Pei-Yung Hsiao

A comparison of similarity measures of fuzzy values

Abstract This paper extends the work of Pappis and Karacapilidis (1993) to present and compare the properities of several measures of similarity of fuzzy values. The measures examined in this paper are based on the geometric model, the set-theoretic approach, and the matching function S we presented in (Chen, 1988). It is shown that several properties are common to all measures and some properties do not hold for all of them.

A Portable Vision-Based Real-Time Lane Departure Warning System: Day and Night

Lane departure warning systems (LDWS) are an important element in improving driving safety. In this paper, we propose an embedded advanced RISC machines (ARM)-based real-time LDWS. As for software development, an improved lane detection algorithm based on peak finding for feature extraction is used to successfully detect lane boundaries. Then, a spatiotemporal mechanism using the detected lane boundaries is designed to generate appropriate warning signals. As for hardware implementation, a 1-D Gaussian smoother and a global edge detector are adopted to reduce noise effects in the images. By using the developed data transfer channel (DTC) in the reconfigurable field-programmable gate array (FPGA) module, the data transfer rate among the complementary metal-oxide-semiconductor (CMOS) imager module, liquid-crystal display (LCD) display module, and central processing unit (CPU) bus is about 25 frame/s for an image size of 256 times 256. In addition, the proposed departure warning algorithm based on spatial and temporal mechanisms is successfully executed on the presented ARM-based platform. The effectiveness of our system concludes that the lane detection rate is 99.57% during the day and 98.88% at night in a highway environment. The proposed departure mechanisms effectively generate effective warning signals and avoid most false warnings.

Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area

Changing lanes while having no information about the blind spot area can be dangerous. We propose a vision-based vehicle detection system for a lane changing assistance system to monitor the potential sedan vehicle in the blind-spot area. To serve our purpose, we select adequate features, which are directly obtained from vehicle images, to detect possible vehicles in the blind-spot area. This is challenging due to the significant change in the view angle of a vehicle along with its location throughout the blind-spot area. To cope with this problem, we propose a method to combine two kinds of part-based features that are related to the characteristics of the vehicle, and we build multiple models based on different viewpoints of a vehicle. The location information of each feature is incorporated to help construct the detector and estimate the reasonable position of the presence of the vehicle. The experiments show that our system is reliable in detecting various sedan vehicles in the blind-spot area.

On-board vision system for lane recognition and front-vehicle detection to enhance driver's awareness

The objectives of this research are to develop a driving assistance system that can locate the positions of the lane boundaries and detect the existence of the front-vehicle. By providing warning mechanism, the system can protect drivers from dangerousness. In lane recognition, Gaussian filter, peak-finding procedure, and line-segment grouping procedure are used to detect land markers successfully and effectively. On the other hand, vehicle detection is achieved by using three features, such as underneath, vertical edge, and symmetry property. The proposed system is shown to work well under various conditions on the roadway. The vehicle detection rate is higher than 97%. Besides, the computation cost is inexpensive and the systems response is almost real time. Thus, the results of the present research work can improve traffic safety for on-road driving.

Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter

We propose an automatic system to detect preceding vehicles on the highway under various lighting and different weather conditions based on the computer vision technologies. To adapt to different characteristics of vehicle appearance at daytime and nighttime, four cues including underneath, vertical edge, symmetry and taillight are fused for the preceding vehicle detection. By using particle filter with four cues through the processes including initial sampling, propagation, observation and cue fusion and evaluation, particle filter accurately generates the vehicle distribution. Thus, the proposed system can successfully detect and track preceding vehicles and be robust to different lighting conditions. Unlike normal particle filter focuses on a single target distribution in a discrete state space, we detect multiple vehicles with particle filter through a high-level tracking strategy using clustering technique called basic sequential algorithmic scheme (BSAS). Finally, experimental results for several videos from different scenes are provided to demonstrate the effectiveness of our proposed system.

Multilayered Image Processing for Multiscale Harris Corner Detection in Digital Realization

The PC-based software programming used in complex or luxuriant image processing algorithms is time consuming and resource wasting. As appropriate processing for the image data indeed speedups complicated algorithms, we focus on a crucial case - multilayered processes. In this paper, we gauge deeply into the data flow of multilayered image processing to avoid waiting for the result from every previous steps to access the memory which occurs in many applicable algorithms. Based on combining the parallel and pipelined properties to eliminate unnecessary delays, we propose new visual pipeline architecture and use field programmable gate array to implement our hardware scheme. For verification, the multiscale Harris corner detector in cooperating with shape context and thin-plate splines were combined to complete our real-time experiment of the integrated hardware and software (H/S) system for pattern recognition.

A Portable Real-Time Lane Departure Warning System based on Embedded Calculating Technique

Lane departure warning systems are expected to improve safety on the roads. In the present paper, we proposed the design of a handheld real-time lane departure warning system. Such a device can be used on every road vehicle and dramatically improve safety. In order to attain our goal, we adopted the technology of embedded systems. In recent years, the enhancement of embedded calculating technology has allowed the performance of embedded systems to gain enough capability to carry out the huge calculations for lane departure warning algorithms. Finally, we implemented a real-time lane departure warning system on an image-processing platform that we also developed and which can be validated both in actual road tests and on a small-sized PCB board

Using a multiple storage quad tree on a hierarchical VLSI compaction scheme

A graph-generating algorithm and the experimental results of a hierarchical mask-layout-compaction scheme based on a plane-sweep algorithm, a fast region-query and a space-efficient data structure called the hierarchical multiple-storage quad tree are presented. For a mask-layout design, a rectangle is used as the primary element of the layout. Hence, in the hierarchical mask-compaction scheme, the graph-generating algorithm is based on the edges of rectangles rather than the central lines of symbols for the symbolic-compaction design. The plane-sweep algorithm is also called a dynamic event scheduling algorithm and can be applied to solve some other problems in the field of computational geometry and image processing. The efficiencies of the plane-sweep algorithm and the graph-generating algorithm are dependent on the region-query operations of the spatial data structure. By using the improved multiple storage quad tree as the spatial data structure in the system, the mask-layout compactor has been accomplished in a practically linear time performance in terms of the rectangles in the source layout. >

Region-Level Motion-Based Foreground Segmentation Under a Bayesian Network

This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effects, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest, within which the spatial relationships are represented by a region adjacency graph. Next, we consider the motion of the foreground objects and, hence, utilize the temporal coherence property in the regions detected. Thus, the foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result priorly obtained, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner by maximizing the conditional joint probability density function of these two elements. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

Unsupervised query-based learning of neural networks using selective-attention and self-regulation

Query-based learning (QBL) has been introduced for training a supervised network model with additional queried samples. Experiments demonstrated that the classification accuracy is further increased. Although QBL has been successfully applied to supervised neural networks, it is not suitable for unsupervised learning models without external supervisors. In this paper, an unsupervised QBL (UQBL) algorithm using selective-attention and self-regulation is proposed. Applying the selective-attention, we can ask the network to respond to its goal-directed behavior with self-focus. Since there is no supervisor to verify the self-focus, a compromise is then made to environment-focus with self-regulation. In this paper, we introduce UQBL1 and UQBL2 as two versions of UQBL; both of them can provide fast convergence. Our experiments indicate that the proposed methods are more insensitive to network initialization. They have better generalization performance and can be a significant reduction in their training size.