Performance evaluation of a feature-preserving filtering algorithm for removing additive random noise in digital images

We evaluate the performance of a feature-preserving filtering algorithm over a range of images corrupted by typical additive random noise against three common spatial filter algorithms: median, sigma and averaging. The concept of the new algorithm is based on a corrupted-pixel identification methodology over a variable subimage size. Rather than processing every pixel indiscriminately in a digital image, this corrupted-pixel identification algorithm interrogates the image in variable-sized subimage regions to determine which are the corrupted pixels and which are not. As a result, only the corrupted pixels are being filtered, whereas the uncorrupted pixels are untouched. Extensive evaluation of the algorithm over a large number of noisy images shows that the corrupted-pixel identification algorithm exhibits three major characteristics. First, its ability in removing additive random noise is better visually (subjective) and has the smallest mean-square errors (objective) in all cases compared with the median filter, averaging filter and sigma filter. Second, the effect of smoothing introduced by the new filter is minimal. In other words, most edge and line sharpness is preserved. Third, the corrupted-pixel identification algorithm is consistently faster than the median and sigma filters in all our test cases. © 1996 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Lane detection by orientation and length discrimination

This paper describes a novel lane detection algorithm for visual traffic surveillance applications under the auspice of intelligent transportation systems. Traditional lane detection methods for vehicle navigation typically use spatial masks to isolate instantaneous lane information from on-vehicle camera images. When surveillance is concerned, complete lane and multiple lane information is essential for tracking vehicles and monitoring lane change frequency from overhead cameras, where traditional methods become inadequate. The algorithm presented in this paper extracts complete multiple lane information by utilizing prominent orientation and length features of lane markings and curb structures to discriminate against other minor features. Essentially, edges are first extracted from the background of a traffic sequence, then thinned and approximated by straight lines. From the resulting set of straight lines, orientation and length discriminations are carried out three-dimensionally with the aid of two-dimensional (2-D) to three-dimensional (3-D) coordinate transformation and K-means clustering. By doing so, edges with strong orientation and length affinity are retained and clustered, while short and isolated edges are eliminated. Overall, the merits of this algorithm are as follows. First, it works well under practical visual surveillance conditions. Second, using K-means for clustering offers a robust approach. Third, the algorithm is efficient as it only requires one image frame to determine the road center lines. Fourth, it computes multiple lane information simultaneously. Fifth, the center lines determined are accurate enough for the intended application.published_or_final_versio

New feature-preserving filter algorithm based on a priori knowledge of pixel types

The concept and algorithmic details of a new corrupted-pixel-identification- (CPI)-based estimation filter are presented. The approach is by transforming a noisy subimage centered on a corrupted pixel into its discrete cosine transform (DCT) domain, and approximating the transformed subimage by its DC (average) coefficient only, an estimation of the noise distribution is made by combining the knowledge of the number of corrupted pixels in the subimage and the pixel intensity of the noise term. This enables the DC coefficient of the restored image in the DCT domain to be determined, and from this, the restored pixel intensity can be calculated by an inverse DCT. The whole restored image can be obtained after all the corrupted pixels are exhausted. From an extensive performance evaluation, it was found that the new algorithm has a number of desirable characteristics. First, the CPI-based estimation algorithm performs extremely well when heavily degraded images are concerned. Second, the CPI-based estimation algorithm has acceptable feature-preserving properties, far better than the conventional median filter. Third, the new algorithm can be applied iteratively to the same noisy image. Fourth, the computing speed of the CPI-based estimation algorithm is almost three times faster than the conventional median filter, and 1.6 times faster than the original CPI algorithm, making it the fastest algorithm in this class so far. ©1996 Society of Photo – Optical Instrumentation Engineers.published_or_final_versio

A Fast And Accurate Scoreboard Algorithm For estimating Stationary Backgrounds In An Image Sequence

This paper presents a stationary background estimation algorithm for color image sequence. The algorithm employs the running mode and running average algorithms, which are two commonly used algorithms, as the estimation core. A scoreboard is used to kept the pixel variations in the image sequence and is used to select between the running mode or the running average algorithm in each estimation. Our evaluation results show that by selecting, intelligently, the estimation core between the two algorithms according to the scoreboard values, the proposed background estimation algorithm has excellent performance in terms of estimation accuracy and speed.published_or_final_versio

Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation

This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that first, a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom (PTZ) actions without needing further human interaction. Second, the size of the virtual loops is much smaller for estimation accuracy. This enables the use of standard block-based motion estimation techniques that are well developed for video coding. Third, the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors (ILDs), there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy. Second, it may also be varied for an effective utilization of the computing resources. Third, there is no failure rate associated with the virtual loops or physical installation. As the loops are defined on the image sequence, changing the detection configuration or redeploying the loops to other locations on the same image sequence requires only a change of the assignment parameters. Fourth, virtual loops may be reallocated anywhere on the frame, giving flexibility in detecting different parameters. Our simulation results indicate that the proposed method is effective in type classification.published_or_final_versio

An effective video analysis method for detecting red light runners

This paper presents a novel method for automatic red light runner detection on a video, which is fundamentally different from the concept of conventional red light camera systems. In principle, it extracts the state of the traffic lights and vehicle motions without any physical or electronic interconnections to the traffic light control system or the buried loop detectors. Purely from the video, the new method first constructs a traffic light sequence and then it estimates vehicle motions beyond the stop line while the light is red. In the former, the spatial and temporal relationships of individual traffic lights are utilized. In the latter, the concept of virtual loop detector has been introduced to emulate the physical loop detectors. A prototype was implemented based on this method and was tested in a number of field trials. The results show that the new method is able to detect multiple red light runners in multiple lanes. It is also capable of tolerating a number of hostile but realistic situation such as: 1) minimum number of traffic light; 2) pseudomotions due to shadows; 3) poor contrast; 4) pedestrian motions; and 5) turning vehicles.published_or_final_versio

A character segmentation algorithm for off-line handwritten script recognition

In this paper, a new character segmentation algorithm for dealing with off-line handwritten script recognition is presented. The X-axis projection, Y-axis projection and geometric classes techniques used by the algorithm proves to be successful in segmenting normal handwriting with a success rate of 93.5%. As a result of this development, detailed understanding of geometric classes of English characters and the difficult cases in segmentation was gained. Although the algorithm works quite well with a randomly chosen sample, results of a detailed analysis may shed new light into the tuning of the algorithm especially for segmenting the identified difficult cases.published_or_final_versio

Recognition of vehicle registration mark on moving vehicles in an outdoor environment

In this paper, we propose a new method for segmenting vehicle registration plate and recognizing the registration mark on a moving vehicle in an outdoor environment. The algorithm first segments the plate from the vehicle and other complex objects in view, based on the plate color and its dimension. The segmented plate is then corrected in orientation and size, before being matched with a sub-set of templates in the database. The matching adopts a high tolerant scheme allowing characters to have certain degree of shifting, rotation and mis-match. This proves to be an important criterion for successful recognition. Preliminary tests show that this method offers high success rate and high confident level.published_or_final_versio

Vehicle type classification from visual-based dimension estimation

This paper presents a visual-based dimension estimation method for vehicle type classification. Our method extracts moving vehicles from traffic image sequences and fits them with a simple deformable vehicle model. Using a set of coordination mapping functions derived from a calibrated camera model and relying on a shadow removal method, vehicle's width, length and height are estimated. Our experimental tests show that the modeling method is effective and the estimation accuracy is sufficient for general vehicle type classification.published_or_final_versio

Vehicle-type identification through automated virtual loop assignment and block-based direction biased motion estimation

This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for estimation accuracy; and the number of virtual loops per lane is large. Comparing this with traditional ILD, there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy and fully utilize computing resources. Second, there is no failure rate associated with the virtual loops and installation and maintenance cost can be kept to a minimum. Third, virtual loops may be re-allocated anywhere on the frame, giving flexibility in detecting different parameters.published_or_final_versio