Fingerprint Enhancement Algorithm Based-on Gradient Magnitude for the Estimation of Orientation Fields

An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of fingerprint images improves the ridge-valley structure and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve ridge orientation textures using gradient magnitude. That algorithm has four steps; firstly, normalization of fingerprint image, secondly, foreground extraction, thirdly, noise areas identification and marking using gradient coherence and finally, enhancement of grey level. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods

A PCA-based automated finder for galaxy-scale strong lenses

We present an algorithm using Principal Component Analysis (PCA) to subtract galaxies from imaging data, and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimized to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centered on the lens galaxy center. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity and elongation. We apply the two methods to a sample of simulated Einstein rings, as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinates transform allows us to reach a completeness of 90% and a purity of 86%, as soon as the signal-to-noise integrated in the ring is higher than 30, and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes

Hardware Acceleration in Image Stitching: GPU vs FPGA

Image stitching is a process where two or more images with an overlapping field of view are combined. This process is commonly used to increase the field of view or image quality of a system. While this process is not particularly difficult for modern personal computers, hardware acceleration is often required to achieve real-time performance in low-power image stitching solutions. In this thesis, two separate hardware accelerated image stitching solutions are developed and compared. One solution is accelerated using a Xilinx Zynq UltraScale+ ZU3EG FPGA and the other solution is accelerated using an Nvidia RTX 2070 Super GPU. The image stitching solutions implemented in this paper increase the system’s field of view and involve the end-to-end process of feature detection, image registration, and image mixing. The latency, resource utilization, and power consumption for the accelerated portions of each system are compared and each systems tradeoffs and use cases are considered

Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

Lens modeling is the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known abundance of lenses, we find that a lens sample may be generated that is ~100% pure, but only ~20% complete. This shortfall is due primarily to the over-simplicity of the lens model. With a more optimistic robot, ~90% completeness can be achieved while rejecting ~90% of the candidate objects. The remaining candidates must be classified by human inspectors. We are able to classify lens candidates by eye at a rate of a few seconds per system, suggesting that a future 1000 square degree imaging survey containing 10^7 BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched in a modest amount of time. [Abridged]Comment: 17 pages, 11 figures, submitted to Ap

A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table

Framework for extracting and solving combination puzzles

Selles töös uuritakse, kuidas arvuti nägemisega seotud algoritme on võimalik rakendada objektide tuvastuse probleemile. Täpsemalt, kas arvuti nägemist on võimalik kasutada päris maailma kombinatoorsete probleemide lahendamiseks. Idee kasutada arvuti rakendust probleemide lahendamiseks, tulenes tähelepanekust, et probleemide lahenduse protsessid on kõik enamasti algoritmid. Sellest võib järeldada, et arvutid sobivad algoritmiliste probleemide lahendamiseks paremini kui inimesed, kellel võib sama ülesande peale kuluda kordades kauem. Siiski ei vaatle arvutid probleeme samamoodi nagu inimesed ehk nad ei saa probleeme analüüsida. Niisiis selle töö panuseks saab olema erinevate arvuti nägemise algoritmide uurimine, mille eesmärgiks on päris maailma kombinatoorsete probleemide tõlgendamine abstraktseteks struktuurideks, mida arvuti on võimeline mõistma ning lahendama.Praegu on antud valdkonnas vähe materiali, mis annab hea võimaluse panustada sellesse valdkonda. Seda saavutatakse läbi empiirilise uurimise testide kogumiku kujul selleks, et veenduda millised lähenemised on kõige paremad. Nende eesmärkide saavutamiseks töötati läbi suur hulk arvuti nägemisega seotud materjale ning teooriat. Lisaks võeti ka arvesse reaalaja toimingute tähtsus, mida võib näha erinevate liikumisest struktuuri eraldavate algoritmide(SLAM, PTAM) õpingutest, mida hiljem edukalt kasutati navigatsiooni ja liitreaalsuse probleemide lahendamiseks. Siiski tuleb mainida, et neid algoritme ei kasutatud objektide omaduste tuvastamiseks.See töö uurib, kuidas saab erinevaid lähenemisi kasutada selleks, et aidata vähekogenud kasutajaid kombinatoorsete päris maailma probleemide lahendamisel. Lisaks tekib selle töö tulemusena võimalus tuvastada objektide liikumist (translatsioon, pöörlemine), mida saab kasutada koos virutaalse probleemi mudeliga, et parandada kasutaja kogemust.This thesis describes and investigates how computer vision algorithms and stereo vision algorithms may be applied to the problem of object detection. In particular, if computer vision can aid on puzzle solving. The idea to use computer application for puzzle solving came from the fact that all solution techniques are algorithms in the end. This fact leads to the conclusion that algorithms are well solved by machines, for instance, a machine requires milliseconds to compute the solution while a human can handle this in minutes or hours. Unfortunately, machines cannot see puzzles from human perspective thus cannot analyze them. Hence, the contribution of this thesis is to study different computer vision approaches from non-related solutions applied to the problem of translating the physical puzzle model into the abstract structure that can be understood and solved by a machine.Currently, there is a little written on this subject, therefore, there is a great chance to contribute. This is achieved through empirical research represented as a set of experiments in order to ensure which approaches are suitable. To accomplish these goals huge amount of computer vision theory has been studied. In addition, the relevance of real-time operations was taken into account. This was manifested through the Different real-time Structure from Motion algorithms (SLAM, PTAM) studies that were successfully applied for navigation or augmented reality problems; however, none of them for object characteristics extraction.This thesis examines how these different approaches can be applied to the given problem to help inexperienced users solve the combination puzzles. Moreover, it produces a side effect which is a possibility to track objects movement (rotation, translation) that can be used for manipulating a rendered game puzzle and increase interactivity and engagement of the user

Depth-based descriptor for matching keypoints in 3D scenes

Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of objects, automatic navigation and analysis of biomedical images. Successful implementation of higher level image analysis tasks, however, is conditioned by reliable detection of characteristic image local regions termed keypoints. A large number of keypoint detection algorithms has been proposed and verified. In this paper we discuss the most important keypoint detection algorithms. The main part of this work is devoted to description of a keypoint detection algorithm we propose that incorporates depth information computed from stereovision cameras or other depth sensing devices. It is shown that filtering out keypoints that are context dependent, e.g. located at boundaries of objects can improve the matching performance of the keypoints which is the basis for object recognition tasks. This improvement is shown quantitatively by comparing the proposed algorithm to the widely accepted SIFT keypoint detector algorithm. Our study is motivated by a development of a system aimed at aiding the visually impaired in space perception and object identification