https://www.cedar.buffalo.edu/~govind/enhancement_pr.pdf

Fingerprint enhancement using STFT analysis

A modified Gabor filter design method for fingerprint image enhancement

A survey on skeletonization algorithms and their applications

We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complements radar and sonar for situational awareness at sea and has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction, and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The key processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background, and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research, which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on Singapore Maritime Dataset.

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

In this paper, we introduce a new large-scale dataset of ships, called SeaShips, which is designed for training and evaluating ship object detection algorithms. The dataset currently consists of 31 455 images and covers six common ship types (ore carrier, bulk cargo carrier, general cargo ship, container ship, fishing boat, and passenger ship). All of the images are from about 10 080 real-world video segments, which are acquired by the monitoring cameras in a deployed coastline video surveillance system. They are carefully selected to mostly cover all possible imaging variations, for example, different scales, hull parts, illumination, viewpoints, backgrounds, and occlusions. All images are annotated with ship-type labels and high-precision bounding boxes. Based on the SeaShips dataset, we present the performance of three detectors as a baseline to do the following: 1) elementarily summarize the difficulties of the dataset for ship detection; 2) show detection results for researchers using the dataset; and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the SeaShips dataset would hopefully advance research and applications on ship detection.

/pdf/seaships-a-large-scale-precisely-annotated-dataset-for-ship-28caz3nk2c.pdf

SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we propose two methods for fingerprint image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method use a unique anisotropic filter for direct grayscale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of either efficiency or time required.

/pdf/fingerprint-image-enhancement-using-filtering-techniques-3obu4457jb.pdf

Fingerprint image enhancement using filtering techniques

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. Most of the minutiae detection methods are based on image binarization while some others extract the minutiae directly from gray-scale images. In this work we compare these two approaches and propose two different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method uses a unique anisotropic filter for direct gray-scale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency.

/pdf/fingerprint-image-enhancement-using-filtering-techniques-d3p1m86q4g.pdf

Improved structure-adaptive anisotropic filter

We present a region-of-interest-based segmentation (ROI-S) algorithm and apply it for automatic target detection. The proposed algorithm requires no templates or a priori knowledge of the targets. An automatic ROI extraction approach based on localized texture and statistical features is used to locate targets in an IR scene without any prior knowledge of their type, exact size, and orientation. Two locally adaptive histogram-based segmentation techniques are applied to extract the target signature. The Bayes decision rule is applied for a bimodal histogram while entropic correlation is used for all other cases. Geometric and statistical features are automatically extracted for each suspected ROI. We suggest a unique variance-based metric for discriminating targets from clutter and for evaluating the probability of correct detection. The proposed system is successfully tested on several hundred single-frame IR images that contain multiple examples of military vehicles, with various sizes and brightness levels and in various background scenes and orientations. A high probability of correct detection (greater than 90%) with a low false alarm rate is achieved.

Region-of-interest-based algorithm for automatic target detection in infrared images

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Shlomo Greenberg

Papers

Fingerprint image enhancement using filtering techniques

Fingerprint image enhancement using filtering techniques

Improved structure-adaptive anisotropic filter

Region-of-interest-based algorithm for automatic target detection in infrared images

Neural-network classifiers for automatic real-world aerial image recognition.