fine-tuning the clustering results. Problems and examples of the algorithms are discussed in the paper.
KEYWORDS: Mobile devices, Image processing, Personal digital assistants, Signal processing, Digital signal processing, Embedded systems, Feature extraction, Java, Raster graphics, Binary data
Mobile devices use embedded processors with low computing capabilities to reduce power consumption. Since floating-point arithmetic units are power hungry, computationally intensive jobs must be accomplished with either digital signal processors or hardware co-processors. In this paper, we propose to perform fixed-point arithmetic on an integer hardware unit. We illustrate the advantages of our approach by implementing fingerprint verification on mobile devices.
This paper discusses the constructing method of a general integer wavelet transform algorithm. Coupling such an algorithm with subblock differential pulse code modulation (DPCM), an encoding scheme for near-lossless image compression is obtained for remote sensing images. This method possesses the following features: (1) real-time processing is possible; (2) hardware implementation is easy; (3) the algorithm is of parallel structure; (4) only addition, subtraction and bit-shift are involved in the processing. Experiments illustrate that our algorithm is an effective encoding scheme to compress remote sensing images.
This paper studies an approach to solve the problem of color purification for images of scanned paper maps in an experimental manner. The mathematical foundation of the approach is briefly outlined. A computationally feasible algorithm is then proposed. This algorithm is tested through real life testing. Results indicate that this approach not only restores and purifies colors of the map digitally. It compresses the data of the image files too.
A new approach for generating gray scale characters, called gray scale rasterization, is discussed in this paper for generating Chinese gray scale characters. Gray scale characters proved to be superior in quality than the traditional black-and-white characters on the CRT display which is the most important output device for all multimedia, Internet applications. Digital filtering, a commonly used technique is image processing, is often used to generate English gray scale characters which are stored in font libraries for high quality display applications. Simple estimations show that such an approach cannot be used to support the creation of Chines gray scale font libraries. A gray scale rasterizer, which generates gray scale pixel maps directly from the outline descriptions of the characters, is proposed in this proposal. Some implementation techniques are introduced in this paper: the approximation of Beizer curves by straight lines, the investigation of the filling process and the migration of the horizontal and vertical strokes. With such techniques, a gray scale rasterizer will be implemented. It is expected that gray scale Chinese display can be beneficial to the Chinese multimedia and Internet applications.
Lossless or high fidelity compression of images is a critical problem yet to be solved in a number of areas such as satellite remote sensing, medical imaging and color image printing. Now the requirement for preservation of image details has rendered the compression method that preserves important information inapplicable. Limited by the storage capacity and transmitting capability, it is very important to enhance the compression ratio of satellite remotely sensed images at high fidelity. Based on wavelet transform and image reconstruction, a feature coding based image compressing algorithm is studied and proposed. This algorithm makes use of the correlativity between the positions of extrema of wavelet transform coefficients as well as the higher-order correlativity between amplitudes of the extrema to perform compression coding, decoding and reconstruction, achieving the result of a compression ratio greater than or equal to 4 at PSNR greater than or equal to 40 db.
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