Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions

Contiene tablas y fórmulasGabor schemes of multiscale image representation are useful in many computer vision applications. However, the classic Gabor expansion is computationally expensive due to the lack of orthogonality of Gabor functions. Some alternative schemes, based on the application of a bank of Gabor filters, have important advantages such as computational efficiency and robustness, at the cost of redundancy and lack of completeness. In a previous work we proposed a quasicomplete Gabor transform, suitable for fast implementations in either space or frequency domains. Reconstruction was achieved by simply adding together the even Gabor channels. In this work, we develop an optimized spatial-domain implementation, using one-dimensional, 11-tap filter masks, that is faster and more flexible than Fourier implementations. The reconstruction method is improved by applying fixed and independent weights to the Gabor channels before adding them. Finally, we analyze and implement, in the spatial domain, two ways to incorporate a high-pass residual, which permits a visually complete representation of the image.Peer reviewe

An approach for SLAR images denoising based on removing regions with low visual quality for oil spill detection

This paper presents an approach to remove SLAR (Side-Looking Airborne Radar) image regions with low visual quality to be used for an automatic detection of oil slicks on a board system. This approach is focused on the detection and labelling of SLAR image regions caused by a poor acquisition from two antennas located on both sides of an aircraft. Thereby, the method distinguishes ineligible regions which are not suitable to be used on the steps of an automatic detection process of oil slicks because they have a high probability of causing false positive results in the detection process. To do this, the method uses a hybrid approach based on edge-based segmentation aided by Gabor filters for texture detection combined with a search algorithm of significant grey-level changes for fitting the boundary lines in each of all the bad regions. Afterwards, a statistical analysis is done to label the set of pixels which should be used for recognition of oil slicks. The results show a successful detection of the ineligible regions and consequently how the image is partitioned in sub-regions of interest in terms of detecting the oil slicks, improving the accuracy and reliability of the oil slick detection.This work was supported by the project (RTC-2014-1863-8) of call for collaboration challenges MINECO

Early Detection of Ovarian Cancer Using Gabor Wavelet Phase Quantization and Binary Coding

Ovarian cancer is the 5th most common cancer in women, but it is the most difficult to detect in its early stages. Early detection and treatment of ovarian cancer has been shown to increase the five year survival rate of a woman from 12% if caught in stage four of the disease up to 92% if caught in stage one of the disease. Using signal processing, pattern classification and a learning algorithm, it is possible to identify patterns in high dimensionality mass spectrometry data that distinguishes between cancer and non-cancer ovarian samples. For our research, proteomic spectra were generated using SELDI-TOF mass spectrum data, which was composed of 162 ovarian cancer and 91 non-ovarian cancer samples. We introduce a Gabor filter on the mass spectrometry data and design a binary coding scheme for phase quantization encoding that is used for the pattern classification. This pattern will expose crucial features in the data that can be used to correctly classify unmasked samples for the presence or absence of ovarian cancer. Our proposed algorithm was able to successfully discriminate ovarian cancer and non-ovarian samples that yielded results with sensitivities, specificities and accuracies in the 90% to 100% range

Efficient Spatial-Domain Implementation Of A Multiscale Image Representation Based On Gabor Functions

Gabor schemes of multiscale image representation are useful in many computer vision applications. However, the classic Gabor expansion is computationally expensive due to the lack of orthogonality of Gabor functions. Some alternative schemes, based on the application of a bank of Gabor filters, have important advantages such as computational efficiency and robustness, at the cost of redundancy and lack of completeness. In a previous work we proposed a quasicomplete Gabor transform, suitable for fast implementations in either space or frequency domains. Reconstruction was achieved by simply adding together the even Gabor channels. In this work, we develop an optimized spatial-domain implementation, using one-dimensional, 11-tap filter masks, that is faster and more flexible than Fourier implementations. The reconstruction method is improved by applying fixed and independent weights to the Gabor channels before adding them. Finally, we analyze and implement, in the spatial domain, two wa..

Overcomplete Image Representations for Texture Analysis

Advisor/s: Dr. Boris Escalante-Ramírez and Dr. Gabriel Cristóbal. Date and location of PhD thesis defense: 23th October 2013, Universidad Nacional Autónoma de México.In recent years, computer vision has played an important role in many scientific and technological areas mainlybecause modern society highlights vision over other senses. At the same time, application requirements and complexity have also increased so that in many cases the optimal solution depends on the intrinsic charac-teristics of the problem; therefore, it is difficult to propose a universal image model. In parallel, advances in understanding the human visual system have allowed to propose sophisticated models that incorporate simple phenomena which occur in early stages of the visual system. This dissertation aims to investigate characteristicsof vision such as over-representation and orientation of receptive fields in order to propose bio-inspired image models for texture analysis